Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty-two research projects and a list of publications.Sanders, a Lockheed-Martin Corporation Contract BZ4962U.S. Army Research Laboratory Contract DAAL01-96-2-0001U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-96-1-0930National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Science Foundation Graduate Research FellowshipAT&T Bell Laboratories Graduate Research FellowshipU.S. Army Research Laboratory Contract DAAL01-96-2-0002National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000

FAST IMPLEMENTATION TECHNIQUES OF MULTICHANNEL DIGITAL FILTERS FOR COLOR IMAGE PROCESSING USING MATRIX DECOMPOSITIONS

For the processing of color images, multivariable 3-input, 3-output 2-D digital filters are used, considering decomposition in the R, G and B components. Assuming that the three image components are decorrelated, three independent single-input, single-output (SISO) two-dimensional (2-D) digital filters are needed for the processing of each monochromatic image. Additional processing is needed for the correlated noise components in each chan- nel. The requirement of very fast processing dictates the use of special purpose hardware implementations. The VLSI array processors, which are special purpose, locally intercon- nected computing networks, are ideally suited for the fast implementation of digital filters, since they maximize concurrency by exploiting both parallelism and pipelining. In this paper fast implementation architectures of 3-input, 3-output 2-D multi-input digital filters for color image processing that are based on matrix decompositions are presented. The resulting structures are modular, regular, have high inherent parallelism and are easily pipelined, so that they may be implemented via VLSI array processors

Digital Signal Processing

Contains an introduction and reports on fifteen research projects.U.S. Navy - Office of Naval Research (Contract N00O14-81-K-0742)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)National Science Foundation (Grant ECS80-07102)National Science Foundation (Grant ECS84-07285)Amoco Foundation FellowshipSanders Associates, Inc.Advanced Television Research ProgramM.I.T. Vinton Hayes FellowshipHertz Foundation Fellowshi

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Speech Enhancement Guided by Contextual Articulatory Information

Previous studies have confirmed the effectiveness of leveraging articulatory information to attain improved speech enhancement (SE) performance. By augmenting the original acoustic features with the place/manner of articulatory features, the SE process can be guided to consider the articulatory properties of the input speech when performing enhancement. Hence, we believe that the contextual information of articulatory attributes should include useful information and can further benefit SE in different languages. In this study, we propose an SE system that improves its performance through optimizing the contextual articulatory information in enhanced speech for both English and Mandarin. We optimize the contextual articulatory information through joint-train the SE model with an end-to-end automatic speech recognition (E2E ASR) model, predicting the sequence of broad phone classes (BPC) instead of the word sequences. Meanwhile, two training strategies are developed to train the SE system based on the BPC-based ASR: multitask-learning and deep-feature training strategies. Experimental results on the TIMIT and TMHINT dataset confirm that the contextual articulatory information facilitates an SE system in achieving better results than the traditional Acoustic Model(AM). Moreover, in contrast to another SE system that is trained with monophonic ASR, the BPC-based ASR (providing contextual articulatory information) can improve the SE performance more effectively under different signal-to-noise ratios(SNR).Comment: Will be submitted to TASL

Cognitive Information Processing

Contains research objectives and summary of research on five research projects.National Science Foundation (Grant SED74-12653-A01)Joint Services Electronics Program (Contract DAAB07-76-C-1400)National Science Foundation (Grant ENG74-24344)Associated Press (Grant)National Institutes of Health (Grant 1 ROI GM22547-01)National Institutes of Health (Grant 2 PO1 GM19428-04