Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Directed Sensor Fusion and Planning: A Computational Approach

1 Introduction. - 1.1 A Model for Information Gathering. - 1.2 A Strategy for Realizing Information Gathering. - 1.3 Organizations for Information Gathering. - 1.4 An Overview of this Book. - 1.5 Literature. - 2 Modeling Sensors. - 2.1 Modeling Sensing Geometry. - 2.2 Modeling Sensor Observation Uncertainty. - 2.3 Additional Modeling Considerations. - 2.4 An Example System. - 2.5 Discussion. - 2.6 Literature. - 3 Task Modeling and Decision Making. - 3.1 Task Modeling. - 3.2 Decision Theory. - 3.3 Discussion. - 3.4 Literature. - 4 Mean-Square Estimation. - 4.1 Derivation of Mean Square Estimation Techniques. - 4.2 Robustness to System Variation. - 4.3 Robust Rules for Nonlinear Systems. - 4.4 Additional Comments On Moment-Based Representations. - 4.5 Discussion. - 4.6 Literature. - 5 Grid-Based Probability Density Methods. - 5.1 Grid-Based Probability Density Updating. - 5.2 Estimation and Payoff Computation. - 5.3 Robustness. - 5.4 Error Analysis. - 5.5 Simulation Evaluation. - 5.6 Extensions. - 5.7 Discussion. - 5.8 Literature. - 6 Choosing Viewpoints and Features. - 6.1 Describing the Sensor Action Space. - 6.2 Implementing Sensor Planning. - 6.3 Simulation Analysis of Sensor Planning. - 6.4 Discussion and Extensions. - 6.5 Literature. - 7 Towards a Task-Level Programming Environment. - 7.1 Sensor Fusion. - 7.2 Task Specification. - 7.3 Observation Planning. - 7.4 Summary and Future Development. - 7.5 Literature. - 8 An Experimental System. - 8.1 Implementation Description. - 8.2 Experimental Results. - 8.3 Discussion. - 9 Future Extensions. - 9.1 System Organization. - 9.2 Information Gathering with Multiple Sensors. - 9.3 The Model Selection Problem. - 9.4 Sensor Fusion and Artificial Intelligence. - 9.5 Summary. - A Review of Probability. - A.1 Basic Probability. - A.2 Conditional Probability. - A.3 Expectations. - A.4 Transforming Probability. - A.5 Convergence. - B Review of Methods for Estimation. - B.1 Stochastic Approximation. - B.2 Least Squares Methods. - B.3 Maximum Likelihood Method. - B.4 Maximum A Posteriori Probability. - B.5 Decision Theory. - B.6 Game Theory. - B.7 ? -maximin Game Theory. - C System Hardware. - References. - Glossary of Mathematical Notation. - Glossary of Symbols.
A finite-state morphological analysis of Tswana nouns

This article describes the progress made in developing a broad-coverage computational morphological analyser for Tswana, an agglutinative disjunctively written indigenous South African language. Two central questions are addressed, viz. what is essential in order to build a broad-coverage computational morphological analyser/generator and how do we go about building this software application? In answer to the first question, a precise linguistic description is required of the linguistic phenomena mechanisms governing the morphology of the relevant natural language, in this case Tswana, the accurate modelling and implementation of these morphological phenomena and, ideally speaking, a complete list of all and only the valid roots and stems in Tswana. The answer to the second question lies within the realm of the discipline of software engineering and its methodologies. The article discusses the development of a morphological analyser with respect to the appropriate life cycle model, tools and testing procedures; Tswana noun morphology, in particular, the relevant morphotactics and morphophonological alternation rules; their modelling and implementation: preliminary results; testing procedures and future work.
Vowel Recognition Based on Surface Electromyography with Electrode Grid on Submental Region

This paper proposes the use of electrode grid for Japanese vowel recognition based on surface electromyography (sEMG). Previous studies have indicated the potential effectiveness of sEMG-based speech recognition, not only for healthy people, but also for dysarthric patients. In these studies, however, disc electrodes or parallel bar electrodes were used and located empirically, although there exist relatively small muscles in proximity to each other in the face or neck region. In order to avoid missing out information about speech, we examined the effectiveness of using an electrode grid, which consists of densely-spaced multielectrodes. In our experiments, we measured sEMG signals from the submental region with the electrode grid during the production of 5 vowel sounds. Continuous hidden Markov models were applied to the sEMG signals for vowel recognition. We compared the recognition accuracies between the two methods : One was based on signals from all channels and the other was based on virtually reconstructed single bipolar signal. The former achieved considerably higher recognition accuracy than the latter. This result indicates that using electrode grid is more effective in extracting information for sEMG-based speech recognition.
Quadratic classifier in nonstationary pattern recognition systems and its application to robust AR speech analysis

This paper is dedicated to an extensive comparative experimental analysis of nonstationary pattern recognition methods based on the quadratic classifier: the iterative quadratic classifications, its real-time modification, and the quadratic classifier with sliding training data set. The main purpose of this analysis lies in the choice of the nonstationary pattern recognition method based on quadratic classifier which gives the best results referred to the following criteria: a classification error, an adaptiveness in tracking of the nonstationary signals, and a sensitivity to the length of learning data set. The comparative analysis is done through analyzing the natural speech, isolated spoken Serbian vowels and digits, which are used as the examples of nonstationary signal.
Helicopter adaptive flight control using neural networks

This paper summarizes the theoretical development and numerical investigation of a direct adaptive tracking control architecture using neural networks. Neural networks are proposed for use in combination with nonlinear controllers designed based on feedback linearization. Neural networks capable of online learning are required to compensate for inversion error which may arise from imperfect modeling, approximate inversion or sudden and unanticipated changes in dynamics. A stable weights adjustment rule for the online neural network is presented. Under mild assumptions on the nonlinearities representing the inversion error, the adaptation algorithm assures that all the signals in the loop are uniformly bounded and that the weights of the online neural network tend to constant values. Simulation results using an AH-64 6DOF simulation model are presented to illustrate the performance of the online neural network based adaptation algorithm. <<ETX>>
Scalable planning under uncertainty

Autonomous agents that act in the real-world can often improve their success by capturing the uncertainty that arises because of their imperfect knowledge and potentially faulty actions. By making plans robust to uncertainty, agents can be prepared to counteract plan failure or act upon information that becomes available during plan execution. Such robust plans are valuable, but are often difficult to construct when uncertainty is high and goal achievement requires many actions. Unfortunately, current approaches to planning under uncertainty are not scalable: they focus on small problems where the challenge is finding an optimal solution. I study (non-deterministic and probabilistic) conformant and conditional planning in large problems where just finding a feasible solution is the challenge. I develop scalable heuristic search techniques that are inspired both by optimal approaches to planning under uncertainty (based on Markov decision processes) and scalable approaches for classical planning (based on planning graph reachability heuristics). Specifically, I develop measures for the cost of completing partial plans, heuristics that estimate the measures, efficient data-structures and inference techniques to compute the heuristics, and a multi-objective heuristic search algorithm that uses the heuristics. Through extensive empirical evaluation, I show the resulting set of techniques significantly improves the scalability of planning under uncertainty.
Bias Modeling for Distantly Supervised Relation Extraction

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1) bias-dist to model the relative distance between points (instances) and classes (relation centers); (2) bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.
A Robust Digital Image Watermarking Technique Based on DWT and Fastica

With the increasing use of internet and effortless copying, copyright protection for multimedia data has become an important issue. Digital watermarking has emerged as a tool for protecting the multimedia data from copyright infringement. Many literatures have reported about frequency transform based watermarking techniques for data security, since they are robust against image processing attacks. In this paper, an attempt is made to develop a watermarking scheme based on discrete wavelet transform (DWT) and extraction using fast independent component analysis (FastICA) algorithm. A perceptual model is applied with stochastic approach for watermark embedding. This is based on computation of noise visibility function (NVF) which has local image properties where the strength of watermarking is controlled. ICA, an intelligent detection algorithm, which is based on blind source separation technique, is implemented for extraction without the use of previous knowledge of the original image. The features of FastICA are quick convergence, easy to implement and suitable for watermark applications. Robustness of the proposed scheme is validated by implementing common image processing attacks. Peak signal to noise ratio (PSNR) and similarity measure (SM) values are calculated to evaluate the performance of the proposed work.
Mathematical Foundation of A Semantic Directed Compiler Generator

This paper describes technical details which were not included in the paper ''Control Flow Treatment in a Simple Semantics-Directed Compiler Generator'', presented at the IFIP working conference on Formal Description of Programming Concepts, Germany, 1982, due to lack of space. The present paper cannot be read separately. We describe our method in full detail, including the iterate -operator in the S-algebra. Furthermore we give complete formal descriptions of the semantic algebra S and its models (Section 1), the compiler generation function d^c and the compile time interpretation J (Section 2), the target language semantics (Section 3), and in Section 4 the correctness proof.
Collaborative Digital Library Services in a Cloud

We have developed a web-based system that allows users to collaboratively organize large online image collections according to an evolving faceted classification schema. One of the major issues identified in the early deployment and evaluation in a university setting is the scalability of the system on traditional server implementations. Traditional computing cannot support ever-increasing number of users, documents, schema objects, schema history, and automated classification processes without difficult, expensive and time consuming resource reconfiguration. To address this problem we are proposing to move our system on a cloud-based Microsoft Windows Azure platform exposing it to users as a collaborative cloud service. Cloud computing will enable the Facet System to scale virtually unlimited. In this paper, we describe the architectural design for deploying our facet-based system and early prototypical modules as we ll as the work in progress implementing it on the Windows Azure platform. Keywords-faceted digital library; windows azure; cloud computing
Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation system

An evolutionary autonomous navigation system is described that evolves two basic, conflicting behaviors, namely, obstacle avoidance and target seeking, as the system acquires skill to coordinate them (behavior emergence and coordination skill acquisition happen simultaneously). Simulation experiments show promising results: the number of target captures increases and the number of collisions stabilizes, as generations proceed. They confirm the evolutionary learning capacity of the reactive navigation system proposed.
Low-latency network coding for streaming video multicast

Network coding has been successfully employed to increase throughput for data transfers. However, coding inherently introduces packet inter-dependencies and adds decoding delays which increase latency. This makes it difficult to apply network coding to real-time video streaming where packets have tight arrival deadlines. This thesis presents FLOSS, a wireless protocol for streaming video multicast. At the core of FLOSS is a novel network code. This code maximizes the decoding opportunities at every receiver, and at the same time minimizes redundancy and decoding latency. Instead of sending packets plainly to a single receiver, a sender mixes in packets that are immediately beneficial to other receivers. This simple technique not only allows us to achieve the coding benefits of increased throughput, it also decreases delivery latency, unlike other network coding approaches. FLOSS performs coding over a rolling window of packets from a video flow, and determines with feedback the optimal set of packet transmissions needed to get video across in a timely and reliable manner. A second important characteristic of FLOSS is its ability to perform both interand intra-flow network coding at the same time. Our technique extends easily to support multiple video streams, enabling us to effectively and transparently apply network coding and opportunistic routing to video multicast in a wireless mesh. We devise VSSIM*, an improved video quality metric based on [46]. Our metric addresses a significant limitation of prior art and allows us to evaluate video with streaming errors like skipped and repeated frames. We have implemented FLOSS using Click [22]. Through experiments on a 12-node testbed, we demonstrate that our protocol outperforms both a protocol that does not use network coding and one that does so naively. We show that the improvement in video quality comes from increased throughput, decreased latency and opportunistic receptions from our scheme. Thesis Supervisor: Dina Katabi Title: Associate Professor
A method of training multi-layer networks with heaviside characteristics using internal representations

A learning algorithm is presented that uses internal representations, which are continuous random variables, for the training of multilayer networks whose neurons have Heaviside characteristics. This algorithm is an improvement in that it is applicable to networks with any number of layers of variable weights and does not require 'bit flipping' on the internal representations to reduce output error. The algorithm is extended to apply to recurrent networks. Some illustrative results are given. <<ETX>>
An Autonomous Distributed Route Planning Method for Multiple Mobile Robots

Route planning of multiple AGVs(Automated Guided Vehicles) is expected to minimize the transportation time without collision a d deadlock among the AGVs in many transportation systems. In this paper, we propose an autonomous distributed route planning method for multiple mobile robots. The proposed method has a characteristic that each mobile robot individually creates a near optimal route through the repetitive data exchange among the robots and the local search of route using Dijkstra's algorithm. The proposed method is applied to several transportation route planning problems. The optimality of the solution generated by the proposed method is evaluated using the duality gap derived by Lagrangian relaxation method. A near optimal route plan within 5% of duality gap for a large scale transportation system consisting of 143 nodes and 15 AGVs can be obtained with five seconds of computation time by losing Pentium III (1GHz) processor. Moreover, it is shown that the proposed method is effective for various types of problems despite the fact that each route for AGV is created without optimizing the entire objective function even when the velocity of each AGV is different.
A Secure Neuromemristive Primitive to Mitigate Correlation Power Analysis on SHA-3 Hash Function

A Secure Neuromemristive Primitive to Mitigate Correlation Power Analysis on SHA-3 Hash Function James B. Thesing Supervising Professor: Dr. Dhireesha Kudithipudi Passing messages to soldiers on the battle field, conducting online banking, and downloading files on the internet are very different applications that all share one thing in common, concerns over security of the data being processed. Data security depends on the cryptographic systems that take into account both the algorithmic weakness and the weaknesses of the hardware devices they are implemented on. The current dominant hardware design medium is complementary metal-oxide-semi-conductor (CMOS). CMOS has been shown to leak more power as the technology node size decreases. The leaked power has a strong correlation with the bits being manipulated inside a device. These power leakages have brought on a class of power analysis that is able to extract secret information being processed in the algorithm with far less computational power than brute force guessing. Recently, many hardware designs have been proposed which have shown resistance against different forms of power analysis by changing hardware layouts; however, these designs are realized in the same technology, CMOS, that causes the side channel attack problem. There are many emerging technologies that are becoming more practical to implement in conjunction with CMOS. Of these, neuromemristive systems have two characteristics that can be exploited to prevent side channel attacks: low power operation and stochastic behavior. Attacks were conducted on both CMOS and neuromemrisitve based mitigations of the SHA-3 algorithm. In this thesis, digital side channel attack mitigations are created to exploit dual-rail logic. A secure neuromemristive primitive is designed using neural logic blocks that, to the best of our knowledge, have not been considered by others in mitigation of power analysis. Also, an in-depth analysis of power attacks on linear functions compared to typical non-linear attack points is conducted. Metrics such as number of power traces used for the Correlation Power Analysis (CPA), correlation coefficients, confidence ratios, power consumption, and transistor count were used to compare circuit performance. Success rate of guessing a key during SHA-3 operations, while configured as a MAC, was used as a system benchmark. It was found that CMOS is effective in countermeasures when masking linear functions, with the ability to use current standard cells, in ASIC design. If reconfigurable circuits are considered, the neuromemristive circuit had the overall best mitigation strength with almost complete decoupling of input data to power dissipated; moreover, this design offered low power operation and small form factor compared to the original circuit.
An efficient data management method for spatial objects using MD-tree-experimental evaluation and comparisons

An efficient spatial data management method for non-zero size spatial objects, called R(Region)MD-tree, is proposed. In this method, a graphic object in an N-dimensional space is represented as a point in a 2*N dimensional space by the centroid and the extension in each axial direction. The point data are managed by the MD-tree after applying the coordinate transformation. The point location problem or range searching in the original N-dimensional space are carried out by the hyper-rectangular range searching to the point data in the 2*N dimensional space. In this representation and data management, data objects are sorted and classified according to not only the location but also the size of the data. By the simulation experiments, the performances and efficiencies of the RMD-tree are compared with the conventional MD-tree and the R-tree. As the data objects become larger, the RMD-tree is superior in the searching speed by 2 approximately 4 times to the conventional ones, and the performances are almost comparable even if the objects are points or small graphic data. <<ETX>>
Tango introduction and tutorial

Tango is a software-based multiprocessor simulator that can generate traces of synchronization events and data references. The system runs on a uniprocessor and provides a simulated multiprocessor environment. The user code is augmented during compilation to produce a compiled simulation system with optional logging. Tango offers flexible and accurate tracing by allowing the user to incorporate various memory and synchronization models. Tango achieves high efficiency by running compiled user code, by focusing on information that is of specific interest to multiprocessing studies and by allowing the user to select the most efficient memory simulation that is appropriate for a set of experiments.
Source Separation and DOA Estimation for Underdetermined Auditory Scene

In human-machine communication the separation of a target speech signal and localization of it in noisy environments are very important tasks. [1] For carrying out these tasks recent advanced sensor array signal processing is promising technology. [2] It utilizes the collection of multi-channel acoustic data by an array of microphones for detecting and producing output signals which is much more intelligible and suitable for communication and automatic speech recognition. [3]
Contouring blood pool myocardial gated SPECT images with a neural network leader segmentation and a decision-based fuzzy logic

The authors have developed an algorithm to extract ventricular contours in gated single photon emission computed tomography (G-SPECT) images of the blood pool. In this kind of images, the authors have to deal mainly with 3 problems. First of all, there is a superposition of nuclear emission sources within the epicardium making difficult the separation of the 2 myocardial ventricles. Secondly, due to the great variety of ventricular forms, the process is hard to automate and, thus, human participation is required although it is time consuming. Third, the authors have to deal with noise and diffusion resulting from the nature of the technique itself. Their algorithm employs wavelets filters. To overcome the first and third difficulties. A neural network leader type algorithm, combined with a decision-based fuzzy logic system, enables the authors to segment the G-SPECT images and to recognize the left ventricle. This way, the endocardial contour is extracted and the second problem can be solved. Experimental results and a comparison with other methods, among them the one used in the authors' laboratory in clinical routine, show that the performance of the authors' algorithm is very high.