• Title/Summary/Keyword: binary sensor network

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Design and Implementation of BNN-based Gait Pattern Analysis System Using IMU Sensor (관성 측정 센서를 활용한 이진 신경망 기반 걸음걸이 패턴 분석 시스템 설계 및 구현)

  • Na, Jinho;Ji, Gisan;Jung, Yunho
    • Journal of Advanced Navigation Technology
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    • v.26 no.5
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    • pp.365-372
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    • 2022
  • Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

MDA-SMAC: An Energy-Efficient Improved SMAC Protocol for Wireless Sensor Networks

  • Xu, Donghong;Wang, Ke
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.4754-4773
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    • 2018
  • In sensor medium access control (SMAC) protocol, sensor nodes can only access the channel in the scheduling and listening period. However, this fixed working method may generate data latency and high conflict. To solve those problems, scheduling duty in the original SMAC protocol is divided into multiple small scheduling duties (micro duty MD). By applying different micro-dispersed contention channel, sensor nodes can reduce the collision probability of the data and thereby save energy. Based on the given micro-duty, this paper presents an adaptive duty cycle (DC) and back-off algorithm, aiming at detecting the fixed duty cycle in SMAC protocol. According to the given buffer queue length, sensor nodes dynamically change the duty cycle. In the context of low duty cycle and low flow, fair binary exponential back-off (F-BEB) algorithm is applied to reduce data latency. In the context of high duty cycle and high flow, capture avoidance binary exponential back-off (CA-BEB) algorithm is used to further reduce the conflict probability for saving energy consumption. Based on the above two contexts, we propose an improved SMAC protocol, micro duty adaptive SMAC protocol (MDA-SMAC). Comparing the performance between MDA-SMAC protocol and SMAC protocol on the NS-2 simulation platform, the results show that, MDA-SMAC protocol performs better in terms of energy consumption, latency and effective throughput than SMAC protocol, especially in the condition of more crowded network traffic and more sensor nodes.

Low area field-programmable gate array implementation of PRESENT image encryption with key rotation and substitution

  • Parikibandla, Srikanth;Alluri, Sreenivas
    • ETRI Journal
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    • v.43 no.6
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    • pp.1113-1129
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    • 2021
  • Lightweight ciphers are increasingly employed in cryptography because of the high demand for secure data transmission in wireless sensor network, embedded devices, and Internet of Things. The PRESENT algorithm as an ultralightweight block cipher provides better solution for secure hardware cryptography with low power consumption and minimum resource. This study generates the key using key rotation and substitution method, which contains key rotation, key switching, and binary-coded decimal-based key generation used in image encryption. The key rotation and substitution-based PRESENT architecture is proposed to increase security level for data stream and randomness in cipher through providing high resistance to attacks. Lookup table is used to design the key scheduling module, thus reducing the area of architecture. Field-programmable gate array (FPGA) performances are evaluated for the proposed and conventional methods. In Virtex 6 device, the proposed key rotation and substitution PRESENT architecture occupied 72 lookup tables, 65 flip flops, and 35 slices which are comparably less to the existing architecture.

Emotion Recognition using Short-Term Multi-Physiological Signals

  • Kang, Tae-Koo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.3
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    • pp.1076-1094
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    • 2022
  • Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

Improved Target Localization Using Line Fitting in Distributed Sensor Network of Detection-Only Sensor (탐지만 가능한 센서로 구성된 분산센서망에서 라인피팅을 이용한 표적위치 추정기법의 성능향상)

  • Ryu, Chang Soo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.49 no.9
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    • pp.362-369
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    • 2012
  • Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

Noise Mitigation for Target Tracking in Wireless Acoustic Sensor Networks

  • Kim An, Youngwon;Yoo, Seong-Moo;An, Changhyuk;Wells, Earl
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.5
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    • pp.1166-1179
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    • 2013
  • In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

A neural network method for recognition of part orientation in a bowl feeder (보울 피이더에서 신경 회로망을 이용한 부품 자세 인식에 관한 연구)

  • 임태균;김종형;조형석;김성권
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.275-280
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    • 1990
  • A neural network method is applied for recognizing the orientation o f individual parts being fed from a bowl feeder. The system is designed in such a way that a part can be discriminated and sorting according to every possible stable orientation without implementing any a mechanical tooling. The operation of the bowl feeder is based on a 2D image obtained from an array of fiber optic sensor located on the feeder track. The acquired binary image of a moving and vibrating part is used as input to a neural network which, in turn, determines t he orientation of the part. The main task of the neural network, here is to synthesize the appropriate internal discriminant functions for the part orientation using the part features. A series of the experiments reveals several promising points on performance. Since the operation of the feeder is highly programmable, it is well suited for feeding and sorting small parts prior to small batch assembly work.

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TPC-BS: Transmission Power Control based on Binary Search in the Wireless Sensor Networks (TPC-BS: 센서 네트워크에서 이진검색 방법을 이용한 빠른 전송전력 결정 방법)

  • Oh, Seung-Hyun
    • Journal of Korea Multimedia Society
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    • v.14 no.11
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    • pp.1420-1430
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    • 2011
  • This paper proposes a new method to optimize energy consumption in a wireless modem by setting up a transmission power value according to the distance between nodes and circumstance in the MAC layer of IEEE 802.15.4. The proposed method can dynamically find an optimal transmission power range using the binary search scheme and minimize overhead caused by multiple message transmissions when determining the optimal transmission power. The determined transmission power is used for transmitting data packets and can be modified dynamically depending on the changes in a network environment when exchanging data packets and acknowledgement signals. The results of the simulations show 30% reduction in energy consumption while 2.5 times increase in data transmission rate per unit of energy comparing with IEEE 802.15.4 standard.

Intruder Detection System Based on Pyroelectric Infrared Sensor (PIR 센서 기반 침입감지 시스템)

  • Jeong, Yeon-Woo;Vo, Huynh Ngoc Bao;Cho, Seongwon;Cuhng, Sun-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.5
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    • pp.361-367
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    • 2016
  • The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

Real-Time Fault Detection in Discrete Manufacturing Systems Via LSTM Model based on PLC Digital Control Signals (PLC 디지털 제어 신호를 통한 LSTM기반의 이산 생산 공정의 실시간 고장 상태 감지)

  • Song, Yong-Uk;Baek, Sujeong
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.2
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    • pp.115-123
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    • 2021
  • A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.