Shashi Phoha

Bio: Shashi Phoha is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Visual sensor network. The author has an hindex of 19, co-authored 135 publications receiving 1480 citations. Previous affiliations of Shashi Phoha include National Institute of Standards and Technology & Louisiana Tech University.

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

Abstract: Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects during the build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning. During the build process, multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera. For each neighborhood in the resulting layerwise image stack, multi-dimensional visual features were extracted and evaluated using binary classification techniques, i.e. a linear support vector machine (SVM). Through binary classification, neighborhoods are then categorized as either a flaw, i.e. an undesirable interruption in the typical structure of the material, or a nominal build condition. Ground truth labels, i.e. the true location of flaws and nominal build areas, which are needed to train the binary classifiers, were obtained from post-build high-resolution 3D CT scan data. In CT scans, discontinuities, e.g. incomplete fusion, porosity, cracks, or inclusions, were identified using automated analysis tools or manual inspection. The xyz locations of the CT data were transferred into the layerwise image domain using an affine transformation, which was estimated using reference points embedded in the part. After the classifier had been properly trained, in situ defect detection accuracies greater than 80% were demonstrated during cross-validation experiments.

Sensor Network Operations

Abstract: PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

Self-organizing sensor networks for integrated target surveillance

Abstract: Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course

Surveillance coverage of sensor networks under a random mobility strategy

Abstract: We consider the problem of surveillance of a region undertaken by a group of mobile sensors. Random mobility strategies are discussed in terms of coverage efficiency, communication and reliability in hostile environments. Under a Brownian motion random mobility strategy for the sensor grid, the distribution of the time-until-detection of slowly moving (point) targets is studied. Both two and three dimensional environments are considered. We obtain explicit formulas in three dimensions and bounds in two.

Symbolic Analysis of Sonar Data for Underwater Target Detection

Abstract: This paper presents a symbolic pattern analysis method for robust feature extraction from sidescan sonar images that are generated from autonomous underwater vehicles (AUVs). The proposed data-driven algorithm, built upon the concepts of symbolic dynamics and automata theory, is used for detection of mines and mine-like objects in the undersea environment. This real-time algorithm is based on symbolization of the data space via coarse graining, i.e., partitioning of the two-dimensional sonar images. The statistical information, in terms of stochastic matrices that serve as features, is extracted from the symbolized images by construction of probabilistic finite state automata. A binary classifier is designed for discrimination of detected objects into mine-like and nonmine-like categories. The pattern analysis algorithm has been validated on sonar images generated in the exploration phase of a mine hunting operation; these data have been provided by the Naval Surface Warfare Center. The algorithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detecting objects on the seabed-bottom.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Underwater acoustic sensor networks: research challenges

Abstract: Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined. � 2005 Published by Elsevier B.V.

Energy conservation in wireless sensor networks: A survey

Abstract: In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.