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Electronics, Volume 12, Issue 2 (January-2 2023) – 220 articles

Cover Story (view full-size image): In this study, we investigate an AI-enabled detection method to discover multi-level attacks and malware in Smart Environments. The proposed method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes smart environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on the selected IoT gateways, the result with precise measurement of the power consumption and concurrency testing shows that our framework detects malware and attacks smart environments accurately and efficiently. View this paper
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Article
Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network
Electronics 2023, 12(2), 472; https://doi.org/10.3390/electronics12020472 - 16 Jan 2023
Viewed by 447
Abstract
Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification [...] Read more.
Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
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Article
Linear Active Disturbance Rejection Control for a Laser Powder Bed Fusion Additive Manufacturing Process
Electronics 2023, 12(2), 471; https://doi.org/10.3390/electronics12020471 - 16 Jan 2023
Viewed by 331
Abstract
Functional metal parts with complicated geometry and internal features for the aerospace and automotive industries can be created using the laser powder bed fusion additive manufacturing (AM) technique. However, the lack of uniform quality of the produced parts in terms of strength limits [...] Read more.
Functional metal parts with complicated geometry and internal features for the aerospace and automotive industries can be created using the laser powder bed fusion additive manufacturing (AM) technique. However, the lack of uniform quality of the produced parts in terms of strength limits its enormous potential for general adoption in industries. Most of the defects in selective laser melting (SLM) parts are associated with a nonuniform melt pool size. The melt pool area may fluctuate in spite of constant SLM processing parameters, like laser power, laser speed, hatching distance, and layer thickness. This is due to heat accumulation in the current track from previously scanned tracks in the current layer. The feedback control strategy is a promising tool for maintaining the melt pool dimensions. In this study, a dynamic model of the melt pool cross-sectional area is considered. The model is based on the energy balance of lumped melt pool parameters. Energy coming from previously scanned tracks is considered a source of disturbance for the current melt pool cross-section area in the control algorithm. To track the reference melt pool area and manage the disturbances and uncertainties, a linear active disturbance rejection control (LADRC) strategy is considered. The LADRC control technique is more successful in terms of rapid reference tracking and disturbance rejection when compared to the conventional PID controller. The simulation study shows that an LADRC control strategy presents a 65% faster time response than the PID, a 97% reduction in the steady state error, and a 98% reduction in overshoot. The integral time absolute error (ITAE) performance index shows 95% improvement for reference tracking of the melt pool area in SLM. In terms of reference tracking and robustness, LADRC outperforms the PID controller and ensures that the melt pool size remains constant. Full article
(This article belongs to the Special Issue Feature Papers in Systems & Control Engineering)
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Article
Interactivity Recognition Graph Neural Network (IR-GNN) Model for Improving Human–Object Interaction Detection
Electronics 2023, 12(2), 470; https://doi.org/10.3390/electronics12020470 - 16 Jan 2023
Viewed by 296
Abstract
Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction [...] Read more.
Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction detection algorithms, resulting in inaccurate interaction detection. To recognize invalid human–object interaction pairs, this paper proposes a model structure, the interactivity recognition graph neural network (IR-GNN) model, which can directly infer the probability of human–object interactions from a graph model architecture. The model consists of three modules: The first one is the human posture feature module, which uses key points of the human body to construct relative spatial pose features and further facilitates the discrimination of human–object interactivity through human pose information. Second, a human–object interactivity graph module is proposed. The spatial relationship of human–object distance is used as the initialization weight of edges, and the graph is updated by combining the message passing of attention mechanism so that edges with interacting node pairs obtain higher weights. Thirdly, the classification module is proposed; by finally using a fully connected neural network, the interactivity of human–object pairs is binarily classified. These three modules work in collaboration to enable the effective inference of interactive possibilities. On the datasets HICO-DET and V-COCO, comparative and ablation experiments are carried out. It has been proved that our technology can improve the detection of human–object interactions. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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Article
A Novel Framework for Classification of Different Alzheimer’s Disease Stages Using CNN Model
Electronics 2023, 12(2), 469; https://doi.org/10.3390/electronics12020469 - 16 Jan 2023
Viewed by 382
Abstract
Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the [...] Read more.
Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models. Full article
(This article belongs to the Special Issue Advances in Fuzzy and Intelligent Systems)
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Article
A 10 GHz Compact Balun with Common Inductor on CMOS Process
Electronics 2023, 12(2), 468; https://doi.org/10.3390/electronics12020468 - 16 Jan 2023
Viewed by 268
Abstract
This paper presents a compact balun with a common inductor design. The design used Wilkinson-type balun topology with modified lumped transmission lines and a common inductor to realize circuit size reduction on a lossy CMOS process. Measurements of the prototype chip had a [...] Read more.
This paper presents a compact balun with a common inductor design. The design used Wilkinson-type balun topology with modified lumped transmission lines and a common inductor to realize circuit size reduction on a lossy CMOS process. Measurements of the prototype chip had a reflection coefficient below 17.8 dB at all ports, an insertion loss of 1.98 dB, and an isolation of 16.8 dB. The chip size was only 0.025λ0 × 0.034λ0. Full article
(This article belongs to the Special Issue Microwave Subsystems and Wireless Propagation)
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Article
Efficient Training on Alzheimer’s Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification
Electronics 2023, 12(2), 467; https://doi.org/10.3390/electronics12020467 - 16 Jan 2023
Viewed by 479
Abstract
Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training [...] Read more.
Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By the 3D-to-2D conversion, the proposed model can easily forward the fused 2D image through a pre-trained 2D model while achieving better performance over different 3D and 2D baselines. In the implementation, we chose to use ResNet34 for feature extraction as it outperformed other 2D CNN backbones. We further showed that the weights of the slices are location-dependent and the model performance relies on the 3D-to-2D fusion view, with the best outcomes from the coronal view. With the new approach, we were able to reduce 75% of the training time and increase the accuracy to 0.88, compared with conventional 3D CNNs, for classifying amyloid-beta PET imaging from the AD patients from the CU participants using the publicly available Alzheimer’s Disease Neuroimaging Initiative dataset. The novel 3D-2D model may have profound implications for timely AD diagnosis in clinical settings in the future. Full article
(This article belongs to the Special Issue Medical Image Processing Using AI)
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Article
Efficient Image Segmentation of Cardiac Conditions after Basketball Using a Deep Neural Network
by and
Electronics 2023, 12(2), 466; https://doi.org/10.3390/electronics12020466 - 16 Jan 2023
Viewed by 340
Abstract
The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images [...] Read more.
The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images of the right ventricle. In this system, the right ventricle of the heart is segmented using an improved model called RAU-Net. The sensitivity and specificity of the network are enhanced by improving the loss function. We adopted an extended convolution rather than ordinary convolution to increase the receptive field of the network. In the network-sampling phase, we introduce an attention module to improve the accuracy of network segmentation. In the encoding and decoding stages, we also introduce three residual modules to solve the gradient explosion problem. The results of experiments are provided to show that the proposed algorithm exhibited better segmentation accuracy than an existing algorithm. Moreover, the algorithm can also be trained more rapidly and efficiently. Full article
(This article belongs to the Special Issue Efficient Machine Learning for the Internet of Things)
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Article
Pre-Layout Parasitic-Aware Design Optimizing for RF Circuits Using Graph Neural Network
Electronics 2023, 12(2), 465; https://doi.org/10.3390/electronics12020465 - 16 Jan 2023
Viewed by 403
Abstract
The performance of analog and RF circuits is widely affected by the interconnection parasitic in the circuit. With the progress of technology, interconnection parasitics plays a larger role in performance deterioration. To solve this problem, designers must repeat layout design and validation process. [...] Read more.
The performance of analog and RF circuits is widely affected by the interconnection parasitic in the circuit. With the progress of technology, interconnection parasitics plays a larger role in performance deterioration. To solve this problem, designers must repeat layout design and validation process. In order to achieve an upgrade in the design efficiency, in this paper, a Graph Neural Network (GNN)-based pre-layout parasitic parameter prediction method is proposed and applied to the design optimization of a 28 nm PLL. With the new method adopted, the frequency band overlap rate of the VCO is improved by 2.3 percents for an equal design effort. Similarly, the optimized CP is superior to the traditional method with a 15 ps mismatch time. These improvements are achieved under the premise of greatly saving the optimization iteration and verification costs. Full article
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Article
Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
Electronics 2023, 12(2), 464; https://doi.org/10.3390/electronics12020464 - 16 Jan 2023
Viewed by 471
Abstract
Dual-Core Lockstep (DCLS) is one of the most commonly used techniques in applications requiring functional safety. As the semiconductor process nodes keep shrinking, the DCLS technique is also more and more frequently seen in industrial or even consumer electronics. The paper presents the [...] Read more.
Dual-Core Lockstep (DCLS) is one of the most commonly used techniques in applications requiring functional safety. As the semiconductor process nodes keep shrinking, the DCLS technique is also more and more frequently seen in industrial or even consumer electronics. The paper presents the novel approach to the DCLS technique. While the typical approach is to set the slave core delay as a fixed number of clock cycles, we allow the checker core to run freely behind the main core within the constrained boundaries of clock cycles. This increases the temporal diversity needed for common mode failure mitigation. The system integrity provided by DCLS may also be used in the area of security applications. In this paper, we show that the proposed Variable Delayed Dual-Core Lockstep technique can flatten the power consumption correlation between the running cores, essential for a wide range of attacks. The proposed technique was implemented in the RISC-V processor core and verified in the Xilinx VCU108 FPGA platform. Full article
(This article belongs to the Special Issue Design, Fabrication and Testing of Integrated Circuits and Systems)
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Article
A CC-Type IPT System Based on S/S/N Three-Coil Structure to Realize Low-Cost and Compact Receiver
Electronics 2023, 12(2), 463; https://doi.org/10.3390/electronics12020463 - 16 Jan 2023
Viewed by 371
Abstract
The characteristics of load-independent constant current (CC) output and zero phase angle (ZPA) operation are required in many scenarios of inductive power transfer (IPT) applications. However, the existing topologies with CC output characteristics usually need to introduce additional compensation components on the receiving [...] Read more.
The characteristics of load-independent constant current (CC) output and zero phase angle (ZPA) operation are required in many scenarios of inductive power transfer (IPT) applications. However, the existing topologies with CC output characteristics usually need to introduce additional compensation components on the receiving side to compensate for reactive power and achieve the preset function. This not only increases the occupied space of the receiving side, but also increases the cost and weight. Therefore, this study proposes a new IPT system based on an S/S/N three-coil structure. The proposed system can achieve the CC output function and an operation nearly ZPA and zero-voltage switching (ZVS) through flexible parameter design. Moreover, there are no compensation components on the receiving side of the proposed system, which guarantees a low-cost, lightweight, and compact receiver. Firstly, a comprehensive analysis of the proposed S/S/N three-coil structure IPT system that implements CC output characteristics and ZPA operation is provided. Then, the conditions for realizing ZVS are discussed in terms of parameter design and the sensitivity of CC output characteristics to the changes in compensation capacitance parameters. Furthermore, the proposed S/S/N three-coil structure IPT system is compared with previous related studies to reflect its advantages. Finally, the correctness of the theory is verified by simulation and experiment. Full article
(This article belongs to the Special Issue Advanced RF, Microwave Engineering, and High-Power Microwave Sources)
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Article
Packet Loss Optimization in Router Forwarding Tasks Based on the Particle Swarm Algorithm
Electronics 2023, 12(2), 462; https://doi.org/10.3390/electronics12020462 - 16 Jan 2023
Viewed by 380
Abstract
Software-defined networks (SDNs) are computer networks where parameters and devices are configured by software. Recently, artificial intelligence aspects have been used for SDN programs for various applications, including packet classification and forwarding according to the quality of service (QoS) requirements. The main problem [...] Read more.
Software-defined networks (SDNs) are computer networks where parameters and devices are configured by software. Recently, artificial intelligence aspects have been used for SDN programs for various applications, including packet classification and forwarding according to the quality of service (QoS) requirements. The main problem is that when packets from different applications pass through computer networks, they have different QoS criteria. To meet the requirements of packets, routers classify these packets, add them to multiple weighting queue systems, and forward them according to their priorities. Multiple queue systems in routers usually use a class-based weighted round-robin (CBWRR) scheduling algorithm with pre-configured fixed weights for each priority queue. The problem is that the intensity of traffic in general and of each packet class occasionally changes. Therefore, in this work, we suggest using the particle swarm optimization algorithm to find the optimal weights for the weighted fair round-robin algorithm (WFRR) by considering the variable densities of the traffic. This work presents a framework to simulate router operations by determining the weights and schedule packets and forwarding them. The proposed algorithm to optimize the weights is compared with the conventional WFRR algorithm, and the results show that the particle swarm optimization for the weighted round-robin algorithm is more efficient than WFRR, especially in high-intensity traffic. Moreover, the average packet-loss ratio does not exceed 7%, and the proposed algorithms are better than the conventional CBWRR algorithm and the related work results. Full article
(This article belongs to the Section Networks)
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Article
A Composite Pipeline for Forwarding Low-Latency Traffic in SDN Programmable Data Planes
Electronics 2023, 12(2), 461; https://doi.org/10.3390/electronics12020461 - 16 Jan 2023
Viewed by 342
Abstract
With the rapid evolution of network technologies over recent years, emerging network services, especially industrial control networks, video conferencing, intelligent driving, and other scenarios, have put forward higher demand for the low-latency forwarding of network traffic. The existing flow caching and hardware acceleration [...] Read more.
With the rapid evolution of network technologies over recent years, emerging network services, especially industrial control networks, video conferencing, intelligent driving, and other scenarios, have put forward higher demand for the low-latency forwarding of network traffic. The existing flow caching and hardware acceleration methods only improve the overall forwarding performance of data-plane devices but cannot separate the forwarding process of low-latency traffic from others to reflect the priority of these flows. In this paper, we extend the POF southbound interface protocol and propose a marking method for low-latency flows, based on which we design a composite pipeline to achieve fast processing for low-latency traffic by introducing a fast-forwarding path. The experiments show that the fast path has a higher forwarding capability than the MAT pipeline in the POF Switch and can reduce the forwarding delay of low-latency flows by 62–68%. In a real network environment with a mixed traffic simulation, the reduction reaches 17–20% with no delay increment for the non-low-latency part. Full article
(This article belongs to the Section Networks)
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Article
A Novel Virus Capable of Intelligent Program Infection through Software Framework Function Recognition
Electronics 2023, 12(2), 460; https://doi.org/10.3390/electronics12020460 - 16 Jan 2023
Viewed by 328
Abstract
Viruses are one of the main threats to the security of today’s cyberspace. With the continuous development of virus and artificial intelligence technologies in recent years, the intelligentization of virus technology has become a trend. It is of urgent significance to study and [...] Read more.
Viruses are one of the main threats to the security of today’s cyberspace. With the continuous development of virus and artificial intelligence technologies in recent years, the intelligentization of virus technology has become a trend. It is of urgent significance to study and combat intelligent viruses. In this paper, we design a new type of confirmatory virus from the attacker’s perspective that can intelligently infect software frameworks. We aim for structural software as the target and use BCSD (binary code similarity detection) to identify the framework. By incorporating a software framework functional structure recognition model in the virus, the virus is enabled to intelligently recognize software framework functions in executable files. This paper evaluates the BCSD model that is suitable for a virus to carry and constructs a lightweight BCSD model with a knowledge distillation technique. This research proposes a software framework functional structure recognition algorithm, which effectively reduces the recognition precision’s dependence on the BCSD model. Finally, this study discusses the next researching direction of intelligent viruses. This paper aims to provide a reference for the research of detection technology for possible intelligent viruses. Consequently, focused and effective defense strategies could be proposed and the technical system of malware detection could be reinforced. Full article
(This article belongs to the Special Issue AI in Cybersecurity)
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Article
Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search
Electronics 2023, 12(2), 459; https://doi.org/10.3390/electronics12020459 - 16 Jan 2023
Viewed by 335
Abstract
In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology [...] Read more.
In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance. Full article
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Review
Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
Electronics 2023, 12(2), 458; https://doi.org/10.3390/electronics12020458 - 16 Jan 2023
Viewed by 472
Abstract
The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a [...] Read more.
The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Real World)
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Article
Machine Learning Approach towards LoRaWAN Indoor Localization
Electronics 2023, 12(2), 457; https://doi.org/10.3390/electronics12020457 - 16 Jan 2023
Viewed by 561
Abstract
The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, [...] Read more.
The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, processing power, and energy consumption. This paper introduces a system for device indoor localization that uses variations in the strength of the wireless signal. The proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceutic delivery, delivery of confidential documents and court exhibits, and even food, since the same is introduced into human organism and presents a potential risk of terrorist or other attack. This work proposes a concept based on low-power and low-cost LoRaWAN based system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. Furthermore, using signal strength measurements, that is, RSSI and SNR captured by LoRaWAN gateways, it is possible to estimate the location of the device point with an accuracy of up to 98.8%. Full article
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Article
Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System
Electronics 2023, 12(2), 456; https://doi.org/10.3390/electronics12020456 - 15 Jan 2023
Viewed by 566
Abstract
The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency [...] Read more.
The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective. Full article
(This article belongs to the Section Circuit and Signal Processing)
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Article
Photonic Multiple Microwave Frequency Measurement System with Single-Branch Detection Based on Polarization Interference
Electronics 2023, 12(2), 455; https://doi.org/10.3390/electronics12020455 - 15 Jan 2023
Viewed by 299
Abstract
A photonic microwave frequency measurement system with single-branch detection based on polarization interference is proposed. In this scheme, a 15-line non-flat optical frequency comb (OFC) based on sawtooth signal modulation via a Mach–Zehnder modulator is generated. The intercepted microwave signal with multiple-frequency components [...] Read more.
A photonic microwave frequency measurement system with single-branch detection based on polarization interference is proposed. In this scheme, a 15-line non-flat optical frequency comb (OFC) based on sawtooth signal modulation via a Mach–Zehnder modulator is generated. The intercepted microwave signal with multiple-frequency components can be measured by frequency down-conversion with this simple structure. This system can measure the multi-tone microwave signals in real time. The single-branch detection makes the system a simple and compact structure and avoids the unbalanced variation, as in a two-branches scheme. The blind area of the system can be solved by adjusting the comb-line spacing of the OFC. A simulation is carried out and related discussion is given. The result reveals that it can measure multi-tone microwave signals with a resolution of less than 2 MHz over 0.1–12 GHz. Full article
(This article belongs to the Special Issue Optical Fiber Communications: Innovations and Challenges)
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Article
Online Education Management: A Multivariate Analysis of Students’ Perspectives and Challenges during Online Classes
Electronics 2023, 12(2), 454; https://doi.org/10.3390/electronics12020454 - 15 Jan 2023
Viewed by 340
Abstract
The aim of the present study is to find solutions for better management of online education, starting from students’ perspectives regarding the challenges they encountered in the last two years when online courses were imposed during the COVID-19 pandemic. The research methodology we [...] Read more.
The aim of the present study is to find solutions for better management of online education, starting from students’ perspectives regarding the challenges they encountered in the last two years when online courses were imposed during the COVID-19 pandemic. The research methodology we used was partial least squares structural equation modelling based on data collected by applying a survey among students in Romanian universities. The novelty of our study consists in the proposed model, which has five variables: communication problems specific to online education, professors’ skill in conducting online classes, the quality of online education, the stress felt by students during online education, and the technical requirements of online education. The results revealed that despite challenges during online classes students benefited from a high-quality education because they had the support of their professors, all the educational resources that they needed, a device to connect from, and a very good internet connection. These findings are helpful for managers in the higher education system to create better educational strategies meant to satisfy the educational needs of students in the digital age. Full article
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Article
A New Varactor-Tuned 5.8 GHz Dielectric Resonator Band-Stop Filter for ITS and C-V2X Coexistence with Vehicular DSRC
Electronics 2023, 12(2), 453; https://doi.org/10.3390/electronics12020453 - 15 Jan 2023
Viewed by 260
Abstract
In this paper, we introduce an electronically tuneable band-stop filter based on a dielectric resonator, along with its design principles and equivalent circuit model aimed at the coexistence of intelligent transport systems (ITS) and cellular vehicle-to-everything (C-V2X) communications operating at 5.9 GHz, with [...] Read more.
In this paper, we introduce an electronically tuneable band-stop filter based on a dielectric resonator, along with its design principles and equivalent circuit model aimed at the coexistence of intelligent transport systems (ITS) and cellular vehicle-to-everything (C-V2X) communications operating at 5.9 GHz, with the widely spread vehicular dedicated short range communications (DSRC) at 5.8 GHz. The proposed architecture involves a dielectric resonator coupled via a microstrip transmission line to a planar ring resonator. The resonance is electronically tuned by varying the bias voltage of a varactor diode placed in the ring. This design is analyzed theoretically and experimentally. For validation, a filter operating at 5.8 GHz was designed. The prototype is capable of enhancing coexistence exhibiting to features. First, when inserted into the ITS transmitter chain, it reduces unwanted transmitter emissions in the out-of-band spectrum occupied by the DSRC carriers in the 5.8 GHz frequency band of at least 25 dB, with a minimal insertion loss at 5. 9 GHz of 1.6 dB; secondly, when inserted in the ITS receiver chain, it exhibits high linearity that prevents the generation of intermodulation products falling into the filter pass band. The prototype features more than 21 dBm of third-order input intercept points, and a tuning range of 26 MHz while maintaining a minimal loaded Q factor of 93. The prototype is discussed regarding the detailed equivalent circuit parameter extraction and their dependency upon the control voltage. Full article
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Article
Design and Implementation of an Efficient Hardware Coprocessor IP Core for Multi-axis Servo Control Based on Universal SoC
Electronics 2023, 12(2), 452; https://doi.org/10.3390/electronics12020452 - 15 Jan 2023
Viewed by 310
Abstract
The multi-axis servo control system has been extensively used in industrial control. However, the applications of traditional MCU and DSP chips in high-performance multi-axis servo control systems are becoming increasingly difficult due to their lack of computing power. Although FPGA chips can meet [...] Read more.
The multi-axis servo control system has been extensively used in industrial control. However, the applications of traditional MCU and DSP chips in high-performance multi-axis servo control systems are becoming increasingly difficult due to their lack of computing power. Although FPGA chips can meet the computing power requirements of high-performance multi-axis servo control systems, their versatility is insufficient, and the chip is too costly for large-scale use. Therefore, when designing the universal SoC, it is better to directly embed the coprocessor IP core dedicated to accelerating the multi-motor vector control current loop operation into the universal SoC. In this study, a coprocessor IP core that can be flexibly embedded in a universal SoC was designed. The IP core based on time division multiplexing (TDM) technology could accelerate the multi-motor vector control current loop operation according to the hardware–software coordination scheme proposed in this study. The IP was first integrated into a universal SoC to verify its performance, and then the FPGA prototype verification for the SoC was performed under three-axis servo control systems. Secondly, the ASIC implementation of the IP was also conducted based on the CSMC 90 nm process library. The experimental results revealed that the IP had a small area and low power consumption and was suitable for application in universal SoC. Therefore, the cheap and low-power single universal SoC with the coprocessor IP can be suitable for multi-axis servo control. Full article
(This article belongs to the Special Issue Application of Power Electronics Technology in Energy System)
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Article
Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network
Electronics 2023, 12(2), 451; https://doi.org/10.3390/electronics12020451 - 15 Jan 2023
Viewed by 473
Abstract
A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and [...] Read more.
A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency. Full article
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Article
An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform
Electronics 2023, 12(2), 450; https://doi.org/10.3390/electronics12020450 - 15 Jan 2023
Viewed by 360
Abstract
Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without [...] Read more.
Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of 0.0625 bits per pixel, outperforming many related RDH schemes with complete reversibility. Full article
(This article belongs to the Special Issue Machine Learning: Practical Applications for Cybersecurity)
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Article
Saliency Detection Based on Low-Level and High-Level Features via Manifold-Space Ranking
Electronics 2023, 12(2), 449; https://doi.org/10.3390/electronics12020449 - 15 Jan 2023
Viewed by 498
Abstract
Saliency detection as an active research direction in image understanding and analysis has been studied extensively. In this paper, to improve the accuracy of saliency detection, we propose an efficient unsupervised salient object detection method. The first step of our method is that [...] Read more.
Saliency detection as an active research direction in image understanding and analysis has been studied extensively. In this paper, to improve the accuracy of saliency detection, we propose an efficient unsupervised salient object detection method. The first step of our method is that we extract local low-level features of each superpixel after segmenting the image into different scale parts, which helps to locate the approximate locations of salient objects. Then, we use convolutional neural networks to extract high-level, semantically rich features as complementary features of each superpixel, and low-level features, as well as high-level features of each superpixel, are incorporated into a new feature vector to measure the distance between different superpixels. The last step is that we use a manifold space-ranking method to calculate the saliency of each superpixel. Extensive experiments over four challenging datasets indicate that the proposed method surpasses state-of-the-art methods and is closer to the ground truth. Full article
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Article
Descriptive Markers for the Cognitive Profiling of Desktop 3D Spaces
Electronics 2023, 12(2), 448; https://doi.org/10.3390/electronics12020448 - 15 Jan 2023
Viewed by 324
Abstract
3D virtual reality spaces, whether running on desktop environments or on immersive displays, have been noted to support a radically new and highly stimulating way of working with digital content in a variety of application domains. At the same time, research in recent [...] Read more.
3D virtual reality spaces, whether running on desktop environments or on immersive displays, have been noted to support a radically new and highly stimulating way of working with digital content in a variety of application domains. At the same time, research in recent decades has produced a number of experimental results showing that the use of 3D, as opposed to 2D interfaces, can lead to performance improvements from a wide range of aspects, including the ability to comprehend and retain knowledge, ability to work collaboratively in more creative and effective ways, and ability to carry out workflows integrating numerous sources of information in less time. In this paper, we first review the relevant literature; then, we describe an exploratory study that we carried out with test subjects, both in a 3D desktop virtual environment and in a 2D web-based environment, while collecting eye tracking data. In the study, subjects were presented with a set of multimedia content on a range of topics within the field of astronomy, based on which they were subsequently asked to fill out a set of questionnaires. By comparing the 2D and 3D cases in terms of correctness of answers, time taken to perform the task, pupil dilation measurements, subjects’ self-reported difficulty assessments, as well as various kinds of high-level interaction patterns employed during the task (in 3D), we were able to identify a set of descriptive markers which may be relevant to the prediction of users’ effectiveness in virtual reality workspaces. In a weaker sense, the results also seem to support previous research works claiming improved effectiveness in 3D spaces compared to 2D web-based interfaces, although further work is needed to more clearly identify the constraints within which such benefits can be guaranteed. Full article
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Communication
A Reliability-Enhanced Differential Sensing Amplifier for Hybrid CMOS/MTJ Logic Circuits
Electronics 2023, 12(2), 447; https://doi.org/10.3390/electronics12020447 - 15 Jan 2023
Viewed by 300
Abstract
Recently, hybrid logic circuits based on magnetic tunnel junctions (MTJs) have been widely investigated to realize zero standby power. However, such hybrid CMOS/MTJ logic circuits suffer from a severe sensing reliability due to the limited tunnel magnetoresistance ratio (TMR ≤ 150%) of the [...] Read more.
Recently, hybrid logic circuits based on magnetic tunnel junctions (MTJs) have been widely investigated to realize zero standby power. However, such hybrid CMOS/MTJ logic circuits suffer from a severe sensing reliability due to the limited tunnel magnetoresistance ratio (TMR ≤ 150%) of the MTJ and the large process variation in the deep sub-micrometer technology node. In this paper, a novel differential sensing amplifier (DSA) is proposed, in which two PMOS transistors are added to connect the discharging branches and evaluation branches. Owing to the positive feedback realized by these two added PMOS transistors, it can achieve a large sensing margin. By using an industrial CMOS 40 nm design kit and a physics-based MTJ compact model, hybrid CMOS/MTJ simulations have been performed to demonstrate its functionality and evaluate its performance. Simulation results show that it can achieve a smaller sensing error rate of 9% in comparison with the previously proposed DSAs with a TMR ratio of 100% and process variation of 10%, while maintaining almost the same sensing delay of 74.5 ps and sensing energy of 1.92 fJ/bit. Full article
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Article
Design of Generalized Enhanced Static Segment Multiplier with Minimum Mean Square Error for Uniform and Nonuniform Input Distributions
Electronics 2023, 12(2), 446; https://doi.org/10.3390/electronics12020446 - 15 Jan 2023
Viewed by 314
Abstract
In this paper, we analyze the performances of an Enhanced Static Segment Multiplier (ESSM) when the inputs have both uniform and non-uniform distribution. The enhanced segmentation divides the multiplicands into a lower, a middle, and an upper segment. While the middle segment is [...] Read more.
In this paper, we analyze the performances of an Enhanced Static Segment Multiplier (ESSM) when the inputs have both uniform and non-uniform distribution. The enhanced segmentation divides the multiplicands into a lower, a middle, and an upper segment. While the middle segment is placed at the center of the inputs in other implementations, we seek the optimal position able to minimize the approximation error. To this aim, two design parameters are exploited: m, defining the size and the accuracy of the multiplier, and q, defining the position of the middle segment for further accuracy tuning. A hardware implementation is proposed for our generalized ESSM (gESSM), and an analytical model is described, able to find m and q which minimize the mean square approximation error. With uniform inputs, the error slightly improves by increasing q, whereas a large error decrease is observed by properly choosing q when the inputs are half-normal (with a NoEB up to 18.5 bits for a 16-bit multiplier). Implementation results in 28 nm CMOS technology are also satisfactory, with area and power reductions up to 71% and 83%. We report image and audio processing applications, showing that gESSM is a suitable candidate in applications with non-uniform inputs. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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Article
Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China
Electronics 2023, 12(2), 445; https://doi.org/10.3390/electronics12020445 - 14 Jan 2023
Viewed by 314
Abstract
Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the [...] Read more.
Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the fuel consumption, output power and exhaust performance of automobiles, the control strategy has become a research hotspot and focus in automobile R&D industry. Therefore, based on the relevant research results in recent years, after studying and analyzing the typical control strategies of hybrid vehicles, this paper finally puts forward the energy management strategy of hybrid vehicles based on model predictive control (MPC), and strives to contribute to the academic research of energy management strategies of hybrid vehicles. Full article
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Article
FPGA Implementation of a Chaotic Map with No Fixed Point
Electronics 2023, 12(2), 444; https://doi.org/10.3390/electronics12020444 - 14 Jan 2023
Viewed by 371
Abstract
The employment of chaotic maps in a variety of applications such as cryptosecurity, image encryption schemes, communication schemes, and secure communication has been made possible thanks to their properties of high levels of complexity, ergodicity, and high sensitivity to the initial conditions, mainly. [...] Read more.
The employment of chaotic maps in a variety of applications such as cryptosecurity, image encryption schemes, communication schemes, and secure communication has been made possible thanks to their properties of high levels of complexity, ergodicity, and high sensitivity to the initial conditions, mainly. Of considerable interest is the implementation of these dynamical systems in electronic devices such as field programmable gate arrays (FPGAs) with the intention of experimentally reproducing their dynamics, leading to exploiting their chaotic properties in real phenomena. In this work, the implementation of a one-dimensional chaotic map that has no fixed points is performed on an FPGA device with the objective of being able to reproduce its chaotic behavior as well as possible. The chaotic behavior of the introduced system is determined by estimating the Lyapunov exponents and its chaotic behavior is also analyzed using bifurcation diagrams. Simulations of the system are realized via Matlab, as well as in C and the very high-speed integrated circuit (VHSIC) hardware description language (VHDL). Experimental results on FPGA show that they are like those obtained in the simulations; therefore, this chaotic dynamical system could be used as an element in some encryption schemes such as in the generation of cryptographically secure pseudorandom numbers. Full article
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Article
Design of Vehicle Tunnel Illumination Measurement Device Based on STC12C5A60S2 Single-Chip Microcomputer
Electronics 2023, 12(2), 443; https://doi.org/10.3390/electronics12020443 - 14 Jan 2023
Viewed by 355
Abstract
In order to measure tunnel illumination with high efficiency and accuracy, a vehicle-mounted tunnel illumination measurement device is designed in this paper. The device comprises a measurement module, a control module, a display module, and a power module. The measurement module is composed [...] Read more.
In order to measure tunnel illumination with high efficiency and accuracy, a vehicle-mounted tunnel illumination measurement device is designed in this paper. The device comprises a measurement module, a control module, a display module, and a power module. The measurement module is composed of a BCE illuminance sensor and an inductive proximity switch, which can realize a single illuminance measurement within a fixed distance. The control module, i.e., the STC12C5A60S2 single-chip microcomputer, sends the single measurement data to the storage module to realize dynamic automatic measurement. The display module is an LCD1602 liquid crystal display, which displays the measured tunnel mileage and real-time illumination. The whole device is fed by the powered module. The man–machine exchange interface of the Visual Basic (VB) host computer and Access database are used to display and store the previous illuminance measurement data, respectively. Extensive experiments show that the device has the advantages of a simple structure, convenient installation, stable operation, and accurate and efficient measurement, and can realize an automatic measurement of illumination in a long tunnel. Full article
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