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Performance Analysis of Lightweight Algorithm GIFT-COFB for 3-D Printer Security

Performance Analysis of Lightweight Algorithm GIFT-COFB for ...

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international conference on Signal processing and Communication (ICSC)

作者： Nilufar Yasmin Richa Gupta Department of Electronics & Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665454315

Secure lightweight algorithms have lately been proving their benefits and applications in resource-constrained environments. 3-D printers in a distributed network need security approval for their authentic and secure utilization. NIST round 2 algorithm GIFT-COFB and its primitive algorithm GIFT have been tested for their performance for STL files used in 3-D printers. GIFT-COFB is an Authenticated Encryption with Associated Data (AEAD) algorithm, an underlying scheme for which is the GIFT-128 lightweight block cipher. We analyze the power consumed, energy consumed and time taken by the algorithm for the encryption and decryption of a particular STL file. then a comparison of state-of-the-art parameters of GIFT-COFB and GIFT is carried out to show the improvements we achieved by using the COFB lightweight AEAD operating mode. Analysis results show its worth in providing security to STL files. For further improvement in the system, signal processing can be applied.

关键词： Performance evaluation Signal processing algorithms Authentication Passwords Signal processing NIST Encryption

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Image Enhancement Based on Generative Adversarial Neural Network 8

Image Enhancement Based on Generative Adversarial Neural Net...

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8th IEEE international conference on Consumer Electronics-Taiwan (ICCE-TW) - Driving World to 5G and Beyond with Consumer Technology

作者： Cheng, Hao-Wen Fan, Yu-Cheng Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781665433280

In this paper, we present an image enhancement scheme based on generative adversarial neural network. We use deep learning to decompose the image to obtain the shadow layer and the reflection layer image, and then perform image enhancement for the shadow layer image. In addition to being faster than the traditional method, the method we proposed also achieves better results. In this way, the overexposed or dark parts of the picture can be repaired, and more complete picture information can be obtained. For the repair of the shadow layer, we use a neural network for algorithm simulation. the Generative Adversarial Neural Network makes the training and testing speed more accurate and faster by giving conditional restrictions.

关键词： Training Image color analysis Neural networks Low-pass filters parallel processing Reflection Image decomposition

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Integrating Machine Learning with Biomedical Signal processing and Systems Analysis: An Applications-based Course

Integrating Machine Learning with Biomedical Signal Processi...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Patjanaporn Chalacheva Michael C.K. Khoo Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Alfred E. Mann Department of Biomedical Engineering University of Southern California Los Angeles CA USA

the growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, but separately from, more traditional engineering courses, such as signal and systems analysis. We propose a new approach that systematically integrates signal processing and systems analysis with key techniques in machine learning. In the proposed course, the student obtains hands-on experience in applying algorithms that can be applied to practical problems of physiological signal conditioning, analysis and interpretation. this is achieved by exposing the student to a sequence of 4 applications-based modules that represent different biomedical engineering problems: human activity recognition from wearable devices, epileptic seizure detection, quantification of dynamic respiratory-cardiac coupling in humans under different conditions, and detection of sleep apnea episodes from heart rate variability data. Within each module, the student gains the experience of working with the data in question "from the ground up". We also introduce a general plan for assessment of student learning, and discuss the expected outcomes and limitations of this integrative approach to *** Relevance— the proposed course is targeted at biomedical engineering students at the senior undergraduate or first-year graduate level who are interested in learning how to analyze physiological signals. the course would also be suitable for clinician-scientists who have prior training in statistics with some exposure to engineering mathematics.

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A Near-Optimal Deep Detector for BER Minimization in an OFDM AF Relay System

A Near-Optimal Deep Detector for BER Minimization in an OFDM...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Mohammad Shamsesalehi Nima Mozaffari Khosravi Mahmoud Ahmadian Attari K.N.Toosi University of Technology Tehran Iran

ISBN: (纸本)9781665476249

this paper investigates a Near-Optimal Deep Learning (DL)-based detector for an Orthogonal Frequency Division Multiplexing (OFDM) Amplify and Forward (AF) relay system. the main goal of the proposed DL detector is to achieve the best Bit Error Rate (BER) performance by a low-complexity Deep Neural Network (DNN). A pre-processing stage based on the frequency domain is applied before entering the input data into DNN. Since the performance of the model is very sensitive to tuning the parameters, simulation results compare the BER performance for different scenarios to obtain an accurate model. Finally, we show that the desired model is close to the Maximum Likelihood (ML) as an optimal detector.

关键词： Deep learning OFDM Simulation Bit error rate Signal processing algorithms Detectors Relay networks (telecommunication)

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Bangla Sign Language Recognition using YOLOv5

Bangla Sign Language Recognition using YOLOv5

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international conference for Convergence of Technology (I2CT)

作者： Mainul Karim Maun Ali Md. Ferdous Hasan Most Fatema-Tuj-Jahra Syeda Sumaia Sultana Dewan Md. Farid Department of Computer Science and Engineering United International University United City Dhaka Bangladesh

Bangla Sign Language (BdSL), also known as Bengali or Bangladeshi Sign Language, is the sign language that is commonly used for communication with the deaf people in Bangladesh and some parts of India, e.g., West Bengal (formally known as Calcutta), Assam, Tripura, and the Andaman and Nicobar Islands. Bangla is one of the sweetest languages in the world, according to the UNESCO survey, and the 7th most spoken language in the world. In Bangladesh, about 13.7 million people have a hearing problem, and the Bangladesh National Federation of the Deaf (BNFD) was established on December 25, 1963, to provide training, skill development, and education to deaf people. In this study, we have applied transfer learning in machine learning for BdSL recognition that reuses a pre-trained classifier named YOLOv5 to detect and classify BdSL. Transfer learning speeds up the training process with a minimum number of instances and improves classification performance. Transfer learning for deep learning has become very popular for solving computer vision and natural language processing (NLP) tasks nowadays. Initially, we collected a 24.2 GB video of BdSL from SignBD-word and extracted images from the video using the FFmpeg multimedia framework. then, we annotated the images using the LalelImg image annotation tool. Next, we checked the image annotation and built the model using YOLOv5. YOLOv5 is a family of compound-scaled object detection models, which stands for "You Only Look Once", commonly used for detecting objects by separating images into a grid structure. Finally, we have implemented the prototype with an average accuracy of 91.62%.

关键词： Training Deep learning Computer vision Transfer learning Image annotation Gesture recognition Auditory system

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Hybrid Parameter Update: Alleviating Imbalance Impacts for Distributed Deep Learning

Hybrid Parameter Update: Alleviating Imbalance Impacts for D...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Hongliang Li Dong Xu Zhewen Xu Xiang Li College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. this paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. the proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads.

关键词： Training Deep learning Computational modeling Focusing Distributed databases parallel processing Resource management

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GPU parallelization and Optimization of a Combustion Simulation Application

GPU Parallelization and Optimization of a Combustion Simulat...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zhixiang Liao Yongzhou Liu Yonggang Che State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

Graphics processing units (GPUs) are widely used in the area of scientific computing. While GPUs provide much higher peak performance, efficient implementation of real applications on the GPU architectures is still a non-trivial task. It is crucial to realize efficient solution algorithms that can better utilize GPU architectures. this paper presents our efforts in parallelizing and optimizing LESAP, a CFD application for scramjet combustion simulation, on NVIDIA GPUs. the GPU parallelization is realized based on the CUDA programming model, with a data-parallel implicit time-marching method that is efficient on the GPU architecture. Furthermore, shared memory and redundant calculation are proposed to reduce memory access overhead during GPU computation, and data transfer between CPU and GPU is optimized by packing the data to be transferred. the experimental results show that the GPU version, when runs on four V100 GPUs, achieves a speedup of 11.26 times compared to the CPU version that runs on two 24-core Intel Skylake Gold 6240R CPUs. Excellent parallel scalability across multiple GPUs is also observed.

关键词： Gold Smart cities Computational modeling Graphics processing units Computer architecture Combustion Central processing Unit

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Classification of Four-Class Motor-Imagery Data for Brain-Computer Interfaces 8th

Classification of Four-Class Motor-Imagery Data for Brain-Co...

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8th international conference on the Development of Biomedical Engineering, BME8 2020

作者： Ngan thanh, Luu Anh Hoang Lan, Duong Dung Xuan, Nguyen Khiet thi thu, Dang Chau Nu Ngoc, Pham thuong Hoai, Nguyen Huong, Ha thi thanh School of Biomedical Engineering International University Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam

ISBN: (纸本)9783030755058

Brain-computer interface (BCI) provides opportunities for patients with severe motor impairment to regain their motor controls by decoding and converting Electroencephalogram (EEG) signals into the motor control signal. Existing processing schemes, including artifact removal and band-pass filtering, usually cause leakage of EOG artifacts. Hence, the preprocessing order in this paper was optimized for artifact removal in event-related desynchronization. To address this challenge, we implemented an algorithm to classify four different motor imagery tasks, including the left hand, right hand, both feet, and tongue movements. Here, we use Graz dataset A published in the BCI competition IV in 2008. Band-pass filter (7–30 Hz) with 5th order Butterworth IIR combined with linear regression was applied to remove the artifact of the signal. Mutual Information Best Individual Features (MIBIF) and Linear Discriminant Analysis (LDA) algorithms were used for selecting features and classifying the classes. the mean training kappa of our classifier is 0.57, and the mean evaluated kappa is 0.41. Besides, the application of the Common Spatial Pattern enhanced the kappa score (from 0.33 to 0.41). those results are comparable to those from the BCI competition (range from 0.29 to 0.57). Hence, our approach could be the foundation for further development of motor imagery based BCI research. For future works, we propose to create an algorithm for the automatic procedure by automatically choosing proper frequency bands for band power features. © 2022, Springer Nature Switzerland AG.

关键词： Electroencephalography

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WSN based Intelligent Coal Mine Monitoring using Machine Learning

WSN based Intelligent Coal Mine Monitoring using Machine Lea...

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international conference on Signal processing and Communication (ICSC)

作者： Gayatri Sakya Akash Shukla Aniket Kumar Vineet Kumar Department of ECE JSS Academy of Technical Education Noida UP India

ISBN: (纸本)9781665454315

Keeping in mind the various problems related to gas leakage causing accidents in the coal mine, this paper depicts coal monitoring system using wireless sensor networks and IoT, which can monitor the various gas and temperature parameters and take action with the help of multimodal logistic regression algorithm applied on the real time collected data on cloud. It is observed that the model gives good accuracy and can help to predict mine abnormal health and reduce accidents. this system has been made to measure the values of different sensors in real time as well as to send it to our cloud as well as monitoring setup. these value our real time and can also be accessed from remote locations. the machine learning model will take automatic action based on our dataset which will keep on growing as time passes and this model will keep on becoming more accurate. A cost effective NRF based wireless mine supervising system with early-warning intelligence is proposed in this paper.

关键词： Temperature measurement Wireless communication Temperature sensors Wireless sensor networks Machine learning Real-time systems Fuel processing industries

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GPU-based Real-time Image Mosaicing for UAVs

GPU-based Real-time Image Mosaicing for UAVs

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international conference on Network and Information Systems for Computers (ICNISC)

作者： Feng Tian Yingbo Zhou Jialin Cheng Zhangwei Feng Jiawei Yu Guoxiang Ye Shechuan Duan Chengdu Aircraft Industrial (Group) Co. Ltd Chengdu China

ISBN: (纸本)9781665453523

Facing the problem of large amount of UAV image data, multiple redundancies and uneven quality, this paper proposes a GPU-based UAV real-time image mosaicing technology. First, the video stream data captured by the UAV is decoded to obtain frame images. then, for these large number of frames images, pre-processing such as cache frame extraction and geometric correction is performed to obtain high quality and low redundancy UAV images. Extracting image feature points and matching them by SIFT, SURF, etc., and screening matching points by RANSAC algorithm. Finally, based on the screened matching points, image fusion is performed to obtain the UAV mosaicing image. the overall process is parallel accelerated by GPU, which is more efficient and real-time. the mosaiced images can be tile-cut and stored in chunks to achieve fast sharing and efficient application of image data.

关键词： Matched filters Redundancy Graphics processing units Streaming media Feature extraction Real-time systems Reliability

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