Question: Experiments with ad hoc retrieval utilizing WordNet and thesauri constructed automatically.

This paper investigates the efficacy of an automated technique for constructing global thesauri, initially proposed by Crouch in [1] and [2], which relies on clustering the document collection. The methodology's legitimacy is affirmed through a series of experiments, demonstrating that the use of thesauri created through this process significantly enhances retrieval effectiveness across four distinct test collections. Notably, the term discrimination value theory, employed in the thesaurus generation algorithm to categorize terms into specific classes, is found to be ineffective in discerning between "good" and "indifferent" or "poor" thesaurus classes. The authors conclude by proposing an alternative method for automatic thesaurus construction that significantly reduces the complexity of generating viable thesaurus classes. The experimental outcomes indicate that, in some instances, the thesauri produced by this alternative approach are equally effective in retrieval tasks, but at a considerably lower cost. This underscores the potential of simplified automatic thesaurus construction techniques to improve information retrieval systems' performance without the need for extensive manual intervention or resource allocation.

Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/.

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling.

Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality — documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation.

Vector space word representations are useful for many natural language processing applications. The diversity of techniques for computing vector representations and the large number of evaluation benchmarks makes reliable comparison a tedious task both for researchers developing new vector space models and for those wishing to use them. We present a website and suite of offline tools that that facilitate evaluation of word vectors on standard lexical semantics benchmarks and permit exchange and archival by users who wish to find good vectors for their applications. The system is accessible at: www.wordvectors.org.

This paper presents a novel approach to the problem of paraphrase identification. Although paraphrases often make use of synonymous or near synonymous terms, many previous approaches have either ignored or made limited use of information about similarities between word meanings. We present an algorithm for paraphrase identification which makes extensive use of word similarity information derived from WordNet (Fellbaum, 1998). The approach is evaluated using the Microsoft Research Paraphrase Corpus (Dolan et al., 2004), a standard resource for this task, and found to outperform previously published methods.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.

In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.

Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigid symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like natural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.

Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.


Question: Experiments with ad hoc retrieval using WordNet and thesauri constructed automatically.

Over the last two decades, empirical research has consistently demonstrated that text indexing systems, which employ single-term indexing with sophisticated weighting mechanisms, outperform alternative complex text representation methods in retrieval effectiveness. The success of these systems is contingent upon the implementation of potent term-weighting strategies. This paper offers a comprehensive review of advancements in automatic term weighting and establishes baseline models for single-term indexing. These models serve as a reference point for evaluating the performance of more intricate content analysis techniques.

1. AUTOMATED TEXT ANALYSIS

In the late 1950s, Luhn [1] proposed the concept of developing automated text retrieval systems that operate by comparing content identifiers derived from both stored documents and user queries. Typically, these systems extract specific words from the text to serve as content identifiers. Alternatively, content representations can be manually selected by expert indexers who are knowledgeable about the subject matter and the contents of the document collection. Regardless of the method, documents are represented as term vectors, such as:

D = (ti, tj, ..., tp) (1)

where each tk signifies a term associated with a particular document D. Similarly, user queries are represented either as term vectors or as Boolean expressions. A query Q might be structured as:

Q = (qa, qb, ..., qr) (2)

This systematic approach to text analysis and retrieval has proven to be highly effective, particularly when coupled with advanced term-weighting techniques that can accurately reflect the relevance of terms within a document. The paper discusses the evolution of these techniques and their significance in enhancing retrieval performance.

A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures.

Filling the gap between natural language expressions and ontology concepts or properties is the new trend in Semantic Web. Ontology lexicalization introduces a new layer of lexical information for ontology properties and concepts. We propose a method based on unsupervised learning for the extraction of the potential lexical expressions of DBpedia propertiesfrom Wikipedia text corpus. It is a resource-driven approach that comprises three main steps. The first step consists of the extraction of DBpedia triples for the aimed property followed by the extraction of Wikipedia articles describing the resources from these triples. In the second step, sentences mostly related to the property are extracted from the articles and they are analyzed with a Semantic Role Labeler resulting in a set of SRL annotated trees. In the last step, clusters of expressions are built using spectral clustering based on the distances between the SRL trees. The clusters with the least variance are considered to be relevant for the lexical expressions of the property.

We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them shared a word similarity feature that combined LSA word similarity and WordNet knowledge. The first, which achieved the best mean score of the 89 submitted runs, used a simple term alignment algorithm augmented with penalty terms. The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features.

Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the initial query retrieves only little relevant information when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Luhn [23], we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model.
 Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models in- sensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.

People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.

This paper presents the Multiword Expression Toolkit (mwetoolkit), an environment for type and language-independent MWE identification from corpora. The mwetoolkit provides a targeted list of MWE candidates, extracted and filtered according to a number of user-defined criteria and a set of standard statistical association measures. For generating corpus counts, the toolkit provides both a corpus indexation facility and a tool for integration with web search engines, while for evaluation, it provides validation and annotation facilities. The mwetoolkit also allows easy integration with a machine learning tool for the creation and application of supervised MWE extraction models if annotated data is available. In our experiments, the mwetoolkit was tested and evaluated in the context of MWE extraction in the biomedical domain. Our preliminary results show that the toolkit performs better than other approaches, especially concerning recall. Moreover, this first version can be extended in several ways in order to improve the quality of the results.

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.

Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.

Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, questionanswer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-ofthe-art techniques.

Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language model approaches to retrieval, however, produces unstable performance and limited improvements. In this paper, we formally discuss three intrinsic problems of the original PV model that restrict its performance in retrieval tasks. We also describe modifications to the model that make it more suitable for the IR task, and show their impact through experiments and case studies. The three issues we address are (1) the unregulated training process of PV is vulnerable to short document over-fitting that produces length bias in the final retrieval model; (2) the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words; and (3) the lack of word-context information makes PV unable to capture word substitution relationships.


Question: A review of methods for tracking underwater acoustic targets.

This paper delves into the intricacies of establishing mobile underwater wireless sensor networks (UWSNs) for aquatic research, highlighting a unique networking concept. Contrary to terrestrial wireless sensor networks, mobile UWSNs exhibit distinct traits such as reduced communication bandwidth, extensive propagation latency, dynamic node movement, and elevated error rates. These characteristics introduce multifaceted challenges necessitating innovative technological advancements. This article adopts a top-down methodology to scrutinize the design complexities within mobile UWSNs. We systematically investigate the layered protocol stack, beginning with the upper application layer and descending to the foundational physical layer. At every level, we encounter a series of novel design intricacies. Ultimately, we conclude that the development of scalable mobile UWSNs demands a collaborative approach integrating expertise in acoustic communications, signal processing, and network protocol design for mobile aquatic environments.

In this paper we present methods for the design of planar frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) arrays with an emphasis on the problem of moving targets in time-division multiple-access (TDMA) systems. We discuss the influence of target motion and boundaries of operation and present a method to compensate for its effects, which requires special attention in the array design and in signal processing. Array design techniques, examples including an implementation, and measurement results are also covered in this article.

Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.

We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.

The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system’s dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced.

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association

Wireless optical communication can be presented as an alternative to acoustic modems for scenarios where high speed, moderate distances, lower power and less complex communication systems are desired. Wireless communication is much more feasible solution to the problem of communicating with robotic vehicles. In this paper, wireless optical communication for the underwater criteria is designed and analysed. The focus of the paper is to construct light emitting diode based links at low cost with faster data rate of 1Mbps, wirelessly under the water. The choice of using LEDs instead of Lasers was largely economic. However, the underwater environment can be very challenging optically and many of the advantages that lasers have in terms of beam quality can be rapidly degraded by scattering and turbulence. Keyword: Moderate distance, Wireless optical, Super bright blue LED.

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

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

This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.


Question: A review of methods for tracking underwater acoustic targets.

This paper delves into the intricacies of media access control within the unique environment of sensor networks, a domain significantly divergent from traditional mobile computing paradigms due to stringent limitations in processing capabilities, storage, energy resources, and radio technology. Unlike conventional approaches, media access control in sensor networks must prioritize energy efficiency while ensuring equitable bandwidth distribution to the network's infrastructure, catering to all nodes in a multihop topology. To address these dual objectives, we introduce a dynamic rate control strategy. Our empirical findings reveal that this method excels in promoting fairness among nodes while maintaining energy efficiency, demonstrating its efficacy across a broad spectrum of network traffic densities, from low to high duty cycles.

**Question**: The Impact of Renewable Energy Sources on the Stability of Power Grids

**Documents**: An intelligent algorithm for power system stabilizer tuning
This paper presents a new and effective algorithm for the design of power system stabilizers (PSSs) using a combination of genetic algorithm (GA) and particle swarm optimization (PSO). The proposed algorithm, called the Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), is used to optimize the parameters of the PSSs in order to enhance the stability of the power system. The HGAPSO algorithm is applied to a single-machine infinite-bus (SMIB) system and the results are compared with those obtained using GA and PSO separately. The results show that the proposed algorithm is able to achieve better stability performance than the traditional GA and PSO methods.

**Similar Documents**: This study introduces an innovative algorithm for the optimization of power system stabilizers (PSSs) in electrical grids, utilizing a fusion of genetic algorithm (GA) and particle swarm optimization (PSO) techniques. The Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) is deployed to fine-tune PSS parameters, aiming to bolster the stability of power systems. The efficacy of HGAPSO is demonstrated through its application in a single-machine infinite-bus (SMIB) system, with comparative analyses revealing its superiority in enhancing stability performance over stand-alone GA and PSO methods. The outcomes highlight the potential of HGAPSO in achieving superior stability in power system operations.

Using range and Doppler information to produce radar images is a technique used in such diverse fields as air-to-ground imaging of objects, terrain, and oceans and ground-to-air imaging of aircraft, space objects, and planets. A review of the range-Doppler technique is presented along with a description of radar imaging forms including details of data acquisition and processing techniques.

This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired

The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.

A path-tracking criterion for the so-called LHD (load-haul-dump) truck used in underground mining is proposed in this paper. It exploits the particular configuration of this vehicle, composed of two units connected by an actuated articulation. The task is to follow the path represented by the middle of the tunnel, maintaining the whole vehicle at a reduced distance from the path itself, to decrease the risk of crashes against the walls of the tunnel. This is accomplished via feedback through the synthesis of an appropriate path-tracking criterion. The criterion is based on monitoring the distances of the midpoints of both axles of the vehicle from their orthogonal projections on the path, using two different moving frames simultaneously. Local asymptotic stability to paths of constant curvature is achieved by means of linear-state feedback. KEY WORDS—mobile robots, articulated vehicles, mining trucks, path tracking, off-tracking, feedback stabilization

Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor is necessary, because of the typical limitations of single camera systems, resulting from the occlusion effect of other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras is done using a tracking method. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system provides new derived and consolidated information about traffic participants. Thus, this approach is also beneficial for a description of individual traffic participants.

In this demo, we present LLAP, a hand tracking system that uses ultrasound to localize the hand of the user to enable device-free gesture inputs. LLAP utilizes speakers and microphones on Commercial-Off-The-Shelf (COTS) mobile devices to play and record sound waves that are inaudible to humans. By measuring the phase of the sound signal reflected by the hands or fingers of the user, we can accurately measure the gesture movements. With a single pair of speaker/microphone, LLAP can track hand movement with accuracy of 3.5 mm. For devices with two microphones, LLAP enables drawing-in-the air capability with tracking accuracy of 4.6 mm. Moreover, the latency for LLAP is smaller than 15 ms for both the Android and the iOS platforms so that LLAP can be used for real-time applications.


Question: A survey of underwater acoustic target tracking methods.

This paper explores the critical factors influencing effective communication in underwater acoustic sensor networks, a technology essential for oceanographic research, environmental surveillance, and resource exploration. The deployment of Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), laden with sensors, is pivotal for the exploration of marine resources and the collection of scientific data in collaborative monitoring operations. Underwater acoustic networking facilitates these endeavors by connecting a dynamic array of sensors and vehicles tasked with monitoring specific areas. Herein, we delve into the foundational elements of underwater acoustic communications, examining various network architectures suited for both two-dimensional and three-dimensional underwater sensor networks. We also provide an in-depth analysis of the underwater channel's characteristics and the formidable challenges it presents to network efficiency across all protocol stack layers. Additionally, the paper identifies open research questions and suggests potential avenues for addressing these challenges in underwater acoustic communication networks.

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.

Body area networks (BANs) promise to enable revolutionary biomedical applications by wirelessly interconnecting devices implanted or worn by humans. However, BAN wireless communications based on radio-frequency (RF) electromagnetic waves suffer from poor propagation of signals in body tissues, which leads to high levels of attenuation. In addition, in-body transmissions are constrained to be low-power to prevent overheating of tissues and consequent death of cells. To address the limitations of RF propagation in the human body, we propose a paradigm shift by exploring the use of ultrasonic waves as the physical medium to wirelessly interconnect in-body implanted devices. Acoustic waves are the transmission technology of choice for underwater communications, since they are known to propagate better than their RF counterpart in media composed mainly of water. Similarly, we envision that ultrasound (e.g., acoustic waves at non-audible frequencies) will provide support for communications in the human body, which is composed for 65% of water. In this paper, we first assess the feasibility of using ultrasonic communications in intra-body BANs, i.e., in-body networks where the devices are biomedical sensors that communicate with an actuator/gateway device located inside the body. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, bandwidth, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack.

RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.

The aim of the radar systems is to collect information about their surroundings. In many scenarios besides static targets there are numerous moving objects with very different characteristics, such as extent, movement behavior or micro-Doppler spread. It would be most desirable to have algorithms that extract all information on static and moving object automatically, without a system operator. In this paper, we present measurements conducted with a commercially available high-resolution multi-channel linear frequency-modulated continuous-wave radar and algorithms that do not only produce radar images but a description of the scenario on a higher level. After conventional spectrum estimation and thresholding, we present a clustering stage that combines individual detections and generates representations of each target individually. This stage is followed by a Kalman filter based multi-target tracking block. The tracker allows us to follow each target and collect its properties over time. With this method of jointly estimating tracks and characteristics of each individual target in a scenario, inputs for classifiers can be generated. Which, in turn, will be able to generate information that could be used for driver assistance or alarm trigger systems.

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.

A method is described which quantifies the speed and direction of several moving objects in a sequence of digital images. A relationship between the time variation of intensity, the spatial gradient, and velocity has been developed which allows the determination of motion using clustering techniques. This paper describes these relationships, the clustering technique, and provides examples of the technique on real images containing several moving objects.

This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.

Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.

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

OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods.


Question: A review of methods for tracking underwater acoustic targets.

This document introduces the WHOI micro-modem, a sophisticated underwater acoustic communications and navigation subsystem designed for use with multiple platforms. This compact and energy-efficient device is capable of executing low-rate frequency-hopping frequency-shift keying (FH-FSK), variable rate phase-coherent keying (PSK), as well as two distinct forms of long baseline navigation: narrow-band and broadband. The micro-modem offers transmission in four frequency bands ranging from 3 to 30 kHz, with a larger board required for the lowest frequency range. Adhering to the NMEA standard, a widely recognized serial port specification, the user interface ensures compatibility and ease of use. Additionally, the modem incorporates a basic networking feature that facilitates communication among up to 16 units in either polled or random-access modes. It also includes an acknowledgment mechanism that ensures reliable delivery of data. The paper provides an exhaustive overview of the system, complemented by the presentation of test results from various trials.

**Question**: A Comparative Study of Deep Learning Models for Stock Market Prediction

**Documents**: Deep Learning for Stock Market Prediction: A Survey
In recent years, deep learning has gained significant attention in various fields due to its ability to automatically learn complex features from raw data. This paper presents a comprehensive survey on the application of deep learning for stock market prediction. The paper first provides an overview of deep learning models, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. Then, the paper reviews the literature on deep learning for stock market prediction, covering various types of models, data sources, and evaluation metrics. The paper also discusses the challenges and limitations of using deep learning for stock market prediction, such as the non-stationary and noisy nature of financial data. Finally, the paper suggests some potential research directions for future work in this field.

**Similar Documents**: This document offers a thorough review of the utilization of deep learning techniques for stock market forecasting, a domain that has attracted considerable interest in recent times due to deep learning's proficiency in autonomously learning intricate patterns from raw data. The paper begins by outlining various deep learning models, encompassing feedforward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. Subsequently, it delves into the existing literature on applying deep learning to stock market prediction, encompassing a wide array of models, data sources, and performance evaluation metrics. The paper also addresses the obstacles and constraints associated with employing deep learning for financial market prediction, notably the inherently non-stationary and

This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.

A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.

Ultra-wideband (UWB), short-pulse (SP) radar is investigated theoretically and experimentally for the detection and identification of targets buried in and placed atop soil. The calculations are performed using a rigorous, three-dimensional (3-D) Method of Moments algorithm for perfectly conducting bodies of revolution. Particular targets investigated theoretically include anti-personnel mines, anti-tank mines, and a 55-gallon drum, for which we model the time-domain scattered fields and the buried-target late-time resonant frequencies. With regard to the latter, the computed resonant frequencies are utilized to assess the feasibility of resonance-based buried-target identification for this class of targets. The measurements are performed using a novel UWB, SP synthetic aperture radar (SAR) implemented on a mobile boom. Experimental and theoretical results are compared.

Automotive radar sensors will be used in many future applications to increase comfort and safety. Adaptive cruise control (ACC), parking aid, collision warning or avoidance and pre crash are some automotive applications, which are based on high performance radar sensors. This paper describes results gained by an European Commission funded research project and a new radar network based on four individual monostatic 77 GHz FMCW radar sensors. Each sensor measures target range and velocity simultaneously and extremely accurate even in multiple target situations with a maximum range of 40 m. The azimuth angle is estimated by multilateration techniques in a radar network architecture. The system design and the signal processing of this network are described in detail in this paper. Due to the high range resolution performance of each radar sensor ordinary targets cannot be considered any longer as point but as extended targets. System capability, performance and reliability can be improved essentially if the single sensor FMCW signal processing, the target position processing in the radar network and the tracking scheme are jointly optimized.

In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.

The continuing progress of Moore's law has enabled the development of radar systems that simultaneously transmit and receive multiple coded waveforms from multiple phase centers and to process them in ways that have been unavailable in the past. The signals available for processing from these multiple-input multiple-output (MIMO) radar systems appear as spatial samples corresponding to the convolution of the transmit and receive aperture phase centers. The samples provide the ability to excite and measure the channel that consists of the transmit/receive propagation paths, the target and incidental scattering or clutter. These signals may be processed and combined to form an adaptive coherent transmit beam, or to search an extended area with high resolution in a single dwell. Adaptively combining the received data provides the effect of adaptively controlling the transmit beamshape and the spatial extent provides improved track-while-scan accuracy. This paper describes the theory behind the improved surveillance radar performance and illustrates this with measurements from experimental MIMO radars.

We perform target detection in B-scan images obtained using stepped frequency continuous wave (SFCW) ground-penetrating radar (GPR) simulation with Matlab and the open-source software for electromagnetic propagation simulation gprMax and using experimental data from measurements available on the Web. We perform hyperbola detection and target depth estimation using the Hough transform. Additionally, we reduce the complexity of Hough transform using the Column-Connection Clustering (C3) algorithm. The results show that gprMax can be used with SFCW modulated signals, besides its use with conventional impulse signals (impulse GPR). The proposed combination of C3 and Hough transform shows very good detection capability. These algorithms are also tested on experimental GPR data available on the Web to show their applicability in real soil conditions. Mean subtraction method and Singular Value Decomposition (SVD) algorithms are used to reduce the effects of the ground reflected wave.

In this letter, we address the autonomous flight of a small quadrotor, enabling tracking of a moving object. The 15-cm diameter, 250-g robot relies only on onboard sensors (a single camera and an inertial measurement unit) and computers, and can detect, localize, and track moving objects. Our key contributions include the relative pose estimate of a spherical target as well as the planning algorithm, which considers the dynamics of the underactuated robot, the actuator limitations, and the field of view constraints. We show simulation and experimental results to demonstrate feasibility and performance, as well as robustness to abrupt variations in target motion.

This paper presents the implementation and assessment of a direction finding proximity fuze sensor for anti-aircrafts or anti-air missiles. A higher rejection of clutter signals is achieved by employing a binary phase shift keying modulation using Legendre sequence. The direction finding is implemented by comparing received powers from six receiving antennas equally spaced by an angle of 60° around a cylindrical surface. In addition, target detection algorithms have been developed for a robust detection of the target, taking the wide variation of target related parameters into considerations. The performances of the developed fuze sensor are experimentally verified by constructing the fuze-specific encounter simulation test apparatus, which collects and analyzes reflected signals from a standard target. The developed fuze sensor can successfully detect the -10 dBsm target over a 10 m range as well as the direction with an out-of-range rejection of about 40 dB. Furthermore, the developed fuze sensor can clearly detect the target with mesh clutter environment. To assess realistic operation, the fuze sensor is tested using 155 mm gun firing test setup. Through the gun firing test, the successful fuzing range and direction finding performances are demonstrated.

Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.


Question: A survey of underwater acoustic target tracking methods.

This paper provides an in-depth review of the advancements in underwater acoustic telemetry since 1982, structured around six key research domains: (1) the physics of underwater channels, simulations, and empirical measurements; (2) the architecture of receivers; (3) the utilization of diversity; (4) error control coding techniques; (5) systems networked for communication; and (6) alternative modulation methods. The paper discusses the progress made in each domain and offers insights into the future obstacles that researchers will have to overcome. A central argument of this review is that for underwater modems to enhance their performance envelope—measured in terms of range, data rate, and channel complexity—there is a critical need for the seamless integration of high-fidelity channel models into ongoing telemetry research.

This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.

Acoustic sensors or sensor networks can be efficiently used to spot and observe targets of interest and have the ability to conclude the exact location of an object or event within the sensor network's coverage range. For the detection and spot estimation of shooter, the schemes of gunshot localization require one or more sensing technologies. In current situation the importance of counter sniper systems cannot be denied. An efficient counter sniper system that can provide immediate shooter location will provide many benefits to our security personnel. In this paper, the Time Difference Of Arrival (TDOA) of Shock Wave and Muzzle Blast is used to estimate the shooter location. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The result shows that the scheme worked very fine and is capable of locating sniper fires very efficiently, competently and effectively. The designed algorithm worked very well and results were found to be very accurately.

For application in a short range ground based surveillance radar a combination between frequency modulated continuous wave (FMCW) transmit signals and a receive antenna array system is considered in this paper. The target echo signal is directly down converted by the instantaneous transmit frequency. The target range R will be estimated based on the measured frequency shift fB between transmit and receive signal. Due to an extremely short chirp duration Tchirp, the target radial velocity υτ has only a very small influence to the measured frequency shift fB. Therefore the radial velocity υτ will not be measured inside a single FMCW chirp but in a sequence of chirp signals and inside each individual range gate. Finally, the target azimuth angle is calculated utilizing the receive antenna array and applying a digital beamforming scheme. Furthermore, in order to unambiguously measure even high radial velocities, a variable chirp duration is proposed on a dwell to dwell basis.

Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany †Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany ‡Data Fusion Algorithms & Software, EADS Deutschland GmbH, Ulm, Germany marcus.baum@kit.edu, {michael.feldmann |wolfgang.koch}@fkie.fraunhofer.de dietrich.fraenken@eads.com, uwe.hanebeck@ieee.org

Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan racingpsilas autonomous vehicle for the urban grand challenge. Results are presented and discussed.

The environment perception plays a significantly role in intelligent vehicles and the advanced driver assistance system (ADAS), which enhances the driving safety and convenience. Target tracking is one of the key technologies of environment perception. The on-board sensors such as cameras and radar are commonly used for target tracking while they have limitations in terms of detection range and angle of view. One way to overcome the perception limitations of on-board ranging sensors by incorporating the vehicle-to-vehicle (V2V) communication. This paper proposes a vehicle tracking system which fuse the radar and V2V information to improve the target tracking accuracy. The proposed system integrates the radar, GPS and DSRC communication equipment. The GPS and radar are utilized to obtain its own position information and the position information of nearby vehicles. The proposed system also resolves the problem of data association in multiple target tracking measurements by other connected vehicles' identity information. With the association measurements, a Kalman filter is used to improve the accuracy of target tracking. An assessment of tracking system in real road environment shows that the proposed fusion approach for target tracking can reduce the data association error and improve the vehicle target tracking accuracy.

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired

Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.

For the Oxford Aerial Tracking System (OATS) we are developing a robot helicopter that can track moving ground objects. Here we describe algorithms for the device to perform path planning and trajectory prediction. The path planner uses superquadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to avoid local minima traps. The goal of the trajectory prediction system is to reliably predict the target trajectory during occlusion caused by ground obstacles. For this we use two artificial neural networks in parallel whose retraining is automatically triggered if major changes in the target behaviour pattern are detected. Simulation results for both systems are presented.