Question: Is it possible for Google to predict the market trends for mobile games in Iran?

This paper presents a sophisticated model for understanding and predicting customer conversion behavior—transforming visits into purchases—at e-commerce sites. The model is designed to estimate the probability of purchase for each individual customer based on their historical patterns of visits and purchases. It features an individual-level probability model that accounts for various types of customer heterogeneity in a flexible and nuanced way. The conversion behavior of each customer is dissected into two primary components: the accumulation of visit effects and the impact of purchasing threshold effects. Both these components are allowed to fluctuate across different customers and over time.

Visit effects encapsulate the idea that store visits serve diverse roles in the purchasing journey. Some visits are driven by the intent to make a planned purchase, while others are more exploratory, akin to window shopping. The proposed model adeptly handles these and other visit-purchase dynamics in a coherent and efficient manner. The purchasing threshold component, on the other hand, reflects the psychological barriers or facilitators to online purchasing that may evolve as a customer gains more familiarity with the online purchasing process at a specific website.

The paper evaluates various iterations of the model, differing in the complexity of these two critical components, and contrasts the proposed framework with established methods like logistic regression. The findings reveal that the model not only boasts superior statistical properties but also excels in a holdout validation sample. Additionally, it offers valuable managerial insights into the underlying patterns of online buyer behavior, making it a powerful tool for e-commerce businesses seeking to optimize their conversion rates.

**Question**: How can we apply machine learning to predict the outcomes of chemical reactions?

**Documents**: Machine Learning in Chemistry: Predicting Reaction Outcomes
The use of machine learning techniques in chemistry has gained significant traction in recent years, particularly in predicting the outcomes of chemical reactions. Traditional methods for predicting reaction outcomes are often based on empirical rules and the expertise of chemists, which can be time-consuming and subject to error. Machine learning, however, offers a data-driven approach that can learn from large datasets of chemical reactions, enabling more accurate and efficient predictions. This paper reviews the current state of machine learning applications in chemical reaction prediction, highlighting the various types of algorithms used, including support vector machines, neural networks, and random forests. It also discusses the challenges and limitations of these methods, such as the need for high-quality data and the interpretability of the models. The paper concludes with a discussion of future directions for research in this field, emphasizing the potential for machine learning to revolutionize the way we approach chemical synthesis and reaction optimization.

**Similar Documents**:

We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists. © 2006 Published by Elsevier Ltd. PACS: 02.50.Ey; 05.40.-a; 05.50.+q; 89.65.-s

School of Business Administration, Southwestern University of Finance and Economics, Sichuan, P. R. of China; Business School, Shantou University, Guangdong, P. R. of China; Information Technology Management, School of Business, University at Albany, Albany, NY, U.S.A; Department of Management, Hong Kong University of Science and Technology, Hong Kong, P.R. China; Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Hong Kong, P.R. China

Smartphones and "app" markets are raising concerns about how third-party applications may misuse or improperly handle users' privacy-sensitive data. Fortunately, unlike in the PC world, we have a unique opportunity to improve the security of mobile applications thanks to the centralized nature of app distribution through popular app markets. Thorough validation of apps applied as part of the app market admission process has the potential to significantly enhance mobile device security. In this paper, we propose AppInspector, an automated security validation system that analyzes apps and generates reports of potential security and privacy violations. We describe our vision for making smartphone apps more secure through automated validation and outline key challenges such as detecting and analyzing security and privacy violations, ensuring thorough test coverage, and scaling to large numbers of apps.

This paper presents an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. It is based on the analysis of over 4 million messages from nearly 100 users that we collected in order to understand people’s use of the network. We believe that this is the first in-depth study of the properties of WhatsApp messages with an emphasis of noting differences across different age and gender demographic groups. It is also the first to use statistical and data analytic tools in this analysis. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. These differences facilitate the development of user prediction models based on data mining tools. We illustrate this by developing several prediction models such as for a person’s gender or approximate age. We also noted differences in users’ group behavior. We created group behaviorial models including the likelihood a given group would have more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Hedonic information technologies do not focus on productivity-oriented applications, but rather, on lifestyle-augmentation and entertainment. As such users of hedonic IT are provided with benefits related to general enjoyment as opposed to gains in organizational efficiency. The main purpose of this study is to investigate this burgeoning phenomenon by measuring several value drivers for downloading mobile phone ringtones. Hypothesis testing was performed with PLS techniques applied to data collected from 119 ringtone users. Results confirmed that the overall value of hedonic IT is a third-order construct, which successfully predicted behavioral usage intentions and positive word of mouth.

Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

The escalation and convergence of distributed networks and wireless telecommunications has created a tremendous potential platform for providing business services. In consumer markets, mobile marketing is expected to be a key growth area. The immediacy, interactivity and mobility of wireless devices provide a novel platform for marketing. The personal and ubiquitous nature of devices means that interactivity can be provided anytime and anywhere. However, as experience has shown, it is important to keep the consumer in mind. Mobile marketing permission and acceptance are core issues that marketers have yet to fully explain or resolve. This paper provides direction in this area. After briefly discussing some background on mobile marketing, the paper conceptualises key characteristics for mobile marketing permission and acceptance. The paper concludes with predictions on the future of mobile marketing and some core areas of further research.

BACKGROUND
Given the importance of regular physical activity, it is crucial to evaluate the factors favoring participation in physical activity. We aimed to report the psychometric analysis of the Farsi version of the Physical Activity and Leisure Motivation Scale (PALMS).

METHODS
The Farsi version of PALMS was completed by 406 healthy adult individuals to test its factor structure and concurrent validity and reliability.

RESULTS
Conducting the exploratory factor analysis revealed nine factors that accounted for 64.6% of the variances. The PALMS reliability was supported with a high internal consistency of 0.91 and a high test-retest reliability of 0.97 (95% CI: 0.97-0.98). The association between the PALMS and its previous version Recreational Exercise Motivation Measure scores was strongly significant (r= 0.86, P < 0.001).

CONCLUSION
We have shown that the Farsi version of the PALMS appears to be a valuable instrument to measure motivation for physical activity and leisure.

In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the “usage count” data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like “fmri”, “eeg”, “erp”, etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the “heat” of hot topics timely and dynamically.


Question: Is it possible for Google to predict the market direction of mobile games in Iran?

This paper introduces MapReduce, a programming framework designed for efficient and simplified data processing on extensive clusters. MapReduce streamlines the task of processing and generating large datasets, accommodating a wide range of practical applications. Users define their computation through a map and reduce function, while the underlying MapReduce runtime system autonomously distributes the computation across numerous machines in a cluster, manages failures, and optimizes inter-machine communication for efficient network and disk utilization. The ease of use for programmers is a key feature of the system, as evidenced by the development of over ten thousand unique MapReduce programs at Google over the past four years. Currently, an average of one hundred thousand MapReduce tasks are executed daily on Google's clusters, processing in excess of twenty petabytes of data each day, highlighting the system's scalability and performance capabilities. Despite its robustness, MapReduce is not directly equipped to predict market trends in specific sectors such as Iranian mobile games, as its primary function is data processing rather than market analysis or forecasting. However, when combined with appropriate algorithms and models, MapReduce can process the vast amounts of data necessary for such analyses, potentially enabling insights into market trends.

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.

In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of social media text and stock prices of a company. The tweets of several company has been extracted and performed sentiment classification using Naïve Bayes classifier and SVM classifier. To perform the classification, N-gram based feature vectors are constructed using important words of tweets. Further, the pattern of association between number of tweets which are positive or negative and stock prices has been explored. Motivated by such an association, the features related to tweets such as number of positive, negative, neutral tweets and total number of tweets are used to predict the stock market status using Support Vector Machine classifier.

*This work is supported by a grant from National Science Foundation (IIS-1111092) and a strategic research grant from City University of Hong Kong (7002770) *The work was done when the first author was visiting University of Illinois at Chicago. Abstract • The Web has seen a tremendous rise in social media. • People’s experiences and opinions expressed in Social Media (e.g., Twitter, Facebook) contain rich social signals (e.g., about consumer sentiment, stock market, economic conditions, etc.). • The goal of this paper is to exploit public sentiment for stock market prediction. • Topic based sentiment is an important factor in stock prediction because it reflects public opinions about current issues/topics. • We employ a Bayesian non-parametric topic-based sentiment time series approach learned from streaming tweets to predict the S&P100 Stock Index.

Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of the existing e-marketplaces are performed to improve the end-users experience through a Mobile App. The main goal of this study is to find a solution that is capable of integrating multiple heterogeneous hidden data sources and unify the received responses into one single, structured and homogeneous source. Furthermore, the user can easily choose the desired product or reformulate the query through the interface. The proposed Android Mobile App is based on the multi-level conceptual analysis and modeling discipline, in which, data are analyzed in a way that helps in discovering the main concepts of any unknown domain captured from the hidden web. These concepts discovered through information extraction are then structured into a tree-based interface for easy navigation and query reformulation. The application has been evaluated through substantial experiments and compared to other existing mobile applications. The results showed that analyzing the query results and re-structuring the output before displaying to the end-user in a conceptual multilevel mechanism are reasonably effective in terms of number of clicks, time taken and number of navigation screens. Based on the proposed intelligent application, the interface is minimized to only two navigation screens, and the time needed to browse products from multiple marketplaces is kept reasonable in order to reach the target product.

Merchants have developed apps for the smartphone that assist consumers through the buying process, from when they gather information to when they decide to purchase. Sharing information, such as location, shopping preferences and financial data, can enhance consumers’ experience. However, they may be reluctant to disclose these details unless they perceive that the benefits gained are more than the risk of privacy loss. This privacy calculus is added to the unified theory of acceptance and use of technology (UTAUT2) in order to explain consumers’ willingness to exchange the disclosure of personal information for additional value. Sharing information makes mobile commerce more convenient by saving time and effort. Companies are able to send offers that are tailored to a specific customer. Payments are processed faster because the merchant already has the financial data on hand. UTAUT2 is further extended with the Theory of Convenience. Results from a survey of over 300 consumers show that perceived value and perceived convenience are influencing variables and that perceived value mediates the influence of perceived convenience on intention to use.

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

Accurately predicting the popularity of user generated content (UGC) is very valuable to content providers, online advertisers, as well as social media and social network researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. We here focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible, as a step towards building more accurate popularity prediction methods. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Moreover, given the heterogeneity in popularity dynamics across objects in most UGC applications, this tradeoff has to be solved on a per-object basis, which makes the prediction task harder. We propose to tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. The first step exploits a time series clustering algorithm to represent each trend by a time series centroid. We propose to treat the second step as a classification problem. First, we extract a set of features of the target object corresponding to the distances of its early popularity curve to the previously identified centroids. We then combine these features with content features (e.g., incoming links, category), using them to train classifiers for prediction. Our experimental results for YouTube datasets show that we can achieve Micro and Macro F1 scores between 0.61 and 0.71 (a gain of up to 38% when compared to alternative approaches), with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. We also show that our approach can be applied to produce predictions of content popularity at a future date that are much more accurate than recently proposed regression-based and state-space based models, with accuracy improvements of at least 33% and 59%, on average.

In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.

Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.


Question: Is it possible for Google to predict the market trends of mobile games in Iran?

This paper presents Freebase, a collaborative graph database designed to organize and store human knowledge in a structured format. Freebase serves as a robust and scalable tuple database that is collectively created, maintained, and structured by its users. As of now, Freebase houses over 125,000,000 tuples, encompassing more than 4000 types and exceeding 7000 properties. To ensure accessibility and foster collaboration, Freebase offers public read/write access through an HTTP-based graph-query API, utilizing the Metaweb Query Language (MQL). MQL acts as a user-friendly, object-oriented interface for querying and manipulating the tuple data within Freebase, making it an ideal choice for the development of collaborative, web-based applications that focus on data. By enabling easy access and manipulation of structured human knowledge, Freebase facilitates the creation of sophisticated applications that leverage collective intelligence and the vast pool of information available.

Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.

Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.

Social media are playing an increasingly important role as information sources for travelers. The goal of this study is to investigate the extent to which social media appear in search engine results in the context of travel-related searches. The study employed a research design that simulates a traveler’s use of a search engine for travel planning by using a set of pre-defined keywords in combination with nine U.S. tourist destination names. The analysis of the search results showed that social media constitute a substantial part of the search results, indicating that search engines likely direct travelers to social media sites. This study confirms the growing importance of social media in the online tourism domain. It also provides evidence for challenges faced by traditional providers of travel-related information. Implications for tourism marketers in terms of online marketing strategies are discussed. 2009 Elsevier Ltd. All rights reserved.

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics

Online transportation, such as GoJek, is preferred by many users especially in areas where public transport is difficult to access or when there is a traffic jam. Twitter is a popular social networking site in Indonesia that can generate information from users' tweets. In this study, we proposed a system that detect public sentiments based on Twitter post about online transportation services especially GoJek. The system will collect tweets, analyze the tweets sentiments using SVM, and group them into positive and negative sentiment.

Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng Google Research∗ Abstract

Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor’s perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors’ marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers’ continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice. 2015 Elsevier Ltd. All rights reserved.

This study examined the effects of a computer game on students' mathematics achievement and motivation, and the role of prior mathematics knowledge, computer skill, and English language skill on their achievement and motivation as they played the game. A total of 193 students and 10 teachers participated in this study. The teachers were randomly assigned to experimental and control groups. A mixed method of quantitative and interviews were used with Multivariate Analysis of Co-Variance to analyze the data. The results indicated significant improvement of the achievement of the experimental versus control group. No significant improvement was found in the motivation of the groups. Students who played the games in their classrooms and school labs reported greater motivation compared to the ones who played the games only in the school labs. Prior knowledge, computer and English language skill did not play significant roles in achievement and motivation of the experimental group. 2010 Elsevier Ltd. All rights reserved.


Question: Is it possible for Google to predict the market trends of mobile games in Iran?

This paper presents a sophisticated model for analyzing evolving visit patterns in clickstream data, offering deeper insights into individual-level site-visit dynamics in the dynamic landscape of the Internet. Online platforms, be they retailers or content distributors, frequently track visitor traffic as a key performance indicator. Yet, basic metrics such as monthly visit counts often fail to reveal the nuances of individual user behavior, particularly in a rapidly changing digital environment. To address this gap, the authors develop a model that not only accounts for cross-sectional variability in site-visit behavior but also tracks changes over time as users become more familiar with the site. Moreover, the study explores the correlation between visiting frequency and purchasing likelihood at an e-commerce platform. The findings support the hypothesis that frequent visitors to a retail site exhibit a higher propensity to make purchases. Additionally, the research highlights that shifts in an individual's visit frequency over time can indicate which customer segments are more likely to convert into buyers. This study is situated at the intersection of several disciplines, including business, business analytics, e-commerce, management sciences and quantitative methods, marketing, and technology innovation. Accessible at ScholarlyCommons, the full technical report offers a comprehensive analysis of evolving visit behavior in clickstream data. Wendy W. Moe and Peter S. Fader, both from the Marketing Department at the Wharton School, University of Pennsylvania, are the principal contributors to this work. For further inquiries, Wendy can be reached at wendy@marketing.wharton.upenn.edu.

There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.

Accurately predicting the popularity of user generated content (UGC) is very valuable to content providers, online advertisers, as well as social media and social network researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. We here focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible, as a step towards building more accurate popularity prediction methods. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Moreover, given the heterogeneity in popularity dynamics across objects in most UGC applications, this tradeoff has to be solved on a per-object basis, which makes the prediction task harder. We propose to tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. The first step exploits a time series clustering algorithm to represent each trend by a time series centroid. We propose to treat the second step as a classification problem. First, we extract a set of features of the target object corresponding to the distances of its early popularity curve to the previously identified centroids. We then combine these features with content features (e.g., incoming links, category), using them to train classifiers for prediction. Our experimental results for YouTube datasets show that we can achieve Micro and Macro F1 scores between 0.61 and 0.71 (a gain of up to 38% when compared to alternative approaches), with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. We also show that our approach can be applied to produce predictions of content popularity at a future date that are much more accurate than recently proposed regression-based and state-space based models, with accuracy improvements of at least 33% and 59%, on average.

Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help forecast topic trends in the future. This can be beneficial for many applications, such as modeling the evolution of the direction of research and forecasting future trends of the IT industry. In this paper, we propose using association analysis and ensemble forecasting to automatically discover topics from a set of text documents and forecast their evolving trend in a near future. In order to discover meaningful topics, we collect publications from a particular research area, data mining and machine learning, as our data domain. An association analysis process is applied to the collected data to first identify a set of topics, followed by a temporal correlation analysis to help discover correlations between topics, and identify a network of topics and communities. After that, an ensemble forecasting approach is proposed to predict the popularity of research topics in the future. Our experiments and validations on data with 9 years of publication records validate the effectiveness of the proposed design.

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.

While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviours in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different datasets and features.

Companies have increasingly advocated social media technologies and platforms to improve and ultimately transform business performance. This study examines whether social media has a predictive relationship with firm equity value, which metric of social media has the strongest relationship, and the dynamics of the relationship. The results derived from the vector autoregressive models suggest that social media-based metrics (blogs and reviews) are significant leading indicators of firm stock market performance. We also find a faster predictive value of social media, i.e., a shorter “wear-in” effect compared with conventional online behavior metrics, such as web traffic and search. Interestingly, conventional digital user metrics (Google search and web traffic), which have been widely adopted to measure online consumer behavior, are found to have significant yet substantially weaker predictive relationships with firm equity value than social media. The results provide new insights for social media managers and firm equity valuations.

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

a r t i c l e i n f o Understanding the antecedents and consequences of customer satisfaction in the mobile communications market is important. This study explores the effects of service quality and justice on customer satisfaction, which, in turn, affects continuance intention of mobile services. Service quality, justice and customer satisfaction were measured by multiple dimensions. A research model was developed based on this multidimen-sional approach and was empirically examined with data collected from about one thousand users of mobile value-added services in China. Results show that all three dimensions of service quality (interaction quality, environment quality and outcome quality) have significant and positive effects on cumulative satisfaction while only one dimension of service quality (interaction quality) has a significant and positive effect on transaction-specific satisfaction. Besides procedural justice, the other two dimensions of justice (distribu-tive justice and interactional justice) significantly influence both transaction-specific satisfaction and cumulative satisfaction. Furthermore, both types of customer satisfaction have significant and positive effects on continuance intention. Implications for research and practice are discussed. With the rapid advancements of mobile network technologies, provision of various kinds of value-added services by mobile service providers is on the rise around the world. As the market becomes more and more mature, value-added services become more homogeneous and the competition for acquiring new customers and retaining existing customers becomes more intense. In this environment, customer satisfaction is a critical factor for mobile service providers to maintain or improve their market share and profitability. Prior studies have found that customer satisfaction contributes to a firm's profitability and customer retention [33,35]. In a reorganization of the communications industry in China between 2008 and 2009, the original six mobile network operators were reduced to three. Meanwhile, the availability of third-generation telecommunications technologies suggested that more mobile value-added services would be provided to the customers. A recent value-added services survey report on mobile communications conducted by Analysys in 2010 predicted that, the competition among existing mobile network operators would become fierce after the reorganization of the industry and the introduction of third-generation services. Thus, for these mobile network operators, in order to retain customers, enhancing customer satisfaction is an urgent task to tackle with. Moreover, as new mobile value-added services are released, service providers need to focus on if these new services appeal to customers and on the willingness of customers to continue to use the services. Therefore, understanding the …

BACKGROUND
Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

OBJECTIVE
The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

METHODS
We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

RESULTS
The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

CONCLUSIONS
Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

Nowadays, striking growth of online social media has led to easier and faster spreading of rumors on cyber space, in addition to tradition ways. In this paper, rumor detection on Persian Twitter community has been addressed for the first time by exploring and analyzing the significances of two categories of rumor features: Structural and Content-based features. While applying both feature sets leads to precision more than 80%, precision around 70% using only structural features has been obtained. Moreover, we show how features of users tending to produce and spread rumors are effective in rumor detection process. Also, the experiments have led to learning of a language model of collected rumors. Finally, all features have been ranked and the most discriminative ones have been discussed.


Question: Signal processing methods to enhance angular resolution in homodyne FMCW radar systems.

This paper presents an innovative system design for a 77 GHz Frequency Modulated Continuous Wave (FMCW) automotive radar sensor, specifically focusing on achieving high-resolution direction of arrival (DOA) estimation. The paper delves into the critical aspects of system design that aim to balance cost-effectiveness with superresolution signal processing capabilities, which are essential for enhancing the performance of automotive safety systems. Key challenges in the design phase, particularly concerning the mitigation of amplitude and phase mismatches resulting from non-uniform antenna patterns, are addressed. These mismatches can significantly degrade the performance of the Multiple Signal Classification (MUSIC) DOA estimator, a critical component in radar signal processing. Simulation outcomes are provided to establish design guidelines for the requisite signal-to-noise ratio (SNR) and antenna configurations. Additionally, the paper reports on field tests conducted with a prototype system, which confirm the radar's exceptional DOA resolution and validate the design objectives, showcasing the practical applicability of the proposed design in real-world road traffic scenarios.

Marc Rioux National Research Council, Canada Institute for Information Technology Visual Information Technology Ottawa, Canada, K1A 0R6 Abstract. We review some usual laser range finding techniques for industrial applications. After outlining the basic principles of triangulation and time of flight [pulsed, phase-shift and frequency modulated continuous wave (FMCW)], we discuss their respective fundamental limitations. Selected examples of traditional and new applications are also briefly presented. © 2001 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.1330700]

The radar signatures of automotive targets were measured from 22 to 29 GHz and 76 to 81 GHz inside an anechoic chamber using a vector network analyzer. Radar cross section maps of a sedan, truck, van, pedestrian, motorcycle, and bicycle as a function of effective radar sensor bandwidth, center frequency, and polarization are presented.

In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

The International Telecommunications Union provides recommendations regarding spectral emission bounds for primary radar systems. These bounds are currently in review and are defined in terms of spectral occupancy, necessary bandwidth, 40dB bandwidth and out-of-band roll-off rates. Here we derive out-of-band domain spectral envelopes, bandwidth formula and roll-off rates, for various Linear FMCW radar waveforms including sawtooth (LFMCW), Quadratic Phase Coded LFMCW, LFM Pulse Train, and Hann amplitude tapered LFMCW.

This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.

This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.

Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.

Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.

The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

By using multi-pass SAR acquisitions, SAR tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Since the orbits of modern space-borne SAR systems, like TerraSAR-X, are tightly controlled, the elevation resolution (depending on the elevation aperture size) is at least an order of magnitude lower than in range and azimuth. Hence, superresolution algorithms are desired. The high anisotropic 3D resolution element renders the signals sparse in elevation. This property suggests using compressive sensing (CS) methods. The paper presents the theory of 4D (i.e. space-time) CS TomoSAR and compares it with classical tomographic methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented. In addition, the model based time warp method for differential tomographic non-linear motion monitoring is proposed and validated by reconstructing seasonal motion (caused by thermal expansion) of a building complex.


Question: Signal processing methods to enhance angular resolution in homodyne FMCW radar systems.

This article offers a concise perspective on automotive radar, emphasizing its regulatory landscape and technological advancements. It highlights the significance of frequency regulation for both short and long-range radar systems, which is crucial for automotive manufacturers and their sensor providers. The paper delves into the design and antenna techniques employed in 24 GHz and 77 GHz sensors, evaluating their effects on the sensor's field of view and angular measurement precision. A notable focus is given to digital beamforming methodologies, showcasing their potential through encouraging experimental outcomes. This discussion underscores the evolving trends and current status of automotive radar technology, particularly concerning its angular resolution performance and signal processing techniques.

**Question**: Impact of climate change on global food security

**Documents**: Climate change and food security: a framework document
The paper presents a framework for assessing the impact of climate change on food security. The framework comprises a set of indicators that can be used to assess the vulnerability of food systems to climate change, as well as the adaptive capacity of these systems. The framework is intended to help policymakers and researchers better understand the complex interactions between climate change and food security, and to identify priority areas for action.

**Similar Documents**: This document proposes a comprehensive framework to evaluate the repercussions of climate change on global food security. It outlines a series of indicators designed to measure the susceptibility of food systems to climate change impacts and the systems' ability to adapt. The primary goal of this framework is to facilitate a deeper understanding among policymakers and researchers about the intricate interplay between climate change and food security. It aims to pinpoint critical areas requiring immediate attention and intervention, thereby guiding strategic decisions and research efforts toward ensuring food security in a changing climate.

The airborne monitoring of scenes using unmanned aircrafts is becoming increasingly important. Several types of airborne sensors - in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the deployment scenarios, often also demand for the ability to look through dust clouds, smoke, and fog. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR). In this paper we focus on miniaturized SAR systems which were developed and optimized for utilization in a UA V (unmanned aerial vehicle) with a low loading capacity. This not only requires a compact and light radar sensor but the processing also has to cope with the unstable flight conditions of a small aircraft. Therefore, a high-precision inertial measurement unit (IMU) and motion compensating SAR-algorithms are needed. Thanks to the utilization of a high transmit frequency of either 35 GHz or 94 GHz, the sensors are suitable for the detection of small-scale objects. A very high resolution of 15 cm × 15 cm can be achieved when using modern FMCW (frequency modulated continuous wave) generation with a high bandwidth (up to 1 GHz) in combination with small antennas.

A complete radar transceiver for 77 GHz with two integrated antenna elements is presented. Based on a previously published design [1], two of the transmit and receive channels of the transceiver are supplemented with integrated antenna elements. The antennas exhibit a well defined antenna pattern with an efficiency of better than 50%.

The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

We have designed and tested a novel electronic tracking antenna array that is formed by 2 × 2 microstrip sub-arrays. Through time sequence phase weighting on each sub-array, the amplitude and phase on each sub-array can be recovered from the output of the resultant single channel. The amplitude and phase on each array can be used to produce the sum and difference radiation pattern by digital signal processing. In comparison with the monopulse system, the RF comparator is eliminated and the number of the receiver channels is reduced from 3 to 1. A proof-of-concept prototype was fabricated and tested. The measured results confirmed the validity and advantages of the proposed scheme. The procedure of channel correction is given.

This paper describes the design of a dual-channel S-band digital radar test bed. The test bed combines stretch processing with a novel and cost-effective hardware architecture that enables it to achieve an in-band dynamic range of 60 dB over 600 MHz of instantaneous bandwidth. The dual digital receiver channels allow for adaptive digital beamforming which can be used to mitigate a directional source of interference. Experimental test and verification results are presented to demonstrate system performance.

This paper proposed a design scheme of wide band monopulse antenna system for the passive radar seeker (PRS) of anti-radiation missile (ARM). To save the installation space of the PRS, the conical conformal log periodic antennas (LPDA) are employed to form the monopulse antenna system. The specific implementation scheme of PRS based on the conically conformal monopulse antennas was designed and analyzed. A practical monopulse antenna system composed of two log periodic antennas was designed and fabricated over the operating frequency range of 1GHz to 8GHz. A loaded resistor with impedance equal to 50Ω is used to reduce the return loss at low frequency. The experimental results demonstrate that acceptable wide band impedance matching and port isolation for each fabricated antenna was achieved. Furthermore, the wide band sum and difference radiation patterns were formed, which validates the proposed conformal monopulse antenna design scheme for the PRS application.

This paper addresses the analysis and mitigation of the signal distortion caused by oscillator phase noise (PN) in OFDM communications systems. Two new PN mitigation techniques are proposed, especially targeted for reducing the intercarrier interference (ICI) effects due to PN. The first proposed method is a fairly simple one, stemming from the idea of linearly interpolating between two consecutive common phase error (CPE) estimates to obtain a linearized estimate of the time-varying phase characteristics. The second technique, in turn, is an extension to the existing state-of-the-art ICI estimation methods. Here the idea is to use an additional interpolation stage to improve the phase estimation performance around the boundaries of two consecutive OFDM symbols. The paper also verifies the performance improvement of these new PN estimation techniques by comparing them to the existing state-of-the-art techniques using extensive computer simulations. To emphasize practicality, the simulations are carried out in 3GPP-LTE downlink -like system context, covering both additive white Gaussian noise (AWGN) and extended ITU-R Vehicular A multipath channel types.

We present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows us to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.

In this paper, we present an analytic, filtered backprojection (FBP) type inversion method for bistatic synthetic aperture radar (BISAR). We consider a BISAR system where a scene of interest is illuminated by electromagnetic waves that are transmitted, at known times, from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop the FBP-type reconstruction method. We analyze the computational complexity of the numerical implementation of the method and present numerical simulations to demonstrate its performance.

With the recent launches of the German TerraSAR-X and the Canadian RADARSAT-2, both equipped with phased array antennas and multiple receiver channels, synthetic aperture radar, ground moving target indication (SAR-GMTI) data are now routinely being acquired from space. Defence R&D Canada has been conducting SAR-GMTI trials to assess the performance and limitations of the RADARSAT-2 GMTI system. Several SAR-GMTI modes developed for RADARSAT-2 are described and preliminary test results of these modes are presented. Detailed equations of motion of a moving target for multiaperture spaceborne SAR geometry are derived and a moving target parameter estimation algorithm developed for RADARSAT-2 (called the Fractrum Estimator) is presented. Limitations of the simple dual-aperture SAR-GMTI mode are analysed as a function of the signal-to-noise ratio and target speed. Recently acquired RADARSAT-2 GMTI data are used to demonstrate the capability of different system modes and to validate the signal model and the algorithm.


Question: Signal processing methods to enhance angular resolution in homodyne FMCW radar systems.

A novel method for enhancing angular resolution in homodyne Frequency Modulated Continuous Wave (FMCW) radar systems using advanced signal processing techniques is presented in this paper. The approach focuses on improving the estimation of signal parameters and the location of multiple emitters in complex environments. By applying sophisticated algorithms, the radar's ability to distinguish between closely spaced targets is significantly enhanced, leading to improved performance in angular resolution. This is achieved through the development of a new signal processing framework that optimally combines multiple signal classifications and parameter estimation techniques. The method not only boosts the radar's angular resolution but also ensures robustness against various environmental factors that can degrade performance. Through comprehensive simulations and real-world testing, the effectiveness of the proposed technique in enhancing angular resolution for homodyne FMCW radar systems is demonstrated, showcasing its potential for applications in both military and civilian radar systems.

ISAR imaging is described for general motion of a radar target. ISAR imaging may be seen as a 3D to 2D projection, and the importance of the ISAR image projection plane is stated. For general motion, ISAR images are often smeared when using FFT processing. Time frequency methods are used to analyze such images, and to form sharp images. A given smeared image is shown to be the result of changes both in scale and in the projection plane orientation.

Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.

Elevation beamforming and Full Dimension MIMO (FD-MIMO) has been an active area of research and standardization in 3GPP LTE-Advanced. In an FD-MIMO system, a base station with 2-dimensional (2D) active array supports multi-user joint elevation and azimuth beamforming (a.k.a. 3D beamfoming), which results in much higher cell capacity compared to conventional systems. Recent study has shown that with these new FD-MIMO technologies, we can achieve promising 3-5× gain in both cell capacity as well as cell-edge throughput. In this paper, we will provide a brief summary of recent 3GPP activities, including the recently completed 3D channel model, ongoing study on FD-MIMO scenarios, antenna/RF (radio frequency) transceiver architectures, as well as potential network performance benefits. In addition, we also discuss some methods for reducing CSI (channel state information) feedback overhead and ensuring efficient operation of large size FD-MIMO for both TDD and FDD systems.

This paper describes a factorized geometrical autofocus (FGA) algorithm, specifically suitable for ultrawideband synthetic aperture radar. The strategy is integrated in a fast factorized back-projection chain and relies on varying track parameters step by step to obtain a sharp image; focus measures are provided by an object function (intensity correlation). The FGA algorithm has been successfully applied on synthetic and real (Coherent All RAdio BAnd System II) data sets, i.e., with false track parameters introduced prior to processing, to set up constrained problems involving one geometrical quantity. Resolution (3 dB in azimuth and slant range) and peak-to-sidelobe ratio measurements in FGA images are comparable with reference results (within a few percent and tenths of a decibel), demonstrating the capacity to compensate for residual space variant range cell migration. The FGA algorithm is finally also benchmarked (visually) against the phase gradient algorithm to emphasize the advantage of a geometrical autofocus approach.

For the first time, to the best of our knowledge, this paper reports a complete millimeter wave (mmWave) wireless system, which coherently utilizes polarization diversity, multi-input multioutput (MIMO), and beamforming technologies applicable for upcoming fifth generation wireless mobile terminals. Using an interdisciplinary approach across the entire layers constituting of antennas, RF architecture, and digital modem, the polarization of the presented mmWave beamforming antenna system dynamically adapts to the channel environments and MIMO transmission modes. Empirical investigations, which are taken under real-life environments, ascertain that implementing switchable dual-polarized array antennas can alleviate the complexity of the beam searching procedure and MIMO channelization. In addition, experimental results acquired from intensive system level tests indicate more than 22 dB averaged cross-polarization discrimination under polarized MIMO channel conditions at the 60 GHz band. The beam selection algorithm that can be easily implemented based on power metric is confirmed to feature a 99.5% accuracy in comparison with the optimal selection algorithm derived from the theoretical maximal capacity.

While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and (non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection algorithm may be successfully employed for many SAR research endeavors not involving considerably large data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image reconstruction using the matched filter and backprojection algorithms is provided.

The experimental test of novel ultrasound (US) investigation methods can be made difficult by the lack of flexibility of commercial US machines. In the best options, these only provide beamformed radiofrequency or demodulated echo-signals for acquisition by an external PC. More flexibility is achieved in high-level research platforms, but these are typically characterized by high cost and large size. This paper presents a powerful but portable US system, specifically developed for research purposes. The system design has been based on high-level commercial integrated circuits to obtain the maximum flexibility and wide data access with minimum of electronics. Preliminary applications involving nonstandard imaging transmit/receive strategies and simultaneous B-mode and multigate spectral Doppler mode are discussed.

There are lots of local positioning systems available for indoor applications, partly even for outdoor use. Many of them rely on a rather clean environment which does not interfere in their measurements too much. Industrial environments, in- or outdoors, often do not meet these requirements. The positioning applications here are surrounded by dirt, dust and vibrations. The number of local positioning systems being able to perform properly here at all times is limited to such not dependent on optical or sonic measurements. Local secondary radar systems proved to be a reliable source of distance and position measurements, even in harsh environments. This paper examines a few use cases and depicts the performance of local positioning radar systems there.

This paper proposed a design scheme of wide band monopulse antenna system for the passive radar seeker (PRS) of anti-radiation missile (ARM). To save the installation space of the PRS, the conical conformal log periodic antennas (LPDA) are employed to form the monopulse antenna system. The specific implementation scheme of PRS based on the conically conformal monopulse antennas was designed and analyzed. A practical monopulse antenna system composed of two log periodic antennas was designed and fabricated over the operating frequency range of 1GHz to 8GHz. A loaded resistor with impedance equal to 50Ω is used to reduce the return loss at low frequency. The experimental results demonstrate that acceptable wide band impedance matching and port isolation for each fabricated antenna was achieved. Furthermore, the wide band sum and difference radiation patterns were formed, which validates the proposed conformal monopulse antenna design scheme for the PRS application.

Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.


Question: Signal processing methods to enhance angular resolution in homodyne FMCW radar systems.

This document introduces a groundbreaking technique known as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) for achieving high-resolution signal parameter estimation, a critical requirement in diverse signal processing domains. These domains encompass direction-of-arrival (DOA) estimation, system identification, and time series analysis. The document outlines a fresh perspective on the overarching challenge of signal parameter estimation, albeit presented within the context of DOA estimation. The ESPRIT methodology is versatile and can be adapted to a wide spectrum of problems, including the precise detection and estimation of sinusoidal signals in noisy environments. Its efficacy is rooted in the exploitation of a rotational invariance characteristic inherent in signal subspaces, which is facilitated by a sensor array exhibiting translational invariance. When circumstances permit the application of ESPRIT, it showcases substantial improvements in performance and computational efficiency compared to established algorithms such as the Maximum Entropy Method (MEM), Capon's Maximum Likelihood Method (MLM), and the Multiple Signal Classification (MUSIC) technique.

A simple and cost-effective software-based resolver-to-digital converter using a digital signal processor is presented. The proposed method incorporates software generation of the resolver carrier using a digital filter for synchronous demodulation of the resolver outputs in such a way that there is a substantial savings on hardware like the costly carrier oscillator and associated digital and analog circuits for amplitude demodulators. In addition, because the method does not cause any time delay, the dynamics of the servo control using the scheme are not affected. Furthermore, the method enables the determination of the angle for a complete 360deg shaft rotation with reasonable accuracy using a lookup table that contains entries of only up to 45deg. Computer simulations and experimental results demonstrate the effectiveness and applicability of the proposed scheme.

Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.

Several aspects of range and azimuth (time delay and Doppler) ambiguities in spaceborne synthetic aperture radars (SARs) are examined. An accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is described. This method has been applied to the nominal SAR system on SEASAT and the variations of this ratio as a function of orbital latitude and attitude control error are discussed. It is also shown that the detailed range migration-azimuth phase history of an ambiguity is different from that of a signal. The images of ambiguities are, therefore, dispersed. Several examples of such dispersed images observed by the SEASAT SAR are presented. These dispersions are eliminated when the processing parameters are adjusted appropriately. Finally, a method is described which uses a set of multiple pulse repetition frequencies to determine the absolute values of the Doppler centroid frequencies for SARs with high carrier frequencies and relatively poor attitude measurements.

Elevation beamforming and Full Dimension MIMO (FD-MIMO) has been an active area of research and standardization in 3GPP LTE-Advanced. In an FD-MIMO system, a base station with 2-dimensional (2D) active array supports multi-user joint elevation and azimuth beamforming (a.k.a. 3D beamfoming), which results in much higher cell capacity compared to conventional systems. Recent study has shown that with these new FD-MIMO technologies, we can achieve promising 3-5× gain in both cell capacity as well as cell-edge throughput. In this paper, we will provide a brief summary of recent 3GPP activities, including the recently completed 3D channel model, ongoing study on FD-MIMO scenarios, antenna/RF (radio frequency) transceiver architectures, as well as potential network performance benefits. In addition, we also discuss some methods for reducing CSI (channel state information) feedback overhead and ensuring efficient operation of large size FD-MIMO for both TDD and FDD systems.

This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.

It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described.<<ETX>>

Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.

In this paper the use of electronically steerable passive array radiator (ESPAR) antennas is introduced for achieving increased spectral efficiency characteristics in multiple-input, multiple-output (MIMO) systems using a single active element, and compact antennas. The proposed ESPAR antenna is capable of mapping phase-shift-keying (PSK) modulated symbols to be transmitted onto orthogonal basis functions on the wavevector domain of the multi-element antenna (MEA), instead of the traditional approach of sending different symbol streams in different locations on the antenna domain. In this way, different symbols are transmitted simultaneously towards different angles of departure at the transmitter side. We show that the proposed system, called beamspace-MIMO (BS-MIMO) can achieve performance characteristics comparable to traditional MIMO systems while using a single radio-frequency (RF) front-end and ESPAR antenna arrays of total length equal to lambda/8, for popular PSK modulation schemes such as binary-PSK (BPSK), quaternary-PSK (QPSK), as well as for their offset-PSK and differential-PSK variations.

While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and (non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection algorithm may be successfully employed for many SAR research endeavors not involving considerably large data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image reconstruction using the matched filter and backprojection algorithms is provided.

Novel technology to recognize the situation in distant place is necessary to develop a railway safety monitoring system by which human being having fallen onto the tracks from a platform and obstacles in the level crossing can be detected. In this research, we propose a method for detecting a stationary or moving obstacle by the technology which employs the super resolution radar techniques, the image recognition techniques, and the technology to fuse these techniques. Our method is designed for detecting obstacles such as cars, bicycles, and human on the track in the range up to hundreds of meters ahead by using sensors mounted on a train. In super resolution radar techniques, novel stepped multiple frequency radar is confirmed to provide the expected high resolution performance by experimental study using software defined radar. We designed the parameters of the stepped multiple frequency radar. The signal processing processor which employs A/Ds, D/As of high-sampling rate and the highest performance FPGAs has been designed and developed. In image recognition techniques, the main algorithm which detects a pair of rails is based on information about rail edges and intensity gradients. As the obstacle exists in the vicinity of rails, all the detection processes can be limited to the region in the vicinity of rails. As an example, we detect an obstacle by estimating image boundaries which enclose a group of feature points exactly on the obstacle. In order to determine the group of feature points, feature points of the whole image are detected at each frames and tracked over image sequence. For robustness estimation of boundary, we use radar so as to detect the obstacle’s rough position in an image region where an obstacle would exist; the motion segmentation technique is applied to the tracks that is located in the region. Note that an obstacle's position detected by radar is remarkably rough because radar has low transverse resolution. Nevertheless, the position detected by radar contributes to rapid and robust estimation of satisfactory image boundaries. To allow for monitoring in large distances, a prototype for an onboard pan/tilt camera platform with directional control was designed and manufactured as required in a railway situation. To detect obstacles in real time, we have built a high-performance test machine with a GPU and an image processing environment.