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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

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Fault-tolerant Data Aggregation Scheme for Monitoring of Critical Events in Grid based Healthcare sensor networks 11

Fault-tolerant Data Aggregation Scheme for Monitoring of Cri...

引用

Simulation Multiconference

作者： Ather Saeed Andrew Stranieri Richard Dazeley Center for Informatics and Applied Optimization University of Ballarat and Melbourne Institute of Technology

ISBN: (纸本)9781617828409

Wireless sensor devices are used for monitoring patients with serious medical conditions. Communication of content-sensitive and context sensitive datasets is crucial for the survival of patients so that informed decisions can be made. The main limitation of sensor devices is that they work on a fixed threshold to notify the relevant Healthcare Professional (HP) about the seriousness of a patient's current state. Further, these sensor devices have limited processor, memory capabilities and battery. A new grid-based information monitoring architecture is proposed to address the issues of data loss and timely dissemination of critical information to the relevant HP. The proposed approach provides an opportunity to efficiently aggregate datasets of interest by reducing network overhead and minimizing data latency. To narrow down the problem domain, in-network processing of datasets with Grid monitoring capabilities is proposed for the efficient execution of the computational, resource and data intensive tasks. Interactive wireless sensor networks do not guarantee that data gathered from the heterogeneous sources will always arrive at the sink (base) node, but the proposed aggregation technique will provide a fault tolerant solution to the timely notification of a patient's critical state. Experimental results received are encouraging and clearly show a reduction in the network latency rate.

关键词： Data aggregation Grid computing In-network processing sensor networks Fault-tolerance unified retrieval models Fault tolerance sensor networks Grid computing grids Grid Dataset Device sensor data delay data loss Patient Monitoring

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Charm: Exploiting Geographical Diversity through Coherent Combining in Low-Power Wide-Area networks

Charm: Exploiting Geographical Diversity through Coherent Co...

引用

international symposium on information processing in sensor networks (IPSN)

作者： Adwait Dongare Revathy Narayanan Akshay Gadre Anh Luong Artur Balanuta Swarun Kumar Bob Iannucci Anthony Rowe Carnegie Mellon University Carnegie Mellon University IIT Madras Carnegie Mellon University Instituto Superior Tecnico Lisbon

Low-Power Wide-Area networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.

关键词： Logic gates Cloud computing Diversity reception Batteries Hardware MIMO communication Urban areas

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Research on Autonomous Driving Scenario and Decision-making based on Deep Neural Network

Research on Autonomous Driving Scenario and Decision-making ...

引用

international Conference on Electronics and information Technology (EIT)

作者： Daoyi Xiao Yulu Hu Zhiyang He School of Electronics and Control Engineering Chang'an University Xi'an China School of Transportation Engineering Chang'an University Xi'an China School of Information Engineering Chang'an University Xi'an China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

The foundation of autonomous driving safety is high-precision environmental perception. As a mission-critical task, object detection has attracted considerable attention in recent years. It employs a range of sensors to gather data on factors such as target position, size, and direction of movement, thereby providing a crucial foundation for driving decisions. This study employs an enhanced Deep Convolutional Neural Network (CNN) to investigate decision-making processes in autonomous driving scenarios, thereby advancing the field of artificial intelligence. The introduction of a multi-scale feature extraction module and a self-attention mechanism serves to enhance the model's capacity for perception of complex traffic environments and recognition of key scenes. The model is capable of efficiently processing sensor data, thereby facilitating accurate scene understanding and real-time decision optimisation. The experimental analysis demonstrates that the enhanced model exhibits superior performance compared to the conventional approach across a spectrum of driving scenarios, thereby enhancing the precision and dependability of decision-making.

关键词： Accuracy Decision making Real-time systems Stability analysis Safety Convolutional neural networks Time factors Autonomous vehicles Optimization Intelligent sensors

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A study for AGV steering control and identification using vision system

A study for AGV steering control and identification using vi...

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IEEE international symposium on Industrial Electronics (ISIE)

作者： Jin-Woo Lee Sung-Uk Choi Chang-Hoon Lee Young-Jin Lee Kwon-Soon Lee Department of Electronics Control & System Laboratory Dong-A University Busan South Korea Department of Electrical Instrument and Control Korea Aviation Polytechnic College Sacheon South Korea

ISBN: (纸本)0780370902

The authors experimented on an AGV driving test with a color CCD camera. This paper can be divided into two parts. One is the image processing part to measure the condition of the guideline and AGV. The other obtains the reference steering angle through using the image processing parts. First, the two dimensional information of image derived from the vision sensor is interpreted to 3 dimension information by the angle and position of the CCD camera. Through these processes, the AGV knows its driving conditions. Using this information, the AGV calculates the reference steering angle changed by its speed. In the case of low speed, it focuses on the left/right error values of the guideline. As the speed of the AGV increases, it focuses on the slope of the guideline. Lastly, the authors model the above descriptions to the type of PID controller and regulate the coefficient value of the AGV speed.

关键词： Control systems Machine vision Guidelines Charge coupled devices Charge-coupled image sensors Image processing Three-term control Electronic equipment testing sensor systems Neural networks

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Machine Learning Techniques and Computing Technologies for IoT based Smart Healthcare (COVID-19 Case Study)

Machine Learning Techniques and Computing Technologies for I...

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Advances in Computing, Communication Control and Networking (ICAC3N), international Conference on

作者： Mahmood Hussain Mir Sanjay Jamwal Shahidul Islam Qamar Rayees Khan Baba Ghulam Shah Badshah University Rajouri Jammu and Kashmir India

ISBN: (纸本)9781665438124

The exponential growth of smart devices and gadgets connected to the Internet of Things (IoT) produces massive amounts of data. The number of internet-connected devices is predicted to outnumber people by 25 to 50 billion by 2025. The new concept IoT refers to the combination of wired and wireless embedded communication technologies, sensors, actuators (transducers), and internet-connected items. The data generated by these IoT devices is in high velocity, variety, and varsity in accordance with location and time. IoT is generating data so it one of the primary sources of big data. Intelligent analysis and processing need an hour to develop smart IoT applications to tackle this huge volume of data. This article accesses various computing frameworks such as Cloud Computing, Fog Computing and Edge Computing environments for smarter IoT applications. This article also presents how machine learning algorithms can be incorporated in IoT data to get better insights. Various machine learning algorithms are explained and how these algorithms can be applied to data to get a higher level of information. This studies key contribution is how computing frameworks can be integrated with machine learning techniques in the healthcare sector to get better insights from the data. Mostly in this article, IoT and machine learning algorithms are discussed with respect to COVID-19. Moreover, the potential application areas and open issues of IoT data analytics and Machine Learning are discussed.

关键词： COVID-19 Cloud computing Wireless sensor networks Machine learning algorithms Transducers Machine learning Medical services

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Adaptive Tasks Balancing in Wireless sensor networks

Adaptive Tasks Balancing in Wireless Sensor Networks

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3rd international Conference on information and Communication Technologies, vol.4

作者： Fadi Tirkawi Stefan Fischer Institute for Telematics University of Lübeck Lubeck Germany

In this paper, we extend our CPU-scheduling algorithm [1] which is based on topology information of the network. In wireless sensor network applications, the nodes closer to the network's central point have to perform forwarding operations. Concurrently, they also have to process their own sensing packets. In high rate application, such nodes suffer from CPU overloading. In the priority scheduling scheme [2], which solves the overloading in simple FIFO CPU-scheduling in wireless sensor nodes, it is not ensured that these nodes will have fair chance to process their local sensing packets if the forwarding operations will be prioritized over their local processing. To guarantee processing and then sending the local packets, we introduce the scheme of switching in the priority according to the routing information in such nodes, so that they are able to complete their local tasks also. Our proposal enhances the fairness of the distributed sensing.

关键词： Wireless sensor networks Priority CPU-scheduling Tasks Routing

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Real-Time Hand Postures Recognition using Low Computational Complexity Artificial Neural networks and Support Vector Machines

Real-Time Hand Postures Recognition using Low Computational ...

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3rd international symposium on Communications, Control, and Signal processing (ISCCSP 2008), vol.3

作者： Ticiano A. C. Bragatto Gabriel S. I. Ruas Marcus V. Lamar Department of Electrical Engineering Brasilia University Brazil Department of Computer Science Brasilia University Brazil

This paper proposes two main techniques for reduce computational complexity on Artificial Neural networks, using piecewise linear activation function, and Support Vector Machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.

关键词： Hand Posture Recognition Artificial Neural networks Support Vector Machines

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Deep long short-term memory networks for speech recognition

Deep long short-term memory networks for speech recognition

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international symposium on Chinese Spoken Language processing

作者： Jen-Tzung Chien Alim Misbullah Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu

ISBN: (纸本)9781509042951

Speech recognition has been significantly improved by applying acoustic models based on deep neural network which could be realized as the feedforward NN (FNN) or the recurrent NN (RNN). In general, FNN is feasible to project the observations onto a deep invariant feature space while RNN is beneficial to capture the temporal information in a sequential data for speech recognition. RNN based on long short-term memory (LSTM) is capable of storing inputs over a long time period and thus exploiting a self-learned mechanism for long-range temporal context. Considering the complimentary FNN and RNN in their modeling capabilities, this paper presents a deep model which is constructed by stacking LSTM and FNN. Through the cascade of LSTM cells and fully-connected feedforward units, we explore the temporal patterns and summarize the long history of previous inputs in a deep learning machine. The experiments on 3 rd CHiME challenge and Aurora-4 show that the stacks of hybrid model with FNN post-processor outperform stand-alone FNN and LSTM and the other hybrid models for robust speech recognition.

关键词： Hidden Markov models Speech recognition Logic gates Feedforward neural networks Stacking Acoustics Topology

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2D and 3D-integrated image sensor

2D and 3D-integrated image sensor

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Zhe Ming Liu Ming-Chih Lin Chieh Ning Chan Oscal T.-C. Chen Department of Electrical Engineering National Chung Cheng University Chiayi Taiwan

This work develops a 2 Dimension (2D)/ 3 Dimension (3D)-integrated image sensor that adopts the same photodiode under different biased voltages and integrated sensing circuitry. By using the TSMC CMOS technology, four different photodiodes, N-well_P-substrate, P-diffusion_N-well, Pdiffusion_ N-well_P-substrate and N-diffusion_P-substrate, are available. Since P-substrate is used for grounding and P-diffusion_N-well has poor quantum efficiency, only the photodiode of P-diffusion_N-well_P-substrate can be employed by biasing two different voltages to P-diffusion. If the photodiode is driven by little smaller than its breakdown voltage, it functions at the Geiger mode to work for 3D depth sensing. Otherwise, this photodiode performs 2-D gray-level sensing. In the sensing circuitry, a feed-back mechanism is employed to yield a charge supply for a high dynamic range of 2-D sensing, and a feed-back inverter is adopted to boost the processing speed of 3-D sensing. These two feedbacks are effectively integrated in an active pixel to establish 2D/3D image sensor. Based on the TSMC 0.35μm 2P4M technology, a 352×288-pixel 2D/3D image sensor was implemented where its dynamic range can reach 134dB under photocurrents from 10-13A to 5×10-7A and sensing time can be accelerated to 16.3ns. The proposed image sensor can effectively capture 2D and 3D information of an object at the same location without additional alignment and post-processing.

关键词： Image sensors Photodiodes Voltage CMOS technology Dynamic range CMOS image sensors Integrated circuit technology Grounding Integrated circuit yield Inverters

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international Conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. sensors connected through wireless networks make it possible to track the actions of a whole population. sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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