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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Transformation of Video signal processing Techniques from 2D to 3D: A Survey 2nd

Transformation of Video Signal Processing Techniques from 2D...

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2nd international conference on Communications and Cyber-Physical Engineering (ICCCE)

作者： Koli, Sanjay Shamalik, Rameez Dr DY Patil Sch Engn Pune Maharashtra India Bharati Vidyapeeths Coll Engn Women Pune Maharashtra India

ISBN: (纸本)9789811387159;9789811387142

This paper presents an advanced depth intra-coding approach for 3D video coding based on the High Efficiency Video Coding (HEVC) standard and the multi-view video plus depth (MVD) representation. This paper is motivated by the fact that depth signals have specific characteristics that differ from those of natural signals, i.e., camera-view video. Our approach replaces conventional intra-picture coding for the depth component, targeting a consistent and efficient support of 3D video applications that utilize depth maps or polygon meshes or both, with a high depth coding efficiency in terms of minimal artifacts in rendered views and meshes with a minimal number of triangles for a given bit rate. For this purpose, we introduce intrapicture prediction modes based on geometric primitives along with a residual coding method in the spatial domain, substituting conventional intra-prediction modes and transform coding, respectively. The results show that our solution achieves the same quality of rendered or synthesized views with about the same bit rate asMVDcoding with the 3D video extension of HEVC (3D-HEVC) for high-quality depth maps and with about 8% less overall bit rate as with 3D-HEVC without related depth tools. At the same time, the combination of 3D video with 3D computer graphics content is substantially simplified, as the geometry-based depth intra signals can be represented as a surface mesh with about 85% less triangles, generated directly in the decoding process as an alternative decoder output.

关键词： Video coding 3D video Multi view with depth HEVC

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Deep learning Approach: Detection of Replay Attack in ASV Systems 2nd

Deep Learning Approach: Detection of Replay Attack in ASV Sy...

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Saranya, S. Rupesh Kumar, Suvidha Bharathi, B. Department of CSE SSN College of Engineering Chennai India

ISBN: (纸本)9789811524745

Automatic speaker verification (ASV) system is a bio-metric authentication system, which accepts or rejects speech utterance depending on the voiceprint of speakers. Vulnerability of such system to spoofing attacks is the biggest challenge at hand. Proposed system is an initiative toward improvising the countermeasure for replay attacks on ASV systems. It is developed and tested on ASVspoof2017 corpus. Constant Q cepstral coefficients (CQCCs) feature is extracted to build the models using simple neural network and convolutional neural network. CQCC feature is chosen, as it a feature proved to be robust for ASV spoof detection when tested on GMM classifier. A significant improvement of 4.9% EER is achieved using convolution neural network (CNN). © 2020, Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Semantic Segmentation using K-means Clustering and Deep learning in Satellite Image 2

Semantic Segmentation using K-means Clustering and Deep Lear...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Barthakur, Manami Sarma, Kandarpa Kumar Gauhati University Department of Electronics and Communication Engineering GuwahatiAssam781014 India

ISBN: (纸本)9781728107448

In this paper, a deep learning based method, aided by certain clustering algorithm for use in semantic segmentation of satellite images in complex background is proposed. The work considers the formation and training of SegNet in which the output of K-means clustering algorithm is used as input and the label of the particular region of interest (ROI) in the image are used as target. The method does not require any feature extraction, region growing or splitting methods to configure and train the SegNet, which is a deep convolutional Encoder-Decoder architecture trained with (error) Back Propagation learning. The method is tested with different satellite images. The method is also compared with the results obtained when trained with SegNet without selecting the ROI using K-means algorithm and evaluated using metrics such as accuracy, mean IoU and weighted IoU. The normalized confusion matrix is also plotted. The experimental results show that the method is reliable and suitable for real world situations. © 2019 IEEE.

关键词： K-means clustering

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Assertion Detection in Clinical Natural Language processing: A Knowledge-Poor machine learning Approach 2

Assertion Detection in Clinical Natural Language Processing:...

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2nd IEEE international conference on Information and Computer Technologies (ICICT)

作者： Chen, Long Med Data Quest Inc La Jolla CA 92037 USA

ISBN: (纸本)9781728133232

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.

关键词： natural language processing clinical assertion deep learning LSTM attention mechanism

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Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning 2

Software module development for non-invasive blood glucose m...

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2nd Joint international conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Islam, Minarul Min, Chai Ly Shoumy, Nusrat Jahan Ali, Md Shawkat Khatun, Sabira Karim, Mohamad Shaiful Abdul Bari, Bifta Sama Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang26600 Malaysia School of Computing and Mathematics Charles Sturt University Australia Researcher in School of Computer and Communication Engineering Universiti Malaysia Perlis Pauh Putra Arau-Perlis02600 Malaysia

Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Neural networks

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Emotion Detection Through EEG signals Using FFT and machine learning Techniques 2nd

Emotion Detection Through EEG Signals Using FFT and Machine ...

引用

2nd international conference on Innovative Computing and Communication, ICICC 2019

作者： Saxena, Anvita Tripathi, Kaustubh Khanna, Ashish Gupta, Deepak Sundaram, Shirsh Maharaja Agrasen Institute of Technology DelhiNew Delhi India

ISBN: (纸本)9789811512858

Detecting human emotions using various verbal and non-verbal communicating means such as facial, speech, textual and physiological signals are in use from past long time. However, verbal and facial methods are prone to deception. Therefore, emotion recognition through physiological signals has become an active area of research nowadays. Further, Brain–Computer Interaction (BCI) and Emotion detection through Electroencephalographic (EEG) signals are popular domains for the detection of emotional states, whereas the accurate processing of the EEG signals is still a major challenge due to noise and different bands of frequencies present in the signals. In the presented exposition, a methodology using the combination of Fourier Fast Transform (FFT), Principal Component Analysis (PCA) and k Nearest Neighbor (k-NN) has been proposed for emotion detection. To the best of our knowledge the above mentioned combination is first time introduced in the presented paper. Fourier Fast Transform (FFT) converts a signal from a time or space domain into frequency domain and thus eases the analysis of a given signal. PCA was used for feature selection and further, the selected features were categorized into given emotional states using k-NN classifier. The presented analysis carried out using FFT outperformed the previous experiments, with an accuracy of 96.22%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Evaluating Edge processing Requirements in Next Generation IoT Network Architectures 2nd

Evaluating Edge Processing Requirements in Next Generation I...

引用

2nd IFIP international Cross-Domain conference on Internet of Things, IFIPIoT 2019

作者： Olney, Brooks Mahmud, Shakil Karam, Robert University of South Florida TampaFL33620 United States

ISBN: (纸本)9783030436049

The Internet of Things (IoT) is a massively growing field with billions of devices in the field serving a multitude of purposes. Traditionally, IoT architectures consist of "edge" sensor nodes which are used purely for data collection, actuators for intermediary connectivity, gateways for transmitting data, and cloud servers for processing. However, as application requirements change, IoT architectures must evolve. In the next generation of IoT, traditional architectures may not hold due to power limitations, privacy concerns, or network reliability in certain environments. In particular, Artificial Intelligence (AI) and machine learning (ML) applications – especially image/video processing – have become increasingly widespread, and are famously data and compute intensive applications. In this paper, we give an overview and describe the shortcomings of traditional IoT architectures, and outline key scenarios where local processing may be preferred over transferring data to the cloud. We then describe and evaluate metrics which designers can use to assess the needs of their IoT platform. This framework will be beneficial to IoT device manufacturers and network architects for improving usability and reliability, while protecting privacy in the next generation of IoT. © 2020, IFIP international Federation for Information processing.

关键词： Internet of things

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Particle Swarm Optimization (PSO) based 2-DoF-PID power system stabilizer design for damping out low frequency oscillations in power systems 2

Particle Swarm Optimization (PSO) based 2-DoF-PID power syst...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Acharya, Debasis Das, Dushmanta Kumar Rai, Ankur NIT Nagaland Department of EEE Dimapur Nagaland797103 India

ISBN: (纸本)9781728107448

Low frequency oscillation (LOF) could be a serious concern in grid. It may occur due to tiny signal perturbation. It will cause complete grid collapse. Power system stabilizer (PSS) is used to help the system by rising the damping of grid. During this paper, particle swarm optimization (PSO) based two-degree of freedom PID stabilizer is planned to enhance the damping of the system. The stabilizer parameters are tuned utilizing PSO. Integral square error (ISE) is picked as the objective criterion for the plan. To execute this, IEEE type DC1 exciter with single machine unbounded bus (SMUB) system is considered. The execution of the PSO based 2-DoF-PID is compared with PSO based PI, PID stabilizers. To check the viability of the structured stabilizers, comparatives investigation is finished with some current outcomes. © 2019 IEEE.

关键词： Damping

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Pneumonia Radiograph Diagnosis Utilizing Deep learning Network 2

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Netwo...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： O'Quinn, Wesley Haddad, Rami J. Moore, David L. Georgia Southern Univ Dept Elect & Comp Engn Statesboro GA 30458 USA

ISBN: (纸本)9781538692981

Pneumonia is a life-threatening respiratory disease caused by bacterial infection. The goal of this study is to develop an algorithm using Convolutional Neural Networks (CNNs) to detect visual signals for pneumonia in medical images and make a diagnosis. Although Pneumonia is prevalent, detection and diagnosis are challenging. The deep learning network AlexNet was utilized through transfer learning. A dataset consisting of 5659 images was used for training, and a preliminary diagnosis accuracy of 72% was achieved.

关键词： Artificial Intelligence Deep learning Medical Imaging Image processing Pneumonia Radiograph processing

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2nd international conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2019

2nd International Conference on Human Systems Engineering an...

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2nd international conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2019

ISBN: (纸本)9783030279271

The proceedings contain 162 papers. The special focus in this conference is on Human Systems Engineering and Design: Future Trends and Applications. The topics include: Lean and Ergonomics Competencies: Knowledge and Applications;preprocessing Information from a Data Network for the Detection of User Behavior Patterns;designing an e-Coach to Tailor Training Plans for Road Cyclists;half Scale Dress Forms from 3D Body Scans in Active Poses;a Comparison of Heart Rate in Normal Physical Activity vs. Immersive Virtual Reality Exergames;the Impact of Ergonomic Design on Smart Garments;user Centered Design of a Pill Dispenser for the Elderly;The Importance of ICT and Wearable Devices in Monitoring the Health Status of Coronary Patients;improvement of a Monitoring System for Preventing Elderly Fall Down from a Bed;ergonomic Design Process of the Shape of a Diagnostic Ultrasound Probe;autonomous learning Mediated by Digital Technology Processes in Higher Education: A Systematic Review;discussion on the Effect of Bedding on Sleep Postures;design Culture Within the B2B Needs Roadmap;masticatory Evaluation in Non-contact Measurement of Chewing Movement;satisfaction of Aged Users with Mobility Assistive Devices: A Preliminary Study of Conventional Walkers;effect of Added Mass Location on Manual Wheelchair Propulsion Forces;Exploration of TCM Health Service Mode in the Context of Aging Society;standardized Research of Clinical Diagnosis and Treatment Data of Epilepsy;experience Design: A Tool to Improve a Child’s Experience in the Use of Vesical Catheters;human Aspects in Product and Service Development;potential of Industrial Image processing in Manual Assembly.

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