Question: Deep Neural Network-based approach for sentiment classification.

This paper presents Adam, an efficient algorithm designed for the optimization of stochastic objective functions through first-order gradient-based methods. Adam stands out for its simplicity in implementation, computational efficiency, minimal memory footprint, and its robustness to diagonal rescaling of gradients. It is particularly advantageous for scenarios characterized by large-scale data and parameters, non-stationary objectives, and gradients that are either highly noisy or sparse. The hyper-parameters of Adam are intuitively understandable, typically requiring minimal adjustment. The paper delves into the connections between Adam and related optimization algorithms, highlighting the inspirations behind its design. A theoretical analysis of Adam's convergence properties is provided, accompanied by a regret bound that aligns with the best current results within the online convex optimization framework. Empirical evidence confirms Adam's practical effectiveness and its competitive edge over other stochastic optimization techniques. Additionally, the paper introduces AdaMax, an alternative version of Adam that utilizes the infinity norm for optimization tasks.

Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several real-world datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.

1Student,Dept. of Computer Engineering, VESIT, Maharashtra, India ---------------------------------------------------------------------------***-------------------------------------------------------------------Abstract Deep Learning has come into existence as a new area for research in Machine Learning. It aims to act like a human brain, having the ability to learn and process from complex data and also tries solving intricate tasks as well. Due to this capability, its been used in various fields like text, sound, images etc. Natural language process has started to being impacted by the deep learning techniques. This research paper highlights Deep Learning’s recent developments and applications in Natural Language Processing.

Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple “do-it-yourself” implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuning the architecture. The end result is a very simple yet general architecture which can yield state-of-the-art performance for document analysis. We illustrate our claims on the MNIST set of English digit

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.

This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Human Computer interaction is a very powerful and most current area of research because the human world is getting more digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is one aspect of human behaviour which plays an important role in human computer interaction, the computer interfaces need to recognize the emotion of the users in order to exhibit a truly intelligent behaviour. Human express the emotion in the form facial expression, speech, and writing text. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. This comprise a challenging style as it is formed with both plaint text and short messaging language. This paper is mainly focused on an overview of emotion detection from text and describes the emotion detection methods. These methods are divided into the following four main categories: keyword-based, Lexical Affinity method, learning based, and hybrid based approach. Limitations of these emotion recognition methods are presented in this paper and also, addresses the text normalization using different handling techniques for both plaint text and short messaging language.

The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification

Automatic sentiment analysis provides an effective way to gauge public opinion on any topic of interest. However, most sentiment analysis tools require a general sentiment lexicon to automatically classify sentiments or opinion in a text. One of the challenges presented by using a general sentiment lexicon is that it is insensitive to the domain since the scores assigned to the words are fixed. As a result, while one general sentiment lexicon might perform well in one domain, the same lexicon might perform poorly in another domain. Most sentiment lexica will need to be adjusted to suit the specific domain to which it is applied. In this paper, we present results of sentiment analysis expressed on Twitter by UK energy consumers. We optimised the accuracy of the sentiment analysis results by combining functions from two sentiment lexica. We used the first lexicon to extract the sentiment-bearing terms and negative sentiments since it performed well in detecting these. We then used a second lexicon to classify the rest of the data. Experimental results show that this method improved the accuracy of the results compared to the common practice of using only one lexicon.

The demand of text classification is growing significantly in web searching, data mining, web ranking, recommendation systems and so many other fields of information and technology. This paper illustrates the text classification process on different dataset using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applied these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analysis the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.


Question: Deep Neural Network-based approach for sentiment classification.

In this comprehensive review, we explore the evolution of linguistic theory from 1930 to 1955, as detailed in "A Synopsis of Linguistic Theory 1930-1955" in Studies in Linguistic Analysis. The document delves into the foundational theories that have shaped our understanding of language, syntax, and semantics. This period was marked by significant shifts in linguistic thought, moving from structuralism to generative grammar. The work highlights the contributions of key figures such as Ferdinand de Saussure, Leonard Bloomfield, and Noam Chomsky, who introduced transformative concepts that continue to influence linguistic studies. The text analyzes the development of phonetics, morphology, syntax, and semantics, providing insights into how these components interact to form meaning in language. It also discusses the role of context and culture in linguistic analysis, emphasizing the importance of understanding language within its social and historical contexts. This review serves as a valuable resource for linguists, educators, and scholars interested in the historical development of linguistic theory.

**Question**: The Role of Blockchain in Enhancing Supply Chain Transparency

**Documents**: The impact of social media on customer satisfaction in the hotel industry

**Similar Documents**: This paper investigates the transformative role of blockchain technology in enhancing transparency within supply chain management. Blockchain, with its decentralized and immutable ledger system, offers a solution to the challenges of traceability and trust in complex supply chains. The study explores how blockchain can improve supply chain visibility, allowing for real-time tracking of goods and verifying their origin and condition. By providing an unalterable record of transactions, blockchain can reduce fraud, increase efficiency, and ensure compliance with regulatory standards. The paper also discusses the potential for blockchain to empower consumers with information about the ethical and environmental impact of their purchases. Case studies from various industries, including food, pharmaceuticals, and luxury goods, illustrate the practical applications of blockchain in enhancing supply chain transparency. The research highlights the importance of collaboration among supply chain stakeholders, including manufacturers, distributors, retailers, and consumers, to fully leverage the benefits of blockchain technology. This study is a valuable resource for business leaders, policymakers, and researchers seeking to understand the implications of blockchain in modern supply chain management.

**Question**: The Influence of Quantum Computing on Cryptography

**Documents**: The effects of mobile technology on education

**Similar Documents**: This article explores the profound impact of quantum computing on the field of cryptography. Quantum computers, capable of processing information using quantum bits (qubits), pose significant challenges to traditional cryptographic methods. Unlike

*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.

Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user’s star rating for a product, given the user’s text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Näıve Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it’s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. * Corresponding author

Cantonese is an important dialect in some regions of Southern China. Local online users often represent their opinions and experiences on the web with written Cantonese. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product and the Cantonese reviews are unintelligible for Mandarin Chinese speakers. In this paper, standard machine learning techniques naive Bayes and SVM are incorporated into the domain of online Cantonese-written restaurant reviews to automatically classify user reviews as positive or negative. The effects of feature presentations and feature sizes on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The naive Bayes classifier achieves as well as or better accuracy than SVM. Character-based bigrams are proved better features than unigrams and trigrams in capturing Cantonese sentiment orientation. 2010 Elsevier Ltd. All rights reserved.

Activity recognition from wearable sensor data has been researched for many years. Previous works usually extracted features manually, which were hand-designed by the researchers, and then were fed into the classifiers as the inputs. Due to the blindness of manually extracted features, it was hard to choose suitable features for the specific classification task. Besides, this heuristic method for feature extraction could not generalize across different application domains, because different application domains needed to extract different features for classification. There was also work that used auto-encoders to learn features automatically and then fed the features into the K-nearest neighbor classifier. However, these features were learned in an unsupervised manner without using the information of the labels, thus might not be related to the specific classification task. In this paper, we recommend deep neural networks (DNNs) for activity recognition, which can automatically learn suitable features. DNNs overcome the blindness of hand-designed features and make use of the precious label information to improve activity recognition performance. We did experiments on three publicly available datasets for activity recognition and compared deep neural networks with traditional methods, including those that extracted features manually and auto-encoders followed by a K-nearest neighbor classifier. The results showed that deep neural networks could generalize across different application domains and got higher accuracy than traditional methods.

Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologicallytrained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy – correlated at r = .65 with valence and r = .85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.

This paper compares the performance of two recurrent neural network models learning the grammar of simple English sentences and introduces a method for creating useful negative training examples from a set of grammatical sentences. The learning task is cast as a classification problem as opposed to a prediction problem; networks are taught to classify a given sequence as grammatical or ungrammatical. The two neural network models compared are the simple architecture proposed by Elman in [1], and the Long Short-Term Memory (LSTM) network from [4]. For comparison, a Naive Bayes (NB) classifier is also trained with bigrams to accomplish this task. Only the Elman network learns the training data better than NB, and neither network generalizes better than NB on sentences longer than the training sentences. The LSTM network does not generalize as well as the Elman network on a simple regular grammar, but shows comparable ability to generalize knowledge of English Grammar.

This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.

The purpose of this work is to empirically study desirable properties such as semantic coherence, attention mechanism and kernel reusability in Convolution Neural Networks (CNNs) for learning sentence classification tasks. We propose to learn semantically coherent kernels using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet. We also suggest a technique to visualize attention mechanism of CNNs. These ideas are useful for decision explanation purpose. Reusable property enables kernels learned on one problem to be used in another problem. This helps in efficient learning as only a few additional domain specific kernels may have to be learned. Experimental results demonstrate the usefulness of our approach. The performance of the proposed approach, which uses semantic and re-usability properties, is close to that of the state-of-the-art approaches on many real-world datasets.


Question: Deep Neural Network-based approach for sentiment classification.

This paper explores the capabilities of TensorFlow, a software library designed for large-scale machine learning on diverse, distributed systems. The authors, a team of researchers from Google, detail how TensorFlow supports the training of complex models across a range of hardware, including CPUs, GPUs, and specialized accelerators. The system's flexibility and scalability are highlighted through its ability to handle a variety of machine learning tasks, from simple linear regression to deep neural networks with millions of parameters. Key features of TensorFlow include its computational graph abstraction, which allows for efficient data flow and parallel execution, and its support for automatic differentiation, which simplifies the implementation of gradient-based optimization. The paper also discusses TensorFlow's distributed computing capabilities, enabling the training of models on clusters of machines. The authors showcase TensorFlow's effectiveness in various domains, including natural language processing, computer vision, and speech recognition, demonstrating its potential for sentiment classification with deep neural networks.

**Question**: The Role of Blockchain in Supply Chain Management

**Documents**: A Blockchain-based Framework for Secure and Transparent Supply Chain Management
In this paper, we propose a blockchain-based framework for enhancing security and transparency in supply chain management. The use of blockchain technology offers a decentralized and immutable ledger system, which can significantly improve the traceability and accountability of goods throughout the supply chain. By leveraging smart contracts, the framework enables automated and trustless interactions between supply chain participants, reducing the need for intermediaries and manual verification processes. We discuss the architecture of the proposed framework, which includes the integration of Internet of Things (IoT) devices for real-time data collection and monitoring. The paper also presents a case study on the implementation of the framework in a food supply chain, demonstrating its potential to prevent food fraud and ensure product safety. We analyze the challenges and limitations of blockchain adoption in supply chain management and propose potential solutions. Finally, we conclude with the future research directions in this field.

**Similar Documents**: This paper introduces a novel blockchain-based framework designed to revolutionize supply chain management by enhancing security and transparency. Blockchain technology, with its decentralized and unalterable ledger, significantly bolsters the traceability and accountability of products as they move through the supply chain. Smart contracts, a key feature of the framework, facilitate automated and reliable interactions between supply chain entities, minimizing the reliance on intermediaries and manual checks. The proposed framework's architecture integrates Internet of Things (IoT) devices for continuous data capture and surveillance, ensuring real-time visibility. A food supply chain case study is presented, illustrating how the framework can combat food fraud and safeguard product integrity. The

Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method hased on neural networks (Net-Tagger) is presented and its performance is compared to that of a llMM-tagger (Cutting et al., 1992) and a trigrambased tagger (Kempe, 1993). It is shown that the Net-Tagger performs as well as the trigram-based tagger and better than the iIMM-tagger.

Sentiment Analysis deals with the analysis of emotions, opinions and facts in the sentences which are expressed by the people. It allows us to track attitudes and feelings of the people by analyzing blogs, comments, reviews and tweets about all the aspects. The development of Internet has strong influence in all types of industries like tourism, healthcare and any business. The availability of Internet has changed the way of accessing the information and sharing their experience among users. Social media provide this information and these comments are trusted by other users. This paper recognizes the use and impact of social media on healthcare industry by analyzing the users’ feelings expressed in the form of free text, thereby gives the quality indicators of services or features related with them. In this paper, a sentiment classifier model using improved Term Frequency Inverse Document Frequency (TFIDF) method and linear regression model has been proposed to classify online reviews, tweets or customer feedback for various features. The model involves the process of gathering online user reviews about hospitals and analyzes those reviews in terms of sentiments expressed. Information Extraction process filters irrelevant reviews, extracts sentimental words of features identified and quantifies the sentiment of features using sentiment dictionary. Emotionally expressed positive or negative words are assigned weights using the classification prescribed in the dictionary. The sentiment analysis on tweets/reviews is done for various features using Natural Language Processing (NLP) and Information Retrieval (IR) techniques. The proposed linear regression model using the senti-score predicts the star rating of the feature of service. The statistical results show that improved TF-IDF method gives better accuracy when compared with TF and TF-IDF methods, used for representing the text. The senti-score obtained as a result of text analysis (user feedback) on features gives not only the opinion summarization but also the comparative results on various features of different competitors. This information can be used by business to focus on the low scored features so as to improve their business and ensure a very high level of 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.

Emotion recognition from speech plays an important role in developing affective and intelligent systems. This study investigates sentence-level emotion recognition. We propose to use a two-step approach to leverage information from subsentence segments for sentence level decision. First we use a segment level emotion classifier to generate predictions for segments within a sentence. A second component combines the predictions from these segments to obtain a sentence level decision. We evaluate different segment units (words, phrases, time-based segments) and different decision combination methods (majority vote, average of probabilities, and a Gaussian Mixture Model (GMM)). Our experimental results on two different data sets show that our proposed method significantly outperforms the standard sentence-based classification approach. In addition, we find that using time-based segments achieves the best performance, and thus no speech recognition or alignment is needed when using our method, which is important to develop language independent emotion recognition systems.

In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.

In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.

Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

Current approaches in aspect-based sentiment analysis ignore or neutralize unhandled issues emerging from the lexicon-based scoring (i.e., SentiWordNet), whereby lexical sentiment analysis only classifies text based on affect word presence and word count are limited to these surface features. This is coupled with considerably low detection rate among implicit concepts in the text. To address this issues, this paper proposed the use of ontology to i) enhance aspect extraction process by identifying features pertaining to implicit entities, and ii) eliminate lexicon-based sentiment scoring issues which, in turn, improve sentiment analysis and summarization accuracy. Concept-level sentiment analysis aims to go beyond word-level analysis by employing ontologies which act as a semantic knowledge base rather than the lexicon. The outcome is an Ontology-Based Product Sentiment Summarization (OBPSS) framework which outperformed other existing summarization systems in terms of aspect extraction and sentiment scoring. The improved performance is supported by the sentence-level linguistic rules applied by OBPSS in providing a more accurate sentiment analysis.


Question: Altered Wilkinson power dividers for millimeter-wave integrated circuit design.

This study delves into the electrical characteristics of liquid crystal polymer (LCP) material and the performance of transmission lines fabricated on LCP substrates, specifically in the millimeter-wave frequency range of 30 to 110 GHz. LCP has emerged as a promising candidate for high-performance microwave substrates and packaging due to its superior dielectric properties. To accurately determine the electrical properties of LCP at millimeter-wave frequencies, the research employs a variety of methodologies. Microstrip ring resonators and cavity resonators are utilized to measure the dielectric constant (/spl epsi//sub r/) and loss tangent (tan/spl delta/) of LCP above 30 GHz. The results indicate that the dielectric constant remains stable at approximately 3.16, while the loss tangent is consistently below 0.0049. Furthermore, the study examines various transmission lines created on LCP substrates of differing thicknesses, presenting loss characteristics in decibels per centimeter across the frequency range from 2 to 110 GHz. At 110 GHz, peak transmission-line losses are observed to range between 0.88 and 2.55 dB/cm, contingent on the line type and geometry. These findings represent a significant milestone, as they demonstrate for the first time that LCP exhibits outstanding dielectric properties suitable for millimeter-wave applications, thereby solidifying its potential as a high-performance material in the field of microwave technology.

This paper presents an improved broadband balun using a coplanar-waveguide(CPW)-to-slotline field transformation. It operates at very wide frequency, and is of compact size since it does not depend on a resonant structure. The measured results show a passband of 200 MHz to 2 GHz, insertion loss less than 0.75 dB and a size of 20 mm × 14 mm. The amplitude imbalance is approximately 0.3 dB and the phase imbalance is less than 6o over the entire operation range.

A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.

This paper presents a tunable CMOS directional coupler that utilizes lumped-element L-C sections. The lumped-element approach used to build the directional coupler makes it possible to integrate the coupler onto a single monolithic microwave integrated circuit (MMIC), as it occupies a small area compared to printed designs. The directional coupler uses varactors and tunable active inductors (TAIs) to synthesize the series and shunt reactances, respectively, which allows extensive electronic control over the coupling coefficient, while insuring a low return loss and a very high isolation. Furthermore, using varactors and TAIs allows the directional coupler to be reconfigured for operation over a wide range of frequencies. Moreover, the symmetric configuration of the coupler allows it to switch from forward to backward operation by simply exchanging the bias voltages applied across the series varactors. The MMIC coupler was fabricated in a standard 0.13-mum CMOS process and operates from a 1.5-V supply. The circuit occupies 730 mum times 600 mum, and is capable of achieving tunable coupling coefficients from 1.4 to 7.1 dB, while maintaining an isolation higher than 41 dB. The MMIC coupler is also capable of operating at any center frequency over the 2.1-3.1-GHz frequency range with higher than 40-dB isolation. The coupler achieves a -4.1-dBm 1-dB compression point while operating from a 1.5-V supply.

A W-band SIW horn antenna is designed and fabricated using PCB process. Measured S11 reaches -15 dB at 84 GHz with bandwidth of 1 GHz and the maximum simulated gain is 9 dBi. The antenna is loaded with dielectric to increase gain and the effect of different loading lengths on gain is studied. The antenna is fed using a WR10 standard waveguide through a coupling slot in the SIW. The effect of changing the slot width on the return loss is studied. Good agreement is achieved between measured and simulated results. The proposed antenna is suitable for medical imaging and air traffic radar.

This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.

This paper presents a novel size reduction technique for designing a Butler matrix using a new type of coupler. This coupler is half the length of a conventional coupler and has a swapped port characteristic wherein the locations of the isolation and coupled ports are switched. These features make the new coupler, called the "swapped port coupler," effective for reducing the size of the Butler matrix. The behavior of the swapped port coupler was mathematically analyzed, and its design equation was derived. Furthermore, the proposed coupler was employed to design a miniaturized Butler matrix. The area of this Butler matrix was 70 × 70 mm2, and its operating frequency was 1 GHz. When the area was normalized to the frequency, the area of the proposed Butler matrix was 10% of a conventional Butler matrix.

Millimeter-wave technology brings a new paradigm of wireless communication in various areas including mobile devices, automotive, IoT (Internet of Things), medical, military and many others. In this paper, we present our recently developed antenna in a package and system for 5G (Fifth-Generation) millimeter-wave applications. The antenna is configured to be an array to compensate path loss and maintain data link budget. The antenna is designed with a multi-layer laminate substrate and is integrated with a 5G mmW RFIC. 5G NR (New Radio) wireless connection is demonstrated with a RFIC transceiver at 28 GHz band. Interoperability test is conducted to ensure compatibility with the 3GPP standard. Finally, indoor and outdoor field test including beamforming and beamtracking are presented to show realistic system-level throughput performance.

In this letter, a class of wideband bandpass filters (BPFs) with short-circuited multi-mode resonators (MMRs) is proposed and designed by a synthesis approach. The MMRs are formed by n sections of cascaded transmission lines, which are all set to be one quarter wavelength long with respect to the center frequency of the passband. Short-ended stubs are then added at the two ends of the MMR as inductive coupling elements. For the filter design, a transfer function is derived to characterize the frequency response of the proposed BPF, and it is enforced to apply to the equal-ripple in-band response. As such, the impedance value for each section is determined once the specifications are given. With this method, design curves of different orders and ripple factors are provided to aid the design of a class of wideband BPFs. As a design example, a fifth-order ultra-wide filter is designed, fabricated and measured in the end. The experimental results verify the design approach.

The stacked-device power-combining technique is a proven method to increase the output power and load impedance of a power amplifier (PA) simultaneously. The series-input configuration is physically realizable for multicell stacked device configuration in monolithic circuits. The series-input and series-output stack configuration is rigorously analyzed and proven to increase both the input impedance and output impedance simultaneously, easing the matching circuit designs in high PAs. The effects of asymmetry of the input feed and amplifier cells due to distributed effects and process variation on the performance of the stack amplifier are discussed. A four-cell HBT amplifier operating at 5-6 GHz is demonstrated to validate the circuit concept.

A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} < -13.6$  dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.


Question: Altered Wilkinson power dividers for millimeter-wave integrated circuit design.

This paper introduces a novel millimeter-wave Wilkinson power divider fabricated on a flexible, thin, and robust organic substrate, alongside an investigation into bend configurations aimed at minimizing coupling between output paths. The divider exhibits superior performance throughout the entire Ka-band frequency range, spanning from 27 to 40 GHz. With an insertion loss of roughly 0.3 dB and an isolation of approximately 30 dB between output ports at the designated frequency, the divider showcases remarkable efficiency. This design not only meets the stringent requirements of millimeter-wave applications but also demonstrates the potential for low-cost, monolithic integration on flexible substrates, making it a promising solution for advanced communication systems.

Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.

We design and fabricate double-layer slotted waveguide array antennas with wide bandwidths and high efficiencies for 120 GHz and 350 GHz bands. To achieve high gain and high efficiency in high frequency bands, the diffusion bonding technique of plate laminated waveguide is used as a fabrication method. The antennas with 16×16 slots show about 70% efficiency with 32 dBi gain and about 50% efficiency with 31 dBi gain in the 120 GHz and 350 GHz bands, respectively.

A W-band SIW horn antenna is designed and fabricated using PCB process. Measured S11 reaches -15 dB at 84 GHz with bandwidth of 1 GHz and the maximum simulated gain is 9 dBi. The antenna is loaded with dielectric to increase gain and the effect of different loading lengths on gain is studied. The antenna is fed using a WR10 standard waveguide through a coupling slot in the SIW. The effect of changing the slot width on the return loss is studied. Good agreement is achieved between measured and simulated results. The proposed antenna is suitable for medical imaging and air traffic radar.

A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.

A compact waveguide twist using a single step of novel ridged structure for integrated waveguide subsystems is proposed. The cross-section of the ridged waveguide has the shape of two squares partially overlapped. The advantages of this configuration are the facilitation of manufacturing and the reduction of overall dimensions. In this paper, the broadband operation principle is described by using equivalent circuit, moreover the its design using HFSS and the experimental results are presented. In the result, the return loss in excess of 30dB could be obtained over 22 percent of frequency band without additional waveguide steps. The twist length is 0.22 times the wavelength of the standard rectangular waveguides

A metamaterial balun that converts a single-ended input to a differential output over a large bandwidth is presented. The device also exhibits excellent return loss, isolation, and through characteristics over the same frequency band. The balun comprises a Wilkinson divider, followed by a +90/spl deg/ negative-refractive-index (NRI) metamaterial (MM) phase-shifting line along the top branch, and a -90/spl deg/ MM phase-shifting line along the bottom branch. Utilizing MM lines for both the +90/spl deg/ and -90/spl deg/ branches allows the slopes of their phase responses to be matched, resulting in a broadband differential output signal. The theoretical performance of the balun is verified through circuit simulations and measurements of a fabricated prototype at 1.5 GHz. The MM balun exhibits a measured differential output phase bandwidth (180/spl deg//spl plusmn/10/spl deg/) of 1.16 GHz (77%), from 1.17 to 2.33 GHz. The measured isolation and return loss for all three ports remain below -10 dB over a bandwidth in excess of 2 GHz, while the output quantities |S/sub 21/| and |S/sub 31/| remain above -4 dB from 0.5 to 2.5 GHz.

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.

A low-cost high-gain and broadband substrate integrated waveguide (SIW)-fed patch antenna array is demonstrated at the 60-GHz band. A single-layered SIW feeding network with wideband T-junctions and wideband high-gain cavity-backed patch antennas are employed to achieve high gain and wideband performance simultaneously. Although the proposed antenna array has a multilayered structure, it can be fabricated by conventional low-cost single-layered printed circuit board (PCB) technology and then realized by stacking and fixing all of single layers together. The simulated and measured impedance bandwidths of a 4 × 4 antenna array are 27.5% and 22.6% for 10 dB. The discrepancy between simulation and measurement is analyzed. A gain up to 19.6 dBi, and symmetrical unidirectional radiation patterns with low cross polarization are also achieved. With advantages of low fabrication cost and good performances, the proposed antenna array is a promising candidate for millimeter-wave wireless communication systems.

In this paper, a new procedure for the design of contiguous band manifold output multiplexers in H-plane configuration is presented. Such a configuration eliminates the slots between the T-junctions and the filters allowing high power handling, as well as dramatically reducing the risk of multipactor. This paper pursues the division of the design into tasks with low computational effort. The approach comprises: 1) the singly terminated synthesis of the channel filters and their corresponding full-wave responses; 2) the manifold design using fictitious reactive loads simulating the phase response of every channel out of its corresponding band; and 3) the final full-wave optimization of the whole structure using the simulated-annealing method. In order to validate the above procedure, a five-channel Ku-band multiplexer has been designed, manufactured, and measured.

An eleven-stage broadband SIW filter has been designed, fabricated and measured. The measured results show that the SIW filter can be operated from 9.25 GHz to 11.25 GHz with good selectivity performance. The filter has a small size, low weight, and can be integrated with other SIW components without any transitions. It's suitable for the design of microwave and millimeter wave integrated circuits


Question: Altered Wilkinson power dividers for millimeter-wave integrated circuit applications.

This document explores the application and performance of Liquid Crystal Polymer (LCP) materials in microwave and millimeter-wave multilayer packaging. Innovative processing methods have been devised to construct interconnects on LCP, showcasing its potential in advanced packaging technologies. Through comprehensive testing, it has been confirmed that interconnects fabricated on LCP exhibit an impressively low insertion loss, measured at under 0.1 dB/mm across a frequency spectrum up to 50 GHz. These findings underscore the exceptional compatibility of LCP for use in microwave and millimeter-wave packaging applications, where high-frequency performance and low-loss characteristics are paramount.

A systematic approach to designing high frequency and high power oscillators using activity condition is introduced. This method finds the best topology to achieve frequencies close to the fmax of the transistors. It also determines the maximum frequency of oscillation for a fixed circuit topology, considering the quality factor of the passive components. Using this technique, in a 0.13 μm CMOS process, we design and implement 121 GHz and 104 GHz fundamental oscillators with the output power of -3.5 dBm and -2.7 dBm, respectively. Next, we introduce a novel triple-push structure to realize 256 GHz and 482 GHz oscillators. The 256 GHz oscillator was implemented in a 0.13 μm CMOS process and the output power of -17 dBm was measured. The 482 GHz oscillator generates -7.9 dBm (0.16 mW) in a 65 nm CMOS process.

A novel 90deg broadband balun comprising a broadband 90deg Schiffman phase shifter is introduced as a means of enhancing the wideband circular polarization performance of dual-fed type microstrip antennas. The proposed 90deg broadband balun delivers good impedance matching, balanced power splitting and consistent 90deg (plusmn5deg) phase shifting, across a wide bandwidth (~57.5%). A circular patch antenna utilizing the proposed 90deg broadband balun is shown to attain measured impedance(S11< -10 dB) and axial ratio (AR < 3 dB) bandwidths of 60.24% and 37.7%, respectively, for the dual L-probe case; and 71.28% and 81.6% respectively, for the quadruple L-probe case.

This paper reports the design and analysis of 21-29-GHz CMOS low-noise amplifier (LNA), balun and mixer in a standard 0.18-μm CMOS process for ultra-wideband automotive radar systems. To verify the proposed LNA, balun, and mixer architectures, a simplified receiver front-end comprising an LNA, a double-balanced Gilbert-cell-based mixer, and two Marchand baluns was implemented. The wideband Marchand baluns can convert the single RF and local oscillator (LO) signals to nearly perfect differential signals over the 21-29-GHz band. The performance of the mixer is improved with the current-bleeding technique and a parallel resonant inductor at the differential outputs of the RF transconductance stage. Over the 21-29-GHz band, the receiver front-end exhibits excellent noise figure of 4.6±0.5 dB, conversion gain of 23.7±1.4 dB, RF port reflection coefficient lower than -8.8 dB, LO-IF isolation lower than -47 dB, LO-RF isolation lower than -55 dB, and RF-IF isolation lower than -35.5 dB. The circuit occupies a chip area of 1.25×1.06 mm2, including the test pads. The dc power dissipation is only 39.2 mW.

A compact, low cost and high radiation efficiency antenna structure, planar waveguide, substrate integrated waveguide (SIW), dielectric resonator antennas (DRA) is presented in this paper. Since SIW is a high Q- waveguide and DRA is a low loss radiator, then SIW-DRA forms an excellent antenna system with high radiation efficiency at millimeter-waveband, where the conductor loss dominates. The impact of different antenna parameters on the antenna performance is studied. Experimental data for SIW-DRA, based on two different slot orientations, at millimeter-wave band are introduced and compared to the simulated HFSS results to validate our proposed antenna model. A good agreement is obtained. The measured gain for SIW-DRA single element showed a broadside gain of 5.51 dB,-19 dB maximum cross polarized radiation level, and overall calculated (simulated using HFSS) radiation efficiency of greater than 95%.

A method of using low-loss waveguide septum combiners is developed into a high-power -band (31-36 GHz) amplifier producing 50 W at 33 GHz (Ka-band) using 32 low-power (>2 W) solid-state amplifier modules. By using low-loss waveguide combining and a packaged monolithic microwave integrated circuit with a low-loss microstrip-to-waveguide launcher, the output loss is minimized, allowing for the overall power-combining efficiency to remain high, 80% (average insertion loss of combiner < 0.7 dB and average insertion loss of launcher <0.3 dB) over 31-36 GHz. In the past, lower power-combining efficiencies have limited the number of modules that can be combined at -band, and hence, have limited the power output. The approach demonstrated in this paper, with high power-combining efficiency, allows a very large number (32) of solid-state amplifier modules to be combined to produce high powers. Greater than 50 W was demonstrated with low power modules, but even higher powers 120 W are possible. The current approach is based on corporate combining, using low-loss waveguide septum combiners that provide isolation, maintaining the true graceful degradation of a modular solid-state amplifier system.

A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} < -13.6$  dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.

This paper presents a balanced frequency doubler with 6.5-dBm peak output power at 204 GHz in 130-nm SiGe BiCMOS technology ( $f_{T}/f_{\max }=210$ /250 GHz). To convert the single-ended input signal to a differential signal for balanced operation, an on-chip transformer-based balun is employed. Detailed design procedure and compensation techniques to lower the imbalance at the output ports, based on mixed mode S parameters are proposed and verified analytically and through electromagnetic simulations. The use of optimized harmonic reflectors at the input port results in a 2-dBm increase in output power without sacrificing the bandwidth of interest. The measured conversion loss of the frequency doubler is 9 dB with 6-dBm input power at 204-GHz output. The measured peak output power is 6.5 dBm with an on-chip power amplifier stage. The 3-dB output power bandwidth is measured to be wider than 50 GHz (170–220 GHz). The total chip area of the doubler is 0.09 mm2 and the dc power consumption is 90 mW from a 1.8-V supply, which corresponds to a 5% collector efficiency.

Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.

A dual circularly polarized (CP) waveguide array antenna is presented for Ka-band wireless communication. A novel triple-resonant cavity is proposed to implement mode conversion and impedance matching in the <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>-element subarray. A ridge waveguide polarizer is integrated to form the left-hand circular polarization (LHCP) and the right-hand circular polarization (RHCP). A flyover-shaped full-corporate feeding network is designed to accommodate the dual polarizations and keep the wideband characteristic for a large-scale array. Subarray spacing is used to optimize the port-to-port isolation and decrease the sidelobe level at some definite angles. Computer numerically controlled machining technology is applied to fabricating a <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula>-element prototype. Measured results demonstrate a bandwidth of 16% from 27.6 to 32.4 GHz for the dual polarizations, in terms of good reflection coefficient and axial ratio. A maximum gain of 32.8 dBic is achieved at 28.8 GHz. A total efficiency over 60% is obtained throughout the above band for both LHCP and RHCP ports.

We have demonstrated a 0.1-1.8 GHz, 100 W GaN HEMT power amplifier module with 11 dB gain, 94-142 W CW output power and 40.6-74.0 % drain efficiency over the band. The power amplifier module uses broadband low loss coaxial baluns to combine four 30 W broadband lossy matched GaN HEMT PAs on a 2 x 2 inch compact PCB. The individual PAs are fully matched to 50 Ohms and obtains 23.6-30.9 W with 44.5-63.7 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 50 V drain voltage with a GaAs integrated passive matching circuitry. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.


Question: Altered Wilkinson power dividers for millimeter-wave integrated circuit design.

This paper introduces an innovative via-less transition between coplanar waveguide (CPW) and microstrip configurations, offering a wideband solution for radio frequency (RF) applications. The design guidelines, grounded in both simulations and experimental outcomes, are outlined to facilitate practical implementation. The transition exhibits an impressive maximum insertion loss of 1 dB across a broad frequency spectrum from 10 GHz to 40 GHz, with a mere 0.4 dB at 20 GHz. This versatile design is particularly appealing for integration into RF systems-on-a-chip, thanks to its low-loss characteristics, cost-effectiveness, and simplified fabrication process. Its compatibility and performance make it a promising choice for a multitude of applications in the millimeter-wave domain.

This paper describes the analysis and design of saturated silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) switches for millimeter-wave applications. A switch optimization procedure is developed based on detailed theoretical analysis and is then used to design multiple switch variants. The switches utilize IBM's 90-nm 9HP technology, which features SiGe HBTs with peak f T/ fmax of 300/350 GHz. Using a reverse-saturated configuration, a single-pole double-throw switch with a measured insertion loss of 1.05 dB and isolation of 22 dB is achieved at 94 GHz after de-embedding pad losses. The switch draws 5.2 mA from a 1.1-V supply, limiting power consumption to less than 6 mW. The switching speed is analyzed and the simulated turn-on and turn-off times are found to be less than 200 ps. A technique is also introduced to significantly increase the power-handling capabilities of saturated SiGe switches up to an input-referred 1-dB compression point of 22 dBm. Finally, the impact of RF stress on this novel configuration is investigated and initial measurements over a 48-h period show little performance degradation. These results demonstrate that SiGe-based switches may provide significant benefits to millimeter-wave systems.

This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.

A novel single-fed low-cost wideband and high-gain slotted cavity antenna based on substrate integrated waveguide (SIW) technology using high-order cavity modes is demonstrated in this paper. High-order resonant modes (TE130, TE310, TE330) inside the cavity are simply excited by a coaxial probe which is located at the center of the antenna. Energy is coupled out of the cavity by a 3 × 3 slot array etched on the top surface of the cavity. Two antennas with different polarizations are designed and tested. Measured results show that the linearly polarized prototype achieves an impedance bandwidth (|S11| <; -10 dB) of > 26% (28 to 36.6 GHz), and a 1-dB gain bandwidth of 14.1% (30.3 to 34.9 GHz). In addition, a measured maximum gain of 13.8 dBi and radiation efficiency of 92% are obtained. To generate circularly polarized radiation, a rotated dipole array is placed in front of the proposed linearly polarized antenna. Measured results show that the circularly polarized antenna exhibits a common bandwidth (10-dB return loss bandwidth, 3-dB axial ratio bandwidth, and 1-dB gain bandwidth) of 11%.

A high gain, high-efficiency antipodal tapered slot antenna with sine-shaped corrugation and a Fermi profile substrate cut-out has been developed for 5G millimeter wave (MMW) communications. A parametric study of a new substrate cutout with Fermi profile is demonstrated to reduce the sidelobe level at the E-plane and H-plane as well as to increase antenna efficiency by an average of 88% over a band of 20-40 GHz. A low-cost printed circuit board (PCB) is processed simply with a CNC Milling machine to fabricate the proposed antenna with Fermi profile substrate cut-out. The measured reflection coefficient is found to be less than -14 dB over a frequency range of 20-40 GHz. Furthermore, the measured gain of the proposed antenna is 17 dB at 30 GHz and the measured radiation pattern and gain is almost constant within the wide bandwidth from 30-40 GHz. Therefore, this antenna is proposed for use in an H-plane array structure such as for point-to-point communication systems, a switched-beam system.

Usually transitions from microstrip line to rectangular waveguide are made with three-dimensional complex mounting structures. In this paper, a new planar platform is developed in which the microstrip line and rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple taper. Our experiments at 28 GHz show that an effective bandwidth of 12% at 20 dB return loss is obtained with an in-band insertion loss better than 0.3 dB. The new transition allows a complete integration of waveguide components on substrate with MICs and MMICs.

A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.

This paper presents the design, fabrication, and characterization of a compact wideband antenna for high-power microwave applications. The antennas proposed are array of high-power wideband patches with high compactness and less than λ/10 thick. The concept developed can be fed by high-voltage signals (up to 60 kV) in repetitive operation. Two designs are produced at central frequencies of 350 MHz and 1 GHz. Their relative bandwidth is larger than 40% at 350 MHz and 25% at 1 GHz for S11 <; - 10 dB, respectively. The arrays studied produce a gain of more than 14 dB.

Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.

This proposed paper, a single band microstrip patch antenna for 5G wireless application is presented. This proposed antenna is suitable for the millimeter wave frequency. The single band antenna consist of new H slot and E slot loaded on the radiating patch with the 50 ohms microstrip line feeding used. This single band antenna is simulated on a Rogers RT5880 dielectric substrate have relative permittivity 2.2, loss tangent 0.0009, and height 1.6mm. The antenna is simulated by Electromagnetic simulation, computer software technology Microwave studio. The proposed single band antenna and simulated result on return loss, VSWR, surface current and 3D radiation pattern is presented. The simulated antenna shows the return loss −40.99dB at 60 GHz millimeter wave 5G wireless application presented.

This paper presents a C-band rectifier employing a third harmonic termination which is realized by a λg/12 short-ended microstrip transmission line. The proposed harmonic termination balances the capacitive impedance of the diode at the fundamental frequency and blocks harmonic current of the diode. The input low-pass filter or matching circuit is removed and the bandwidth of rectifier is enhanced. Theoretical analysis and experiments are carried out. The results show that the proposed topology can realize high efficiency with a wide input frequency range. The fabricated rectifier has a compact dimension of 14mm by 22mm. A maximum RF-DC conversion efficiency of 75.6% at 13 dBm input power is observed. The measured efficiency remains above 70% with the operating frequency form 5.2 GHz to 6 GHz.


Question: Altered Wilkinson power dividers for millimeter-wave integrated circuit applications.

This paper introduces a novel metamaterial-based Wilkinson balun designed for broadband operation, capable of transforming a single-ended input signal into a differential output across a wide frequency range. The device boasts exceptional return loss, isolation, and through performance within the same frequency band. The balun's architecture consists of a Wilkinson divider, followed by a +90-degree negative-refractive-index (NRI) metamaterial (MM) phase-shifting line on the upper branch and a -90-degree MM phase-shifting line on the lower branch. By employing MM lines for both the +90-degree and -90-degree branches, the phase response slopes are matched, facilitating a broadband differential output signal. The theoretical efficacy of the balun is substantiated through comprehensive circuit simulations and experimental measurements of a prototype fabricated for operation at 1.5 GHz. The metamaterial balun exhibits a measured differential output phase bandwidth (180 degrees +/- 10 degrees) of 1.16 GHz (77%), spanning from 1.17 to 2.33 GHz. Moreover, the measured isolation and return loss for all three ports remain below -10 dB over a bandwidth exceeding 2 GHz, while the output parameters |S21| and |S31| stay above -4 dB from 0.5 to 2.5 GHz. This design showcases the potential of metamaterials in enhancing the performance of Wilkinson baluns for millimeter-wave integrated circuits.

We evaluate the possibility of using a finline orthomode transducer (OMT) at millimeter wavelengths. A finline OMT has low loss, low cross-polarization, and good return loss over a full waveguide band. We propose a novel finline OMT structure for millimeter-wavelengths and present results at X-band.

This paper presents a 4 × 4 transmit/receive (T/R) SiGe BiCMOS phased-array chip at 90-100 GHz with vertical and horizontal polarization capabilities, 3-bit gain control (9 dB), and 4-bit phase control. The 4 × 4 phased array fits into a 1.6×1.5 mm2 grid, which is required at 94 GHz for wide scan-angle designs. The chip has simultaneous receive (Rx) beam capabilities (V and H) and this is accomplished using dual-nested 16:1 Wilkinson combiners/divider with high isolation. The phase shifter is based on a vector modulator with optimized design between circuit level and electromagnetic simulation and results in 1 dB and gain and phase error, respectively, at 85-110 GHz. The behavior of the vector modulator phase distortion versus input power level is investigated and measured, and design guidelines are given for proper operation in a transmit (Tx) chain. The V and H Rx paths result in a gain of 22 and 25 dB, respectively, a noise figure of 9-9.5 (max. gain), and 11 dB (min. gain) measured without the T/R switch, and an input P1 dB of -31 to -26 dBm over the gain control range. The measured output Psat is ~ -5 dBm per channel, limited by the T/R switch loss. Measurements show ±0.6- and ±0.75-dB variation between the 4 × 4 array elements in the Tx mode (Psat) and Rx mode, respectively, and 40-dB coupling between the different channels on the chip. The chip consumes 1100 mA from a 2-V supply in both the Tx and Rx modes. The design can be scaled to >10 000 elements using polyimide redistribution layers on top of the chip and the application areas are in W-band radars for landing systems.

Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.

Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.

It is known that a radial power combiner is very effective in combining a large number of power amplifiers, where high efficiency (greater than 90%) over a relatively wide band can be achieved. However, its current use is limited due to its design complexity. In this paper, we develop a step-by-step design procedure, including both the initial approximate design formulas and suitable models for final accurate design optimization purposes. Based on three-dimensional electromagnetic modeling, predicted results were in excellent agreement with those measured. Practical issues related to the radial-combiner efficiency, its graceful degradation, and the effects of higher order package resonances are discussed here in detail

Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.

This paper presents a T-shaped antenna at millimetre-wave (MMW) frequency ranges to offer a number of advantages including simple structure, high operating bandwidth, and high gain. Defected ground structures (DGS) have been symmetrically added in ground in order to produce multiple resonating bands, accompanied by partial ground plane to achieve continuous operating bandwidth. The antenna consists of T-shaped radiating patch with a coplanar waveguide (CPW) feed. The bottom part has a partial ground plane loaded with five symmetrical split-ring slots. Measured results of antenna prototype show a wide bandwidth of 25.1-37.5 GHz. Moreover, simulation evaluation of peak gain of the antenna is 9.86 dBi at 36.8 GHz, and efficiency is higher than 80% in complete range of operation. The proposed antenna is considered as a potential candidate for the 5G wireless networks and applications.

Coupled-resonator network is applied to the design of a rat-race coupler with bandpass response in this paper. Net-type resonators are used to reduce the circuit size and control the harmonics. As a result, the proposed coupler exhibits not only a compact size but also good stopband rejection. The proposed coupler occupies only one sixth area of the conventional design, while the rejection level in the stopband is better than 30 dB up to 4.3ƒ0. The measured results are in good agreement with the simulated predictions.

A 45GHz Doherty power amplifier is implemented in 45nm SOI CMOS. Two-stack FET amplifiers are used as main and auxiliary amplifiers, allowing a supply voltage of 2.5V and high output power. The use of slow-wave coplanar waveguides (CPW) improves the PAE and gain by approximately 3% and 1dB, and reduces the die area by 20%. This amplifier exhibits more than 18dBm saturated output power, with peak power gain of 7dB. It occupies 0.64mm2 while achieving a peak PAE of 23%; at 6dB back-off the PAE is 17%.

A slot array antenna is designed using ridge gap waveguide technology in two sub arrays. Each sub array is fed through a power divider which is designed on a separate ridge- groove gap waveguide. The coupling between two gap waveguides is conducted using coaxial transmission lines. The power divider of ridge-groove gap is fed using coaxial line that can provide a bandwidth of 22% at 14 GHz. The integrated antenna operates within the frequency range of 13.5-15.6 GHz with a gain of 13.9-15.5 dB. Design of the transmission line, power divider and sub-array antenna is presented.


Question: A creative design method for visualizing rooted tree structures.

Treevis.net: An Extensive Resource for Tree Visualization Techniques

Tree visualization constitutes a significant domain within the realm of information visualization, with over 200 documented techniques for visualizing and laying out tree structures. The Treevis.net project endeavors to serve as a comprehensive, hand-curated bibliographical resource for these evolving visualization methods. This platform provides users with a visual survey of available techniques, allowing for customization through filters based on key design aspects such as dimensionality, edge representation, and node alignment. Detailed information, including direct links to the original publications, is readily accessible upon request. Treevis.net has evolved into a collaborative initiative, with contributions from researchers who submit preprints of their latest tree visualization methodologies or suggest additional relevant details to enrich the database.

Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.

In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-treepsilas search performance significantly.We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-treepsilas search performance.

Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.

In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.

This paper presents a proposal for the use of Spatial Augmented Reality as a tool for 3D data visualizations. The use of traditional display technologies such as LCD monitors provides a fish tank metaphor for the user, i.e. the information is behind a plane of glass. The use of VR technologies allows the user to be immersed in the 3D volume and remove this fish tank problem, but can limit the set of techniques that allow users to interact with spatial data. Spatial Augmented Reality employed in conjunction with a highresolution monitor provides an elegant blend of spatial reasoning, tangible interaction, and detailed information viewing. This paper purposes a range of usages for SAR in 3D visualizations.

We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.

The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness. CR Categories: C.2.0 [Computer-Communication Networks]: General—Security and Protection C.2.3 [ComputerCommunication Networks]: Network Operations—Network Monitoring H.5.2 [Information Systems]: Information Interfaces and Presentation—User Interfaces

(Abstract) Counting the number of paths in a graph, for example the number of nonintersecting (or self-avoiding) rook paths joining opposite corners of an n × n grid, is not an easy problem since no mathematical formula is found and the number becomes too large to enumerate one by one except for small graphs. We have implemented software based on the ZDD technique in Knuth's " The Art of Computer Programming " carefully so as to achieve better memory efficiency, and have succeeded in computing the exact numbers for some graphs that were not known until now.

The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one’s interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.

Parallel coordinate plots (PCPs) are a well-known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a spline-based drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.