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2023 7th international conference on Communication and Information systems An Improved IOTA Protocol based on Optimal Midway Walk

2023 7th International Conference on Communication and Infor...

引用

ieee international conference on Communication and Information systems (ICCIS)

作者： Hongcheng Wu Hui Zhi Yangyang Liu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Hefei Zhilian Technology Industry Investment Co. Ltd Hefei China

IOTA (internet of Things Applications) protocol aims to provide a secure and trustworthy protocol for realizing data-value interactions between IoT devices, and it has become the next possible direction for the development of Blockchain applications. IOTA protocol has the problem that the time-consuming cumulative weight update grows too fast with the increase of the number of transactions, this paper proposed an improved IOTA protocol based on optimal midway walk, the improved protocol uses cutting-edge transaction selection algorithm of weighted Monte Carlo Markov random walk to reduce cumulative weight update delay. Firstly, this improved IOTA protocol mainly ensures the efficiency of transaction confirmation without losing the security of consensus by introducing the optimal midway walk method, i.e., the starting point of each walk is chosen to depart at an optimal location. Secondly, this paper designs the indirect verification identity record algorithm and the layer degree of confirmation principle are designed to realize the improved IOTA protocol's resilience and resistance to Parasite chain attack. The experimental results show that the improved IOTA using random function to select the midway walk starting point from i-50λ, to i-75λsubstantially solves the system computational burden problem and greatly reduces the cumulative weight update delay.

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Application of Artificial Intelligence in Adaptive Face Recognition System

Application of Artificial Intelligence in Adaptive Face Reco...

引用

11th ieee international conference on System Engineering and technology, ICSET 2021

作者： Garcia, Rhose Anne C. Lacayanga, Raymond Paolo Cruz, Febus Reidj G. School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9781665437660

Adaptive facial recognition includes the features of artificial intelligence (AI) technology, internet of things (IoT) devices, and wireless fidelity (Wi-Fi) connection. The application of Artificial Intelligence was used to detect faces from the captured images. Using video image to extract features from the face, Local Binary Pattern Histogram (LBPH) algorithm was used. The ideal distance between the subject and the webcam must be 30 cm for minimum percent error. The study proposes a designed face recognition system of surveillance cameras that adapts through the changes of the features of the person by re-Training the AI by saving various images of it. This utilizes the component Raspberry Pi 4 where the local binary pattern histogram is applied. based on the results, the proposed method was able to produce an overall detection accuracy of 91.72% and an average processing time of 1.4 seconds. The output shows very promising results for face detection devices in the field of face recognition monitoring system. © 2021 ieee.

关键词： Histograms Face recognition Surveillance Lighting Feature extraction internet of Things Face detection

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Processing and Visualization of Tomography signal with AI: Decision Making on COVID-19 4

Processing and Visualization of Tomography Signal with AI: D...

引用

4th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2022

作者： Ishkov, Denis O. Goryachkin, Boris S. Terekhov, Valery I. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9781665414340

The coronavirus COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. As the name suggests, the virus is characterized by SARS (Severe acute respiratory syndrome). Five times more deadly than the influenza virus, COVID-19 infects the respiratory system, causing coughing, shortness of breath and death in severe cases. One of the most effective ways to diagnose the form and severity of the disease is X-ray images and computed tomography (CT) of the chest. To improve the efficiency of diagnosing infection with high accuracy, a method based on artificial intelligence is necessary. Gradient Weighted Class Activation Mapping (Grad-CAM) highlights important areas in images using deep learning heat map models. The dynamic anamorphosis method is considered as a promising tool for decision support. The method helps to activate the visual-figurative mechanisms of thinking of a decision-maker and to simplify the study of information. The article describes an algorithm for cognitive visualization of a two-dimensional image of the lungs by anamorphosis of the Grad-CAM heat map. Directions for further research are outlined. © 2022 ieee.

关键词： COVID-19

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Self-Distillation Hashing for Efficient Hamming Space Retrieval

Self-Distillation Hashing for Efficient Hamming Space Retrie...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Hongjia Zhai Hai Li Hanzhi Zhang Hujun Bao Guofeng Zhang State Key Lab of CAD&CG Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Deep hashing-based approaches have become the optimal solutions for large-scale image retrieval task due to their high computational efficiency and low storage burden. Some methods leverage a large teacher network to improve the retrieval performance of the small student network through knowledge distillation, which incurs high computational and time costs. In this paper, we propose Self-Distillation Hashing (SeDH), which improves the image retrieval performance without introducing a complex teacher model and significantly reduces the overall computation costs. Specifically, we generate the soft targets via ensembling the logits of other similar images among the mini-batch. The ensembled soft targets can model the relations between different image samples, which can act as additional supervision for classification. Besides, to learn more compact features and accurate inter-sample similarities, we propose a similarity-preserving loss on the learned hashing features, which aligns the softened similarity distribution with the pairwise soft similarity. Extensive experiments demonstrate that our approach can yield state-of-the-art performance on deep supervised hashing retrieval.

关键词： Knowledge engineering Costs Computational modeling image retrieval signal processing Acoustics Computational efficiency

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Multi-Stream Facial Adaptive Network for Expression Recognition from a Single image

Multi-Stream Facial Adaptive Network for Expression Recognit...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Baichuan Zhang Fanyang Meng Runwei Ding Mengyuan Liu Key Laboratory of Machine Perception Peking University Shenzhen Graduate School Peng Cheng Laboratory

Facial expression recognition from a single image has potential applications in fields including human-computer interaction and medical diagnosis. Most recent methods use deep neural networks to directly learn from a roughly cropped facial image which is usually detected from a whole image by face detection algorithms. We observe that unrelated surrounding regions in the rough facial image prevent deep neural networks from learning facial-related discriminate features. To solve this problem, we present a Facial Adaptive Network (FAN) which is able to adaptively select an interest region from the given facial image, thus suffering less from the effect of unrelated regions. based on the selected interest region, we further apply the self-attention mechanism to learn discriminate facial features. Moreover, we introduce a multi-stream FAN (ms-FAN) that learns richer facial features from multiple interest regions that are selected from pose-augmented facial images. Extensive experiments on Oulu-CASIA, CK+, and RAF-DB datasets consistently verify the effect of our proposed MS-FAN by achieving comparable results with state-of-the-art methods. Our code is available at https://***/zhangbc12/DAtt-ViT.

关键词： Deep learning Fans Adaptive systems image recognition Face recognition Neural networks Speech recognition

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Emotion Recognition Using Bahasa Malaysia Natural Speech 7

Emotion Recognition Using Bahasa Malaysia Natural Speech

引用

7th ieee international conference on signal and image Processing Applications, ICSIPA 2021

作者： Rizal, Afiq Aiman Ahmad Fairuz Hashim, Nik Nur Wahidah Nik Department of Mechatronics Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9781665435925

In recent years, the technology of emotion speech recognition has gradually become more important to the industries. This is proven by the integration of this system into many applications such as the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies and so on. Generally, speech emotion recognition (SER) is developed to help humans to understand and retrieve desired emotions. In this research, the analysis of using Bahasa Malaysia Language for three basic emotions of happy, sad and angry was analyzed. A total of 30 male and 30 female audio recordings were collected. Mel-frequency cepstral coefficient, chroma and mel spectrogram features were extracted. Feature dimensions were reduced using forward, backward and exhaustive selection methods before classification. Classification was performed using K-nearest neighbors, Support Vector Machine and Random Forest. The analysis demonstrated 78% accuracy for male speech and 78% for female speech. © 2021 ieee

关键词： Emotion Recognition

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Towards Contactless Human Concentration Monitoring Using mmWave signals

Towards Contactless Human Concentration Monitoring Using mmW...

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international conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Yuan Ge Yi Wei Xiaonan Guo Yucheng Xie Yan Wang Jerry Cheng Yingying Chen George Mason University USA Yeshiva University USA Temple University USA New York Institute of Technology USA Rutgers University USA

ISBN: (数字)9798350386707

ISBN: (纸本)9798350386714

Maintaining concentration in today’s complex and distracting environments is increasingly challenging, with significant impacts on productivity, learning outcomes, and safety. Traditional methods like self-reporting and observational studies are subjective and labor-intensive. Current approaches, including camera-based systems and wearable sensors, raise privacy concerns and require continuous physical interaction. To address these limitations, we propose a novel, contactless concentration monitoring system using mmWave technology. Our system leverages Commercial-Off-The-Shelf (COTS) mmWave devices to detect concentration-related activities, such as eye blinking, nodding, yawning and leg shaking. In particular, we enhance the activity detection and overcome the limited field of view (FOV) of mmWave devices through spatial decomposition based on Delay-and-Sum (DAS) beamforming technologies. Moreover, we mitigate interference in concurrent activities by exploiting the distinct frequency ranges associated with each concentration-related activity based on Short-Time Fourier Transform (STFT). A CNN model, integrated with domain adaptation techniques, ensures robust performance in diverse environments. Experiments involving 10 volunteers demonstrated an overall accuracy of 95.3% in detecting human activities. The system maintained robust performance at distances up to 150 cm and across different office environments. Our method offers a contactless and privacy-preserving alternative to current approaches, making it suitable for applications such as classroom monitoring, workplace productivity observance, and cognitive health monitoring.

关键词： Productivity Legged locomotion Adaptation models Accuracy Array signal processing Feature extraction Safety Millimeter wave communication Monitoring Wearable sensors

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LNeRV: Learnable Hierarchical Encoding Improve Neural Representation Video Codec

LNeRV: Learnable Hierarchical Encoding Improve Neural Repres...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jiahong Chen Xiang Liu Bin Chen Baoyi An Dai Tao Shu-Tao Xia Singhua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Harbin Institute of Technology Shenzhen Huawei Technologies Co. Ltd Shenzhen University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing Implicit Neural Representation (INR) video compression techniques have opened up new avenues in the field of video compression. NeRV maps the temporal coordinates to high-resolution images using neural networks, providing a more flexible and efficient encoding method for video data. However, NeRV implicitly stores all video information in the network, requiring post network compression techniques such as pruning. To integrate explicit compression and implicit representation into an end-to-end framework, this study proposes a novel neural representation-based video compression paradigm called Latent code based Neural representation video compression (LNeRV). Specifically, LNeRV consists of hierarchy feature grids, synthesis network and entropy coding network. With a single-stage training process, LNeRV achieves video compression and dynamically allocates bits according to video complexity, better fitting dynamic videos. We provide a comprehensive compression-to-decompression workflow for our approach. Extensive experimental results verify the effectiveness of our LNeRV.

关键词： Training image coding Neural networks Rate-distortion Video compression signal processing Dynamic scheduling Entropy coding Video codecs Speech processing

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Road Identification Algorithm based on Tire Acceleration signal Under Multi-Vehicle Conditions

Road Identification Algorithm Based on Tire Acceleration Sig...

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image Processing and Computer Applications (ICIPCA), ieee international conference on

作者： Peiwen Xia Jiayi Zhang Zheng Nie Wenxin Yao Zongqiao Han Zhichao Xu Shun Zhang Nanjing University of Science and Technology Nanjing China Jiangsu Lubida IOT Technology Co. Ltd Nantong China

In this paper, an intelligent tire algorithm based on back propagation neural network was proposed to identify the type of road peak adhesion coefficient. In the drum test, eight different vehicle conditions were taken into account for each of three simulated road conditions. Tire pressure of 900 or 1000 kPa, speed of 30 or 40 km/h, and load of 500 or 1000 kg were the parameters for the vehicle conditions. The corresponding tire x-acceleration signal was obtained using the tire acceleration sensor. Then, the original signal data was denoised using wavelet transform. The signal data was grouped by tire rotation period and the anomalous data was eliminated using the isolated forest algorithm. Following that, wavelet transform was used once more to extract the time domain and frequency domain characteristics from each batch of data, producing 45-dimensional feature parameters. Principal components analysis was applied to reduce the dimension of feature parameters to obtain 8-dimensional principal components. Finally, to identify the type of road peak adhesion coefficient, the normalized principal components and vehicle condition parameters (tire pressure, speed, load) were used as input to build a four-layer back propagation neural network, while the adam algorithm was used to optimize the learning rate. 10-fold cross validation was used to examine the model effect. The prediction results show that the model has a high identification accuracy for three types of road, with an average accuracy of 99.53%. The prediction accuracy of the models developed in 10-fold cross-validation fluctuates between ±0.2%. Under different vehicle conditions, the proposed road identification perception algorithm establishes the direct correlation between tire x-axis acceleration and road adhesion coefficient. With its high dependability and real-time capabilities, this algorithm has a wide range of possible applications in the areas of automated driving and intelligent tires.

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Road Traffic Update and Control Traffic Light using Dynamic Patterns from Video Streaming Data 25

Road Traffic Update and Control Traffic Light using Dynamic ...

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25th international conference on Computer and Information technology, ICCIT 2022

作者： Islam, Md Shafkat Maharub Hossain, Md. Refat, Mizbatul Jannat International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Premier University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350346022

Controlling traffic congestion is one of the most challenging issues in the real world, and an automated traffic monitoring system established on the computer vision and internet of things is an emerging scientific field in research. Traffic Congestion becomes severe in the most densely populated area to move timely from one place to another, passing through massive traffic. Although several research papers have presented their solutions for monitoring traffic congestion, we do not receive a permanent solution. In this paper, We try to find a different but unique way to overcome it. Our proposed system works with real-time video sequential data and detects different vehicle shapes using contour-based learning and a convex hull algorithm. We execute the procedure in a way where we work with per-second frames (fps) of the video, and then we determine the total, running, and motionless stagnant vehicles in each frame. After observing vehicle differentiation, an automated decision will be generated, i.e., JAM or NOT JAM which will be transmitted to the Map for users' assistance. After that, the decision about the traffic situation of all nodes makes a combination, and then we find two patterns, one will identify the traffic situation of all connected roads, and another will control the traffic signal automatically. © 2022 ieee.

关键词： Traffic congestion

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