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"Embedded system"
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An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem
Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.
Journal Article
A novel pseudorandom number generator based on pseudorandomly enhanced logistic map
In last years, low-dimensional and high-dimensional chaotic systems have been implemented in cryptography. The efficiency and performance of these nonlinear systems play an important role in limited hardware implementations. In this context, low-dimensional chaotic systems are more attractive than high-dimensional chaotic systems to produce the pseudorandom key stream used for encryption purposes. Although low-dimensional chaotic maps present some security disadvantages when they are used in cryptography, they are highly attractive due its simple structure, discrete nature, less arithmetic operations, high output processing, and relatively easy to implement in a digital system. In this paper, we proposed both a pseudorandomly enhanced logistic map (PELM) and its application in a novel pseudorandom number generator (PRNG) algorithm, which produces pseudorandom stream with excellent statistical properties. The proposed PELM is compared with logistic map by using histograms and Lyapunov exponents to show its higher benefits in pseudorandom number generator. In contrast to recent schemes in the literature, we present a comprehensive security analysis over the proposed pseudorandom number generator based on pseudorandomly enhanced logistic map (PRNG–PELM) from a cryptographic point of view to show its potential use in secure communications. In addition, the randomness of the PRNG–PELM is verified with the most complete random test suit of National Institute of Standards and Technology (NIST 800-22) and with TestU01. Based on security results, few arithmetic operations required, and high output rate, the proposed PRNG–PELM scheme can be implemented in secure encryption applications, even in embedded systems with limited hardware resources.
Journal Article
Smart grid cyber-physical systems: communication technologies, standards and challenges
The recent developments in embedded system design and communication technologies popularized the adaption of the cyber-physical system (CPS) for practical applications. A CPS is an amalgamation of a physical system, a cyber system, and their communication network. The cyber system performs extensive computational operations on the data received from the physical devices, interprets the data, and initiates effective control actions in real-time. One such CPS is the smart grid CPS (SG-CPS) consisting of physical devices with diverse communication requirements, and intermediate communication networks. Thus, reliable communication networks are paramount for the effective operation of the SG-CPS. This paper is an elaborate survey on the communication networks from the perspective of the SG-CPS. This paper presents the state-of-art communication technologies that can meet the communication requirements of the various SG-CPS applications. The communications standards and communication protocols are also comprehensively discussed. A systematic mapping among communication technologies, standards, and protocols for various SG-CPS applications has been presented based on an extensive literature survey in this paper. Furthermore, several challenges, such as security, safety, reliability and resilience, etc., have been addressed from SG-CPS’s perspective. This work also identifies the research gaps in the various domains of the SG-CPS that can be of immense benefit to the research community.
Journal Article
OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO
Computer vision is an interdisciplinary domain for object detection. Object detection relay is a vital part in assisting surveillance, vehicle detection and pose estimation. In this work, we proposed a novel deep you only look once (deep YOLO V3) approach to detect the multi-object. This approach looks at the entire frame during the training and test phase. It followed a regression-based technique that used a probabilistic model to locate objects. In this, we construct 106 convolution layers followed by 2 fully connected layers and 812 × 812 × 3 input size to detect the drones with small size. We pre-train the convolution layers for classification at half the resolution and then double the resolution for detection. The number of filters of each layer will be set to 16. The number of filters of the last scale layer is more than 16 to improve the small object detection. This construction uses up-sampling techniques to improve undesired spectral images into the existing signal and rescaling the features in specific locations. It clearly reveals that the up-sampling detects small objects. It actually improves the sampling rate. This YOLO architecture is preferred because it considers less memory resource and computation cost rather than more number of filters. The proposed system is designed and trained to perform a single type of class called drone and the object detection and tracking is performed with the embedded system-based deep YOLO. The proposed YOLO approach predicts the multiple bounding boxes per grid cell with better accuracy. The proposed model has been trained with a large number of small drones with different conditions like open field, and marine environment with complex background.
Journal Article
A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles
Artificial Intelligence (AI) in the automotive industry allows car manufacturers to produce intelligent and autonomous vehicles through the integration of AI-powered Advanced Driver Assistance Systems (ADAS) and/or Automated Driving Systems (ADS) such as the Traffic Sign Recognition (TSR) system. Existing TSR solutions focus on some categories of signs they recognise. For this reason, a TSR approach encompassing more road sign categories like Warning, Regulatory, Obligatory, and Priority signs is proposed to build an intelligent and real-time system able to analyse, detect, and classify traffic signs into their correct categories. The proposed approach is based on an overview of different Traffic Sign Detection (TSD) and Traffic Sign Classification (TSC) methods, aiming to choose the best ones in terms of accuracy and processing time. Hence, the proposed methodology combines the Haar cascade technique with a deep CNN model classifier. The developed TSC model is trained on the GTSRB dataset and then tested on various categories of road signs. The achieved testing accuracy rate reaches 98.56%. In order to improve the classification performance, we propose a new attention-based deep convolutional neural network. The achieved results are better than those existing in other traffic sign classification studies since the obtained testing accuracy and F1-measure rates achieve, respectively, 99.91% and 99%. The developed TSR system is evaluated and validated on a Raspberry Pi 4 board. Experimental results confirm the reliable performance of the suggested approach.
Journal Article
Simulation Oriented Layer of Embedded Software Architecture for Rapid Development of Custom Embedded Systems Virtual Simulators Used in Didactics
The application of the proposed Simulation Oriented Layer in the embedded-software architecture is shown in this paper. The SOL’s purpose is to deliver only limited and highly desirable microprocessor-system functionality to the Application Layer, which would be implemented in a virtual simulator without requiring its complex development. It was used in two virtual simulators of embedded systems, as presented in the article. Each virtual simulator covers one customized embedded system (RPILAB and TMSLAB) used for didactical purposes. On each embedded platform, a different method of system-functionality simulation was shown. Presented virtual simulators can run recompiled (for the virtual-simulator platform) programs in a seamless process, giving real-like experiences for programmers, who can verify and test their high-level solutions. Being accurately chosen, taken for the simulation because of essential and limited functionality, and used in the Application Layer allowed for the rapid design of the virtual simulators. Unit- and functional-test results using RPILAB- and TMSLAB-embedded systems and their virtual simulators are shown in this paper. Both simulators of real RPILAB and TMSLAB platforms are used with success in the didactical process, at the Institute of Automatic Control in Lodz University of Technology, since the COVID-19 pandemic.
Journal Article
Implementing a chaotic cryptosystem in a 64-bit embedded system by using multiple-precision arithmetic
This paper proposes a new chaotic cryptosystem for the encryption of very high-resolution digital images based on the design of a digital chaos generator by using arbitrary precision arithmetic. This can be taken as an alternative to reduce the dynamic degradation that chaotic models present when they are implemented in digital devices and to increase the security of the cryptosystems. The obtained results show that when using high-precision arithmetic, the generated sequences provide good randomness and security during a greater number of iterations of the implemented chaotic maps in comparison with the generated sequences by using the standard of simple precision or double precision according to the IEEE 754 standard for floating-point arithmetic. The proposed method does not require high-cost hardware for increasing the numerical accuracy and security. As an advantage versus other recent works, using high precision, in relation to the methods that use simple precision or double precision, it awards an exponential increase in the key space. In this manner, it is demonstrated that using multiple-precision arithmetic, a key space of
2
33
,
268
or higher can be obtained, depending on the level of high precision configured. The security analysis confirms that the proposed chaotic cryptosystem is secure and robust against several known attacks, as well as statistical tests of NIST and TestU01, proving that high-precision arithmetic helps to enhance the security of the cryptosystems.
Journal Article
Wearable Fall Detector Using Recurrent Neural Networks
Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
Journal Article