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Epileptic Seizure Detection using short time Fourier transform and Convolutional neural network

Epileptic Seizure Detection using short time Fourier transfo...

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The 3rd International conference on distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： O. Alshaltone R. Shaikh N. Nasir F. Barneih M. Sameer T. Bonny M. A. Shabi A. A. Shammaa Research Institute of Science and Engineering University of Sharjah Sharjah UAE University of Mumbai Mumbai India National Institute of Technology Patna India

The progress of Artificial Intelligence (AI) has been the most remarkable in the 21st Century. However, how far AI must have gone, it works in healthcare remains a big challenge for great minds across the globe. Epilepsy is a neurological disorder that occurs in a human being at any stage in life. Thus, frequent to rare seizures are faced by an individual having epilepsy and sometimes lead to death. The electroencephalogram (EEG) signals help diagnose this ailment. However, often long EEG signal takes a day or more even for trained neurologists to detect this disorder and may even lead to human errors. Thus, it becomes imperative to develop a robust and computationally efficient system that can help detect this ailment without causing delays and human error. This paper aims to extract the time-frequency plane from time domain signals using Short-time Fourier transform (STFT) and develop a neural network model using CNN to classify seizures. This paper has achieved the highest classification accuracy of 100% for some of the experiments and 95-99% accuracy for a few of them, thus helping better classify health and seizures with less computation required.

关键词： electroencephalography Fourier transforms health care medical signal detection medical signal processing neural nets neurophysiology signal classification time-frequency analysis

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Stabilizer of SMPC Throughput using neural networks 40

Stabilizer of SMPC Throughput using Neural Networks

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40th IEEE International conference on distributed Computing Systems (ICDCS)

作者： Harayama, Michiko Miyagawa, Noboru Gifu Univ Dept Elect Elect & Informat Engn Fac Gifu Japan Gifu Univ Div Nat Sci & Technol Grad Sch Gifu Japan

ISBN: (纸本)9781728170022

In view of the successful application of deep learning mainly in the field of image recognition, applications of deep learning in the fields of communication and computer networks are being explored. In these fields, systems that follow proper theoretical calculations and procedures are set up. However, due to the large amount of data to be processed, proper processing takes time, and the aspect that deviates from the theory due to the inclusion of uncertain disturbances sometimes appears. Therefore, deep learning or nonlinear approximation by neural networks may be useful in some cases. The communication method SMPC we have studied measures the throughput by a set of packets called train at the destination node and feeds it back to the source node. By comparing it with the transmission rate, the source node detects congestion on the transmission route and adjusts the packet transmission interval. However, there is a problem that the throughput fluctuates during packets pass through the route, and if it is fed back directly, the transmission rate fluctuates significantly, which causes the fluctuation of the throughput larger and the average throughput even lower. It is not desirable to change the transmission rate unnecessarily. In this study, we tried to stabilize the transmission rate by incorporating prediction and learning by neural network. Optimal transmission rates were predicted by a neural network from the throughput data measured by the destination node, and the results are learned by another neural network to generate a stabilizer. A simple moving average method and a stabilizer using three types of neural networks, MLP, RNN, and LSTM, were built into the transmission controller of the SMPC. It was shown that not only can the fluctuation be reduced, but also the average throughput improves. It is shown that deep learning can be used as a device to predict and output stable values from data with complicated time fluctuations that are difficult to analyze.

关键词： transmission rate congestion control stabilizer SMPC neural network

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A Hybrid Approach of Semantic Weight Based Re-Propagation For Convolutional neural networks in Content Based Medical Image Retrieval

A Hybrid Approach of Semantic Weight Based Re-Propagation Fo...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Gourav Sood Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Content-based image retrieval (CBIR) is a homogeneous search technology based on visual features, and information has been leaked to potential users of pharmaceutical testing, education, and research. However, the CBIR collects images related to acquisition properties, as a result of the inability to predict the similarity of images using the medical part, and the difference between the user’s search destination and object space. In the existing methods, effective points of interest are searched for, and L2 normalization is applied to the spatial arrangement of the original and global features and the RGB channels in the index image by effectively detecting their points of interest. The principal component analysis is used for redundant feature sets and feature sets to provide significant effects. The existing system has certain limitations, such as information loss during the hard task completion coding process and ignores the spatial relationship among the patches in image representation. To overcome the existing method issues, the integrated feature of the images is preferred. In this work, Semantic Weight Based Feed Forward Recurrent Back Propagation (SWFFRBP) is used to retrieve the images, and then based on the semantic weight the correctness of retrieval of images is calculated. The semantic weight determines the accuracy of retrieved images. After that, the recurrent backpropagation calculates the weight error derivatives arising from this entire episode of processing in the network. The existing features compared with the proposed features were Bag of Features (BoF), SURF, and HOG. The retrieval methods taken to compare with proposed retrieval techniques are RFRM and KNN. Thus, medical image retrieval for diagnosis using SWFFRBP, accuracy, False positive rate, relevance, feedback, precision, and recall parameters were tested and proved to be efficient than other methods.

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The Best of Both Worlds: distributed PCA That is Both Exact and Communication Efficient

The Best of Both Worlds: Distributed PCA That is Both Exact ...

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European Signal processing conference (EUSIPCO)

作者： Arpita Gang Waheed U. Bajwa Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ USA

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

The effectiveness of machine learning algorithms largely depends on the goodness of the representation of data. While the massiveness in dimension and amount of modern day data requires dimension reduction and feature extraction for efficient use of available computational resources, the use of un-correlated features is known to enhance the performance of such machine learning algorithms. Thus, an efficient representation learning approach should focus on dimension reduction as well as uncorrelated feature extraction. Even though Principal Component Analysis (PCA) and linear autoencoders are fundamental data processing tools largely used for dimension reduction, they can also be used to extract uncorrelated features when engineered properly. At the same time, factors like ever-increasing volume of data or inherently distributed data generation impede the use of existing centralized solutions for representation learning that require availability of data at a single location. This paper proposes two variants of an algorithm called FAST-PCA (Fast and exAct distributed PCA) based on a feedforward neural network-based system that learn data representations in a distributed setting such that they are reduced in dimension as well as have uncorrelated features. The proposed variants are meant to curb the communication overheads prevalent in the existing solutions and are shown to converge to the exact solutions at a linear rate. These claims are further supported by extensive numerical experiments.

关键词： Dimensionality reduction Representation learning Machine learning algorithms distributed databases Signal processing algorithms Signal processing Feature extraction

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Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International conference for High Performance Computing, networking, Storage and Analysis

Proceedings of RSDHA 2021: Redefining Scalability for Divers...

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2021 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures, RSDHA 2021

ISBN: (纸本)9781665458771

The proceedings contain 6 papers. The topics discussed include: multi-accelerator neural network inference in diversely heterogeneous embedded systems;comparing llc-memory traffic between CPU and GPU architectures;platform agnostic streaming data application performance models;distributed training for high resolution images: a domain and spatial decomposition approach;ELIΧR: eliminating computation redundancy in CNN-based video processing;and energy efficient task graph execution using compute unit masking in GPUs.

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High Performance and Energy Efficient Integer Matrix Multiplication for Deep Learning

High Performance and Energy Efficient Integer Matrix Multipl...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Pau San Juan Pedro Alonso-Jordá Enrique S. Quintana-Ortí Universitat Politècnica de València Spain

We present a multi-threaded implementation of the matrix multiplication for deep learning on ARM multicore processors. Following standard practice for inference with convolutional neural networks, our GEMM kernel operates with 16-bit integer arithmetic, yielding significant performance acceleration and cutting the memory requirements with respect to IEEE (floating point) single precision by half, allowing the deployment of larger neural network models on low power devices with limited storage capacity.

关键词： Performance evaluation Deep learning Program processors Multicore processing neural networks Memory management Energy efficiency

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AI Tool with Active Learning for Detection of Rural Roadside Safety Features

AI Tool with Active Learning for Detection of Rural Roadside...

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9th IEEE International conference on Big Data (IEEE BigData)

作者： Yi, Hong Bizon, Chris Borland, David Watson, Matthew Satusky, Matthew Rittmuller, Robert Radwan, Randa Srinivasan, Raghavan Krishnamurthy, Ashok Univ North Carolina Renaissance Comp Inst Chapel Hill NC 27515 USA US Dept Transportat Volpe Natl Transportat Syst Ctr Cambridge MA USA Univ North Carolina Highway Safety Res Ctr Chapel Hill NC 27515 USA

ISBN: (纸本)9781665439022

Roadway safety, especially in rural areas, is one of the most critical components in transportation planning. In collaboration with North Carolina Department of Transportation (NCDOT), UNC Highway Safety Research Center (HSRC), and DOT Volpe National Transportation Systems Center, UNC Renaissance Computing Institute (RENCI) developed a roadside feature detection solution leveraging multiple convolutional neural networks. The solution used an iterative active learning (AL) computer vision model training pipeline integrated into an AI tool to detect safety features such as guardrails and utility poles in geographically distributed NC rural roads. We utilized transfer learning by adopting the Xception neural network architecture [1] as the feature extraction backbone which was then used in an iterative AL process supported by a web-based annotation tool. The annotation tool not only allowed for the collection of annotations through an iterative AL process for multiple safety features, it also enabled visual analysis and assessment of model prediction performance in the geospatial context. AL techniques were used to direct human annotators to label images that would most effectively improve the model aimed at minimizing the number of required training labels while maximizing the model's performance. The iterative AL process combined with a common feature extraction backbone allowed fast model inference on millions of images in the AL sampling space. This enabled a rapid transition between AL rounds while also reducing the computing requirements for each round. Model feature extraction weights were then fine-tuned in the last round of AL to obtain the best accuracy. Since only about 2.7% of 2.6 million unlabeled images in the AL sampling space contain guardrails, there is a significant class imbalance problem that must be addressed in our AL sampling strategies for the guardrail classification model. In this paper, we present our AI tool processing pipeline and methodolo

关键词： deep learning convolutional neural networks transfer learning active learning class imbalance

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ShadowTutor: distributed Partial Distillation for Mobile Video DNN Inference 20

ShadowTutor: Distributed Partial Distillation for Mobile Vid...

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49th International conference on Parallel processing (ICPP)

作者： Chung, Jae-Won Kim, Jae-Yun Moon, Soo-Mook Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450388160

Following the recent success of deep neural networks (DNN) on video computer vision tasks, performing DNN inferences on videos that originate from mobile devices has gained practical significance. As such, previous approaches developed methods to offload DNN inference computations for images to cloud servers to manage the resource constraints of mobile devices. However, when it comes to video data, communicating information of every frame consumes excessive network bandwidth and renders the entire system susceptible to adverse network conditions such as congestion. Thus, in this work, we seek to exploit the temporal coherence between nearby frames of a video stream to mitigate network pressure. That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model. Moreover, we update only a subset of the student's parameters, which we call partial distillation, to reduce the data size of each network transmission. Specifically, the server runs a large and general teacher model, and the mobile device only runs an extremely small but specialized student model. On sparsely selected key frames, the server partially trains the student model by targeting the teacher's response and sends the updated part to the mobile device. We investigate the effectiveness of ShadowTutor with HD video semantic segmentation. Evaluations show that network data transfer is reduced by 95% on average. Moreover, the throughput of the system is improved by over three times and shows robustness to changes in network bandwidth.

关键词： Deep neural networks distributed inference knowledge distillation video semantic segmentation

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Violence Detection from CCTV Footage Using Optical Flow and Deep Learning in Inconsistent Weather and Lighting Conditions 5th

Violence Detection from CCTV Footage Using Optical Flow and ...

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5th International conference on Advances in Computing and Data Sciences (ICACDS)

作者： Madhavan, R. Utkarsh Vidhya, J. V. SRM Inst Sci & Technol Chennai Tamil Nadu India

ISBN: (纸本)9783030814625;9783030814618

Physical assault detection in surveillance systems plays an extremely significant role in the safety of our city. The current methods include a primitive approach for video-based pre-processing methods, which includes grayscale conversion, image augmentation, etc. The models include 3D Convolutional neural networks (3DCNN) and Support Vector Machines (SVMs). The method which we propose consists of a preprocessing method that involves isolating the pixels into a video (or a group of videos) relevant to the action that is taking place in the original video. In addition, a deep learning model which includes a time-distributed Convolutional neural network (CNN) + Long Short Term Memory (LSTM) model extracts the relevant spatial and temporal information or features that can help us predicting violence from an input CCTV footage. Given an input CCTV footage, our model will be able to extract the relevant features to distinguish a violent event from a non-violent one, preferably independent of the lighting. In the current approaches, it was observed that the number of relevant pixels dedicated to the action is low (due to naive cropping). The present system doesn't take into account the inconsistent weather and lighting conditions and its accuracy is expected to drop under those conditions.

关键词： Violence CCTV Physical assault Deep neural network Computer vision CNN LSTM Fight Optical flow

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Developing a Loss Prediction-based Asynchronous Stochastic Gradient Descent Algorithm for distributed Training of Deep neural networks 20

Developing a Loss Prediction-based Asynchronous Stochastic G...

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49th International conference on Parallel processing (ICPP)

作者： Li, Junyu He, Ligang Ren, Shenyuan Mao, Rui Univ Warwick Coventry W Midlands England Univ Oxford Oxford England Shenzhen Univ Shenzhen Peoples R China

ISBN: (纸本)9781450388160

Training Deep neural network is a computation-intensive and time-consuming task. Asynchronous Stochastic Gradient Descent (ASGD) is an effective solution to accelerate the training process since it enables the network to be trained in a distributed fashion, but with a main issue of the delayed gradient update. A recent notable work called DC-ASGD improves the performance of ASGD by compensating the delay using a cheap approximation of the Hessian matrix. DC-ASGD works well with a short delay;however, the performance drops considerably with an increasing delay between the workers and the server. In real-life large-scale distributed training, such gradient delay experienced by the worker is usually high and volatile. In this paper, we propose a novel algorithm called LC-ASGD to compensate for the delay, basing on Loss Prediction. It effectively extends the tolerable delay duration for the compensation mechanism. Specifically, LC-ASGD utilizes additional models that reside in the parameter server and predict the loss to compensate for the delay, basing on historical losses collected from each worker. The algorithm is evaluated on the popular networks and benchmark datasets. The experimental results show that our LC-ASGD significantly improves over existing methods, especially when the networks are trained with a large number of workers.

关键词： neural networks distributed Training Machine Learning

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