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Beamforming Design for ISCC Systems with Reconfigurable intelligent Surfaces

Beamforming Design for ISCC Systems with Reconfigurable Inte...

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International conference on Communication Technology (ICCT)

作者： Ruihang Yang Dezhi Wang Chen Zhu Boyu Ning Zhengyu Zhu Chongwen Huang Zhaohui Yang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Polytechnic Institute Zhejiang University Hangzhou China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China College of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The deployment of various emerging wireless services has necessitated that sixth-generation (6G) wireless networks integrate sensing, computing and communication within the same radio spectrum. However, it is challenging to improve the performance of the integrated sensing, communication, and computation (ISCC) systems in complex propagation environment. In this paper, we investigate the beamforming design problem in reconfigurable intelligent surfaces (RIS)-aided ISCC systems, where an RIS can provide additional spatial degree of freedom. To this end, we first formulate a joint beamforming problem for RIS-aided ISCC, aiming to concurrently improve the performance for multiple target sensing, multi-dimensional computation, and multi-stream communication. Then we present joint optimization of various performances (JOVP) algorithm that minimizes the sum mean square error (MSE) of various demands under power constraints. Next, we analyze the impact of key system parameters on the overall performance of ISCC, providing valuable guidelines for system design. Finally, simulation shows that the sensing error of JOVP algorithm is reduced by at least 46.7% compared with the existing popular algorithms, while the computation rate and communication rate are increased by more than 49.1% and 22.2%, respectively.

关键词： Wireless sensor networks Array signal processing Wireless networks System performance Mean square error methods Reconfigurable intelligent surfaces Communications technology sensors Optimization Guidelines

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An Efficient and intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization

An Efficient and Intelligent IoT-Based Security Model for En...

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Network, Multimedia and information Technology (NMITCON) International conference on

作者： H B Gangadharaswamy Narendra Kumar N Sharmila M Shilpa S Mallikarjunaswamy R Shivaji N M Basavaraju P Balachandra M Komala Dept of Computer Science and Engineering JSS Academy of Technical Eduction Bengaluru India Artificial Intelligence and Machine Learning RNS Institute of Technology Bengaluru India Dept of Electronics and Electrical Engineering JSS Science and Technology University Mysuru India Dept of Information Science and Engineering Bengaluru Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering JSS Academy of Technical Eduction Bengaluru India Dept Electronics and Telecommunication Engineering Jagadambha college of Engineering & Technology India Dept of Mathematics SJB Institute of Technology Bengaluru India Dept of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python’s image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it’s automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs.

关键词： Cloud computing Power demand Costs Simulation Memory Infrared image sensors Motion detection Hybrid power systems Security intelligent sensors

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A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion sensors

A Hybrid Residual CNN with Channel Attention Mechanism for C...

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International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a squeeze-and-excitation (SE) mechanism with residual bidirectional LSTM layers. The goal is to enhance the accuracy of person identification using data from wearable motion sensors. The SE blocks facilitate channel-wise feature refinement, highlighting helpful information. Residual connections enable the training of deeper LSTM architectures. The proposed method's accuracy in identifying individuals across increasing periods between training and testing is evaluated using the SP-SW HAR dataset, which contains activity data collected from wristwatch sensors. By leveraging representation learning and exhibiting resilience against sensor noise and temporal fluctuations, SE-ResBiLSTM improves recognition accuracy compared to regular and convolutional LSTM networks. According to the trial data, SE-ResBiLSTM achieved a maximum identification accuracy of 98.71 %. The experimental results demonstrate that this approach significantly boosts the performance of user identification using wristwatch sensors.

关键词： Training Representation learning Accuracy Motion detection Telecommunications Security Wearable devices

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Development of smart flood monitoring and early warning system using weather forecasting data and wireless sensor networks-A review 3

Development of smart flood monitoring and early warning syst...

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3rd International conference on intelligent Communication Technologies and Virtual Mobile networks, ICICV 2021

作者： Subashini, M.J. Sudarmani, R. Gobika, S. Varshini, R. School of Engineering Avinashilingam Institute for Home Science and Higher Education for Women Department of ECE Coimbatore India

ISBN: (纸本)9780738111834

Flooding could be a normal marvel and it is an overflow of water that submerges the land area, which is ordinarily dry in nature bv creating a negative effect on the society due to its tall misfortune on human lives. Due to the negative imnact on the residents and coastal areas, flood prediction and alerting nlavs an important role in the current generation. Recently, a severe flooding took place in the Indian state Kerala during the last monsoon season with a heavy unusual rain, which has led to a severe loss and not happened previously during the century. Within the fortnight, nearly the died people strength is 373, where almost 83% of the dams in the state are opened out to avoid further loss of lives. Thus, the chances of flood occurring should be periodically monitored. Hence, an early warning system is required to save the lives of the people in the endangered areas by using Wireless sensor Network fWSN). In this paper, a system of Wireless sensor networks has been developed and it comprises of sensor hubs that are conveyed at the particular surge and helpless areas for performing a real-time surge checking and discovery. Wireless sensor networks GWSN1 can be deployed for many other real time projects, as the data could be collected rapidly and efficiently. The sensor node consists of certain sensors such as temperature sensor, ultrasonic and rain sensor with limited processing and communication capabilities. The detected information from the sensors is transmitted to the microprocessor for further preparation. The processed information is transmitted to cloud by using a LoRA (Long Range) module. A mobile application has been developed to provide an early warning to the people in the affected areas, where mobile is very handy and conveys information as soon as possible. Hence, the latest technology like sensor network will be used to determine the accurate level of flooding and also the mobile phone connected with this technology will send the forecasting info

关键词： Internet of things

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Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ranging Signals using Deep Learning

Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ran...

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intelligent Control, Measurement and Signal processing and intelligent Oil Field (ICMSP), International conference on

作者： Mengyao Dong Yiming Liu Yifang Zhang School of Information Science and Technology Southwest Jiaotong University Chengdu China

Ultra-wideband (UWB) is an emerging technology with numerous applications, such as indoor positioning, object tracking, and wireless sensor networks. However, non-line-of-sight (NLOS) scenarios can cause significant performance degradation. In this paper, we propose a deep learning model specifically designed for accurately identifying various types NLOS UWB ranging signals for better error mitigation. We extract 13 feature parameters from low-cost UWB sensors to characterize channel quality. Our proposed deep learning model consists of a convolutional neural network module, a long short-term memory module, and a fully connected module for extracting spatial and time-domain features. Through field experiments, we demonstrate that our model achieves high accuracy and F1 score of 0.98 and 0.97, respectively, indicating its potential for accurately identifying different types of NLOS cases in UWB ranging.

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning networks

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention.

关键词： Deep learning Measurement units Prevention and mitigation Face recognition Signal processing sensor systems Telecommunications

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A Comparative Study of Deep Learning Robustness for sensor-based Human Activity Recognition

A Comparative Study of Deep Learning Robustness for Sensor-b...

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International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Recognition of human activities has become a highly significant area of research in the current era of the Internet of Things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. The integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. This study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. The study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. The results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels.

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LoRaWAN and Biosensor-Assisted Smart City Framework to Mitigate the Effects of Lung Diseases

LoRaWAN and Biosensor-Assisted Smart City Framework to Mitig...

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Modeling, Simulation & intelligent Computing (MoSICom), International conference on

作者： K M Abubeker Michaelraj Kingston Roberts C Prajitha S Rinesh A Shafeena Karim S. Jeevanandham Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India Department of Electronics and Communication Engineering Centre for Advances in Signal Processing and Wireless Sensor Networks Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering Centre for Interdisciplinary Research Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Urbanization and industrialization in cities present substantial obstacles to air quality, resulting in detrimental effects on public health, including respiratory health. This research introduces an Internet of Things (IoT) framework for a smart city environment using LoRaWAN and sensor networks. The framework utilizes the ESP32 LoRaWAN module to provide real-time monitoring of essential air quality metrics, such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, and humidity. Integrating biosensors enhances accuracy by identifying distinct biological indicators linked to respiratory well-being. The proposed work aims to use the framework in an urban setting within a global context, demonstrating its capacity to reduce the effects of lung disorders. The ESP32 LoRaWAN module enables effective communication within the sensor network up to 20 kilometers, guaranteeing a robust and adaptable solution for smart city applications. The developed framework provides a scalable and novel method for developing healthier and more sustainable cities, decreasing respiratory disorders as urban settings develop.

关键词： Temperature sensors Smart cities Pulmonary diseases LoRaWAN Air quality Real-time systems Biosensors Internet of Things Public healthcare Organic compounds

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Advanced Flood Management and Rescue Strategies to Securing Lives from Flood Risks

Advanced Flood Management and Rescue Strategies to Securing ...

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International conference on ent Computing and Control Systems (ICICCS)

作者： Manoranjitham E Kavin V Prasand S Praveen Kumar B Saravanan R Department of Electronics and Telecommunication Karpagam College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Both the locating of flood victims and the provision of continuous alarm information to the Disaster Rescue Force are accomplished by the Flood alarm and Rescue System. Additionally, it shows where the individuals who are trapped in the water are. Afterwards, a flood alarm system might be activated to notify the public. Contrarily, in an emergency, the Rescue System will direct the rescue workers to the people who need them. The individuals in need will activate the rescue operation by pressing the button that the government has given them. The "rescue team will use the signal processing device to receive and decode the encoded communication signal that it emits". Because of this, they will be able to find the individual far more quickly than was previously achievable with the technology at their disposal. This is the case since the top goals of the rescuing force are determined by our system. Our prototype is built with the idea that individuals may simply incorporate the gadget into their daily tasks when floods are in the area. The inclusion of a torch in our product ensures its continued viability and makes it suitable for usage in low-light situations, such as during floods.

关键词： Temperature sensors Wireless sensor networks Force Prototypes Pressing Signal processing Control systems Real-time systems Floods Testing

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Achievable rate-exponent region for parallel distributed detection via binary symmetric channels 6

Achievable rate-exponent region for parallel distributed det...

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6th IEEE International conference on intelligent Computing and Signal processing, ICSP 2021

作者： Liu, Xiangyang Wang, Gang Wang, Qiang Li, Xian Ma, Simin National University of Defense Technology College of Information and Communication Xi'an Shaanxi Province China

ISBN: (纸本)9780738143705

Distributed detection with multiple sensors and a fusion center over binary symmetric channels has been investigated. The fusion center receives noisy observations of the local binary decisions. A fundamental tradeoff between achievable information rate and the error exponent of the missing probability exists. The tradeoff is well characterized by the rate-exponent region. The paper first derived a closed-form analytic expression for the error exponent and information rate by means of the theory of relative entropy typical set. Second, the properties of the optimal error exponent and information rate are discussed in detail. It was demonstrated that they are monotonically decreasing in the bit error probability of the binary symmetric channel. Simulation results validated the conclusions. © 2021 IEEE.

关键词： Wireless sensor networks

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