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A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device. [ABSTRACT FROM AUTHOR]

March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. However, studies show that March algorithms with complexities lower than 18N have poor coverages of faults that have emerged with the advent of the nanometer process technologies and are more relevant to nowadays memories. New March AZ1 and March AZ2 algorithms, with 13N and 14N complexities, respectively, were introduced to provide optimum coverage of those faults and to produce a shorter test than an 18N-complexity test algorithm with a lesser area overhead, thus reducing chip manufacturing costs. This paper presents the implementation and validation of MBIST controllers that applied the March AZ1 and March AZ2 algorithms in a Field-Programmable Gate Array (FPGA) device. They were implemented in the Intel Max 10 DE10-Lite FPGA Development Board. A test generator was built in FPGA, as an alternative to the external tester, to provide test vectors required in initiating the test on the memory model using the implemented MBIST. The FPGA experimental tests demonstrated that they function correctly as the expected test sequences were observed. In addition, their fault detection abilities were also validated through tests on a fault-injected memory model, which shows that the implemented March AZ1 and March AZ2 provide 80.6% and 83.3% coverage of the intended faults, respectively, which outperform any other existing 14N-complexity March algorithms. [ABSTRACT FROM AUTHOR]

The technology for path planning of independent mobile robots is mature, but multi-robot path planning for unified scheduling and allocation is much more complex than single-robot path planning. This requires consideration of collision problems between robots, general optimal path problems, etc. This paper proposes the use of the BSO algorithm for unified scheduling and allocation of multiple robots to improve the efficiency of task execution. The BSO algorithm is a new type of intelligent optimization algorithm that uses clustering ideas to search for local optimal solutions and obtains global optimal solutions by comparing local optimal solutions. It also uses mutation ideas to increase the diversity of the algorithm and avoid becoming trapped in local optimal solutions. Using the GA/SA algorithm and the proposed BSO algorithm for computer simulation comparison, we obtained the optimal path planning for the three robots under unified scheduling. The total distance of the optimal path obtained by the BSO algorithm was 27.36% and 25.31% shorter than those of the GA and SA algorithms, respectively. To further test the performance of the BSO algorithm, we conducted additional experiments on the unified scheduling of multiple robots. The experimental results show that the proposed BSO algorithm can significantly improve the efficiency. The multi-robot under unified scheduling performs point-to-point path planning without collisions, and they can traverse all task target points in the shortest path without repetition. This algorithm is suitable for multi-robot tasks in large-scale environments. [ABSTRACT FROM AUTHOR]

Twin delayed deep deterministic (TD3) policy gradient has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment, due to the complexity of the robot path planning task, the rate of convergence of TD3 algorithm is slow and the rate of collision is high. To address this problem, deep dense dueling twin delayed deep deterministic (D3-TD3) architecture is proposed, a method that preserves important information from cross-layer inputs through dense connections and divides the network into a value function and a dominance function, thus, allowing for faster convergence when solving complex tasks. Finally, a spatial model based on three-dimension (3D) point cloud is built, and simulation experimental results show that in static environment, the algorithm proposed in the paper has 40.6% fewer collisions compared to TD3, 30% fewer collisions compared to TD3-BC, 19.2% fewer collisions compared to Dueling TD3 and 17.4% fewer collisions compared to deep dense TD3. In dynamic and static environment, the algorithm proposed in the paper has 34.4% fewer collisions compared to TD3, 24% fewer collisions compared to TD3-BC, 6% fewer collisions compared to Dueling TD3 and 25% fewer collisions compared to deep dense TD3. [ABSTRACT FROM AUTHOR]

2R-1C equivalent circuit is widely used in electrochemical impedance spectroscopy (EIS), such as modelling of lithium-ion batteries, bioimpedance analysis and water quality monitoring. It allows for an analysis of the basic electrical behavior of electrochemical systems and understanding of the underlying principles. However, the reported parameter estimation methods are usually deployed on the PC-based processing units that limit in-situ and unsupervised applications, where estimation results are free from user inputs. This paper presents a novel methodology for extracting the values of 2R-1C equivalent circuit by solely utilizing the measured impedance modulus. Unlike iterative and PC-based estimation methods, this approach eliminates user inputs in terms of a need for an initial guess, addressing a significant drawback of those approaches. The proposed work was validated by simulated and experimentally obtained impedance. Moreover, details of deployment on the PC-based and embedded hardware were also reported. The utilization of embedded hardware platform (Mega2560) enables an efficient processing of EIS data (80 milliseconds for 100 datapoints) by creating opportunities for in-situ parameter extraction of equivalent electrical circuits. [ABSTRACT FROM AUTHOR]

As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

The price of the product has a great impact on the market competitiveness of the product. The manager can adjust the operation strategy according to the cost information of the product, cut costs, adjust the market decision and make the product more competitive in the market. Therefore, the research of product costing method has a very important significance. Considering that the physical resources in the processing of the virtual network mapping (VNM) fragment embedded in the request are rejected, and the problem of physical resource usage is weakened, a VNM algorithm based on the optimal subnet edge matching algorithm is proposed. After the VNM request is coarsened to the subnet, the breadth-first search algorithm is used to create the physical subnet candidate set, and the constraints of the virtual node and the coarsening network topology are complied with. The simulation results show that the proposed algorithm reduces the number of link mapping hops and can improve the acceptance rate and cost-benefit ratio of virtual networks. First of all, this paper analyzes the research background of manufacturing cost calculation and the research situation at home and abroad, discusses the advantages and disadvantages of standard cost calculation and studies and analyzes the principles, advantages and disadvantages of standard cost calculation in daily business activities. Second, design a new mathematical accounting model based on the mathematical model of standard cost calculation in daily business activities. This combines the advantages of standard costing and time-based activity costing, and provides theoretical support for manufacturing companies to use this accounting method. Finally, the research results of this paper are summarized, and the shortcomings of this paper are analyzed. Cost accounting method needs to be further studied. [ABSTRACT FROM AUTHOR]

In the direct current (DC) microgrid, a communication-less control algorithm to keep a stable state of the isolated DC microgrid is presented in this paper. In order to get balance of the state of charge (SoC) among multiple battery energy storage systems (BESs), a control algorithm with adaptive droop coefficient is proposed. When the SoC of BESs has a deviation, the droop coefficient will change with the SoC. The DC bus voltage is kept at a stable value within a small range of deviation using the proposed adaptive control algorithm, thus improving the voltage quality. The coordinated control strategy is significant to keep the system stable and to ensure the power balance of the DC microgrid. Finally, several representative cases are discussed and verified. The simulation results show that the DC bus voltage regulation has better stability and robustness without communication and the proposed control algorithm has a desirable performance on the balance of the SoC. [ABSTRACT FROM AUTHOR]

Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft- Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking. [ABSTRACT FROM AUTHOR]

This paper realizes the implementation of Improved Multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing Multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a Multi-objective Optimization Problem that consists of different objective functions, viz. fuel cost with/ without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved Multi-objective Mayfly Algorithm is interpreted in terms of the present Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved Multi-objective Mayfly Algorithm, Multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved Multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem. [ABSTRACT FROM AUTHOR]

The era of new human–computer interaction has accelerated, and gesture recognition is one of the development trends of human–computer interaction system in the future. The emerging graph neural network can capture the interdependence between instances and infer the complete information of the image based on local features, which is helpful to the recognition of human gestures. Therefore, the combination of graph neural network and convolutional neural network (CNN) is applied to the research of gesture recognition and it has important research significance. This paper uses the Mixup method to perform data enhancement processing on the collected image data set, applies the pyramid pooling method to the EigenPooL model to achieve efficient capture of image features and selects the Sigmoid function and the PReLU function as the activation function of the network model to meet the model's requirements, such as long time training and strong fitting ability. This paper introduces the structure and algorithm of EigenPooL model in detail. The algorithm uses hypergraph learning method instead of simple graph learning method. On the gesture picture test set, the average accuracy of the algorithm is 86.50%, the recall rate is 94.87% and the average detection time per frame is 421 ms. [ABSTRACT FROM AUTHOR]

GPS receivers have a wide range of applications, but are not always secure. A spoofing attack is one source of conscious errors in which the counterfeit signal overcomes the authentic GPS signal and takes control of the receiver's operation. Recently, GPS spoofing attack detection based on computational algorithms, such as machine learning, classification, wavelet transform and clustering, has been developing. This paper proposes multiple clustering algorithms for accurately clustering the authentic and spoofing signals, called subtractive, FCM and DBSCAN clustering. The spoofing attack is recognized using two distinct features: moving phase detector variance and norms of correlators. Spoofing and authentic signals have different patterns in the proposed features. According to the Dunn and Silhouette indexes, the validation of the results is investigated. The Dunn values for the proposed approaches are 0.8592, 0.5285 and 0.6039 for DBSCAN, FCM and subtractive clustering, respectively. Also, the DBSCAN algorithm is implemented at the RTL level because of its highest value for the Dunn index and algorithm verifiability. Using the Vivado tools, this algorithm is implemented and designed on a Xilinx Virtex 7 xc7vx690tffg1930-3 hardware device for two-dimensional data with 32-bit accuracy and 130 data points. [ABSTRACT FROM AUTHOR]

This paper presents an electric vehicle connected to a charging station based on the proposed method. The proposed technique is the joint execution of the Student Psychology Optimization Algorithm (SPOA) and the AdaBoost algorithm and is therefore called the SPOA-AdaBoost algorithm. In particular, the annualized social cost depends on CS and EVCS set from the objective function of the allocation model. The EVCS is linked with the CS and allows the charging service for electric vehicles. The vehicle-to-grid functions of electric vehicles are properly considered under the present optimization model. The EV load demands are considered as controllable resources, and EV optimal optimization problems are connected with allocation problems. When EV arrives at the charging station, it reports its own energy demand and expected departure time using the EVCS operator. Every EVCS could attack the details of electric vehicles via the proposed method. With this proper action, this method manages the energy demand and the total supply. The constraints are the power flow equations, equivalent load demands on the buses, branch current constraints, discrete size restrictions for CS, constraints on CS outputs, EV participation on V2G activities, mutual exclusivity of the EV charge and discharge statuses, EV Owner charge satisfaction, EV Owner satisfaction charge, EV SOC restrictions, Occupied CF quantities, and EVCS CF sufficiency. Among these, the execution of the present model done by the MATLAB/Simulink platform and the performance of the proposed model is likened with other systems. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper. [ABSTRACT FROM AUTHOR]

Microwave hyperthermia is a new method of treating cancer, where the therapeutic effect is determined by the heating temperature. Traditional active temperature sensors are interfered by high frequency so that the accuracy of temperature measurement cannot be guaranteed. It is of great significance to study the high-precision fluorescent optical fiber temperature sensor with complete insulation. This paper has realized a compact and practical fluorescent optical fiber temperature sensor after studying the optical path, circuit, data processing algorithm. In order to improve the accuracy of the system, the weighted linear least-squares fitting algorithm is improved in this paper. Through experimental tests, compared with the standard linear least-squares fitting algorithm and the unimproved weighted linear least-squares fitting algorithm, the accuracy of the algorithm is improved by about 98% and 65.5%, respectively. In addition, the response time is reduced by about 36.5%, compared with the unimproved weighted linear least-squares fitting algorithm. This algorithm fully meets the precision requirements of microwave hyperthermia. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high complexity and low detection accuracy of single-stage three-dimensional (3D) detection method, a vehicle object detection algorithm based on the Efficient Channel Attention (ECA) mechanism is proposed. This paper provides a good solution to the problems of low object recognition accuracy and high model complexity in the field of 3D object detection. First, we voxelized the original point cloud data, taking the average coordinates and intensity values as the initial features. By entering into the Voxel Feature Encoding (VFE) layer, we can extract the features of each voxel. Then, referring to the VoxelNet model, the ECA mechanism is introduced, which reduces the complexity of the model while maintaining the good performance in the model. Finally, experiments on the widely used KITTI dataset show that the algorithm performs well, and the accuracy of the proposed ECA algorithm has reached 87.75%. Compared with the current mainstream algorithm SE-SSD of object detection, the accuracy is increased by 0.21%. [ABSTRACT FROM AUTHOR]

In the large-scale and ultra-dense Internet of Vehicles (IoVs), constructing the simplest backbone network is an urgent problem to be solved. In fact, constructing the simplest backbone network is an NP-hard problem, and at present, there is no effective solution. In this paper, we propose a graph-based clustering algorithm to solve this problem and construct the simplest backbone network in the large-scale and ultra-dense IoV. We establish a backbone network model for the large-scale and ultra-dense IoV and optimize the backbone network by employing a novel local search iterative algorithm. Simulation results show that with the increase in node density, the number of clusters selected by the proposed algorithm tends to be stable, while the number of optimized clusters decreases by 28.87% on an average. Thus, the proposed algorithm can effectively simplify the backbone network. [ABSTRACT FROM AUTHOR]

Digital microfluidic biochips (DMFBs) are emerging as an alternative to the cumbersome traditional laboratories for biochemical analysis. DMFBs come under micro-electro-mechanical systems and are a class of lab-on-a-chip devices. DMFBs provide automation, miniaturization and software programmability. The droplet routing algorithm determines concurrent routes for a set of droplets from their source cells to individual target cells on a DMFB. In this paper, a double deep Q-network (DDQN)-based droplet routing algorithm has been proposed. DDQN is a temporal difference-based deep reinforcement algorithm that combines Double Q-learning with a deep neural network algorithm. In the proposed work, routes for droplets are determined by DDQN, and later collisions are resolved using stalling and/or detouring. The latest arrival time of droplets arriving last at its target and cell utilization is taken as objectives for routing algorithm performance evaluation. The proposed method is evaluated on two standard benchmark suites. Simulation results show that the proposed DDQN-based droplet routing algorithm produces competitive results compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Image registration of multimodal remote sensing images plays a vital role in remote sensing image analysis. However, there are significant nonlinear intensity differences between multimodal remote sensing image pairs, making it difficult for most traditional image registration algorithms to meet the registration requirements. In this paper, we propose a novel edge descriptor utilizing edge information, which has not only affine invariance but is also insensitive to nonlinear intensity differences. Moreover, we utilize the proposed descriptor to design a multimodal image registration algorithm. We use several different multimodal image pairs to evaluate the proposed algorithm. The experimental results show that the proposed algorithm holds a stable performance and can still achieve accurate spatial alignment even with the huge nonlinear intensity differences. [ABSTRACT FROM AUTHOR]

In this paper, a high-frequency AC-link DC–AC converter is presented with detailed analysis. This converter is used as an interface between a dc power source and a grid. The studied converter is a universal power converter which consists of two bridges separated by an AC-link; each bridge is connected to a source or it feeds a load. The link of this converter contains a parallel pair inductance-capacitor. The inductance is used to stock/supply energy and for the link capacitor, it allows to perform soft switching during turning on/turning off of the switches. The studied converter has considerable advantages compared to the other topologies. Its fundamental properties are, especially, the compactness, reliability and efficiency which it ensures. Also, it guarantees longer lifetime and the possibility to transfer power in the two power flow directions. In this talk, the principles of the ac-link inverter operation are clearly explained in this paper. Simulation results, under MATLAB/SIMULINK, are shown to validate the correct operation and the efficiency of the proposed converter topology. The control algorithm is, also, experimentally implemented using a dSPACE 1104 control board. [ABSTRACT FROM AUTHOR]

Due to uneven space–time distribution of vehicles, Internet of Vehicles (IoV) has problems with load imbalance and low resource utilization of Base Stations (BSs) in the Coordinated Multi-Point (CoMP) communication scenario. This paper proposes a dynamic load balancing algorithm based on vehicle prediction. It is assumed that the number of vehicles arriving at the BSs obeys the segmented Poisson distribution to determine the current and predicted load statuses of BSs. First, analyze the load status of each BS and the location of users (vehicles). Then, screen out BSs whose load below the full load threshold as a switchable low-load cooperative cluster, which can convert interference signals into useful signals and reduce the interference between adjacent BSs. Finally, complete load balancing by redistributing the communication service of edge users through sharing channel information and user date among coordinated BSs. Because IoV is a dynamic network, the proposed algorithm runs dynamically in cycles. Simulation results show that the algorithm can perform balance the load of BSs well, the overload rates of BSs during the traffic off-peak period and peak period are reduced significantly, and the average information rate of users is greatly improved. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of two digital noise-cancellation circuits (DNCCs) using a proposed algorithm and a Karnaugh map for a 2 + 1 multistage noise-shaping (MASH) delta-sigma modulator (DSM). The MASH architecture inherits a superior signal-to-noise-and-distortion ratio (SNDR) with the aid of an efficient noise-cancellation technique either in the analogue or digital domain. The key motivation of this study was to design an area-efficient DNCC. The first approach employed a proposed algorithm (Algorithm-based DNCC) to implement the DNCC and to construct a delay block with an inverter and transmission gate. The second approach involved a Karnaugh map (K-map DNCC) and a delay block with a pair of D flip-flops. A maximum simulated signal-to-noise ratio of 135 dB was completed with optimal analogue scaling coefficients for the proposed 2 + 1 MASH DSM with DNCC. The simulated SNDRs of the Algorithm-based DNCC and K-map DNCC were 91.04 dB and 91.16 dB, respectively. Measured results show that the SNDR of the Algorithm-based DNCC, the SNDR of the K-map DNCC, power consumption and core area are approximately 58.7 dB, 62.1 dB, 0.26 μ W and 2275 μ m2, respectively, for the designed DNCCs with an operating frequency of 10.24 MHz and supply voltage of 1.8 V. The transistor counts of the Algorithm-based DNCC are 74 transistors, while they are 106 transistors for the K-map DNCC. The proposed Algorithm-based DNCC saves 32 transistors and approximately reduces its chip area to 69.8% of the K-map DNCC. [ABSTRACT FROM AUTHOR]

The emergence of computation intensive automotive applications poses significant challenges on computation capacity of automotive electronic systems, thus vehicular edge computing (VEC) is introduced as a new computing paradigm into the internet of vehicle (IoV) to improve its data processing capability. However, as the computation capacity is limited in VEC servers, efficient task offloading algorithms need to be proposed. This paper first proposes a multi-task offloading model and gives the related task response time analysis method, and then, both a mixed-integer linear programming (MILP)-based algorithm and a simulated annealing-based heuristic algorithm are proposed to minimize the task response time. By comparing with a baseline algorithm, the MILP-based offloading algorithm can reduce the average task response time by 91.45%, and the heuristic offloading algorithm can reduce the average task response time by 70%. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

Application-Specific ICs (ASIC) are manufactured in bulk for a long time. In this paper, an approach to High-Speed Application-Independent IC (HS-AIIC) design is discussed. A resolution-selective (RS) and resolution-adaptive (RA) 8-bit Flash ADC are designed for use in various high-speed applications. With the choice of resolution, one can work with the trade-off between speed, power consumption, and resolution for a particular application. The proposed resolution selection algorithm can be implemented for any set of resolutions for a flash ADC design. Further, an adaptive block is added to make the ADC design adaptive in nature so that we do not have to select a particular resolution manually. The proposed design entrusts on saving manufacturing cost and increases the functionality of ADC on a single chip. Proposed resolution adaptive 8-bit flash ADC design dissipates 512 mW of power with an ENOB of 7.56 bits and SNDR of 46.27 dB for 1 GHz sampling clock pulse. [ABSTRACT FROM AUTHOR]

Applications relying on the Ethernet IEEE 802.3ba and IEEE802.3b standards require secure data encryption and authentication at extremely high speeds (at least 100 Gbit/s). The Galois/Counter Mode (GCM) is currently considered the de facto standard for hardware high-speed authenticated encryption, although other algorithms have been proposed in the literature such as the Offset Codebook Mode (OCB). The challenge in terms of providing security for high-speed applications is to achieve implementations that explore the parallelism of these algorithms; however, this translates into area cost. In this work, we propose an alternative to GCM and OCB. We show that a combination of the Offset Two-Round authenticated-encryption scheme with the AES block cipher (known as AES-OTR) is exceptionally well suited for exploiting fine-grained parallelism, and can therefore be used to achieve ultra-high-speed data encryption rates. The experiments reported in this paper show that our pipeline-parallel implementation of AES-OTR outperforms the GCM and OCB schemes in terms of throughput per area while using almost half of the logic resources. Our implementation used a Stratix 4 FPGA device as well as several devices from the Virtex family. Implementations of AES-OTR on Stratix 4 used 11kALMs and achieved a throughput of 143.65 Gbit/s. On the Virtex Ultrascale, our design used 31,859 LUTs with a throughput of 204.92 Gbit/s. [ABSTRACT FROM AUTHOR]

This paper considers the minimax design of two-dimensional (2D) finite impulse response (FIR) half-band filters. First, the design problem is formulated in a matrix form, where the half-band constraints are expressed as a pair of matrix equations. By matrix transformations, the constrained minimax problem is transformed into an unconstrained one. Then, we propose an efficient iterative reweighted least squares (IRLS) algorithm to solve this problem. The weighted least squares (WLS) subproblems arising from the IRLS algorithm are solved using a generalized conjugate gradient (GCG) algorithm. Moreover, the GCG algorithm is guaranteed to converge in a finite number of iterations. In the proposed algorithm, the design coefficients of filters are solved in their matrix form, leading to a great saving in computations and memory space. Design examples and comparisons with existing methods are provided to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a real-time low-cost geophone-based Elephant Footstep Vibration Detection and Identification (EFVDI) system is proposed. The system design started with a real-time low-cost generalized Footstep Vibration Recording and Analyzing (FVRA) system. A series of field experiments to record elephant footstep vibration (target) signals and other possible interfering ground vibration (noise) sources are conducted using the FVRA system. System's actual field performance was evaluated in terms of maximum detection range, signal amplitude, detection ratio, signal frequency, signal time span, etc. Variations of system's performance with several input parameters are also investigated. The recorded signals from target as well as noise sources are analyzed to extract different Signal Parameters (SPs). All SPs are saved in a Ground Vibration Signal Pattern Library (GVSPL) which is then used to frame accurate indigenous Elephant Identification Algorithm (EIA). The EIA is embedded in FVRA system to reshape it as specific Elephant Footstep Vibration Detection and Identification (EFVDI) system. The EFVDI system has successfully segregated elephant footsteps from other noise vibrations with high accuracy under simulated field experiment. The results from the proposed system will provide important data to the ongoing research of developing the much needed highly accurate Elephant Early Warning System (EEWS) in future. [ABSTRACT FROM AUTHOR]

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers. [ABSTRACT FROM AUTHOR]

In this paper, a secure and low energy dynamic slicing algorithm, namely, Improved D-SMART (IM-D-SMART) based on the Data Aggregation Protocol on Slice Mix Aggregate (D-SMART) is proposed to improve the security and confidentiality of wireless sensor networks and reduce the energy consumption of nodes in data collection and transmission in wireless sensor networks. According to the importance of data, the residual energy of nodes and the relative density of nodes, the data are dynamically partitioned to improve the D-SMART algorithm. Simultaneously, sending negative number splicing is used to compensate for the loss caused by the collision of data transmission between nodes. The simulation results show that IM-D-SMART outperforms D-SMART in terms of computation, privacy and communication cost. [ABSTRACT FROM AUTHOR]

Swarm intelligent algorithms can effectively tackle optimization problems that are difficult to solve by using traditional optimization algorithms. However, with the huge increase in the time and space cost for solving optimization problems, the use of swarm intelligent algorithms suffer from the limitation of overly long computation time. Based on Spark, which is the most popular open-source distributed computing framework, this paper studies specifically using swarm intelligent algorithms to solve combinatorial optimization problems. Based on the characteristics of typical swarm intelligent algorithms, we develop Spark-based parallel implementation of these algorithms to accelerate the population updating and parameter tuning procedures involved in swarm intelligence. Specifically, we first initialize the swarm and generate the initial solution, then perform the distributed iterative evolution procedure, and finally obtain the optimal solution. In addition, in order to improve solution quality, we rely on the Spark platform to perform distributed parameter tuning. The tuning strategy first generates different parameter combinations according to a given parameter list, then execute swarm intelligent algorithms with different parameter combinations in a distributed and parallel manner, and finally determine the optimal parameter combination by comparing the solutions of all algorithms. Experimental results on benchmark datasets show that the distributed algorithms can significantly enhance the computational efficiency without affecting the solution quality. [ABSTRACT FROM AUTHOR]

The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to accelerate parameter extraction process of surface-potential-based (PSP) MOSFET model is presented for the submicron MOS transistor. To reduce the computational time, Field Programmable Gate Array (FPGA) implementation of PSP model library — SiMKit is demonstrated using Xilinx's Zynq Ultrascale + MPSoC (Multi-processor System-on-Chip) platform. Parameter extraction is carried out using Particle Swarm Optimization (PSO) algorithm for 65 nm technology nMOS devices. With the available measurement data, 32 various PSP model parameters are extracted. Experimental results validate the performance and accuracy of parameter extraction by achieving Root Mean Square Error below 10% for various current–voltage characteristics of nMOS device. 41.57% acceleration in execution time for extraction process is achieved by Zynq Ultrascale + MPSoC platform compared to the conventional computer-based software approach. In addition, various design optimization directives are explored, and their performances are compared as a part of RTL generation of SiMKit. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a graphical visualization of the electrical activity of the human heart that is recorded by placing a surface electrode at standardized position on a person's chest. ECG signals suffer from artifacts/noises due to baseline wander (BW), electrode artifacts, muscle artifacts, power-line interference and channel noises during acquisition and transmission of the ECG signals. Reduction of these artifacts is crucial for efficient diagnosis and interpretation of the human heart condition. In this paper, an effective adaptive noise canceller (ANC) based on empirical mode decomposition (EMD)-Jaya algorithm is proposed for denoising electrocardiogram. In this approach, intrinsic mode functions (IMFs) produced by EMD are used as reference and Jaya algorithm is used to calculate optimum weights of finite impulse response (FIR) filter. This scheme is compared with EMD, wavelet transform (WT) thresholding, and hybrid EMD-least mean square (LMS) approaches through extensive simulation on noise corrupted ECG besides verifying the robustness with real ECG signals. The performance of the proposed technique is assessed using standard metric signal-to-noise ratio (SNR) with different contamination levels. The results obtained demonstrate the superiority of the hybrid when compared to other competing approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a well-optimized FPGA implementation of a chaos-based cryptosystem for real-time image encryption and decryption. A highly sensitive Pseudo-Random Number Generator (PRNG) based on the Lorenz chaotic system is designed to generate pseudo-random numbers. A high-quality encryption key is acquired by encrypting the numbers of a 128-bit counter using the PRNG. For image encryption, the image is first decomposed into 128-bit blocks. Then, the blocks are encrypted by XORing pixels with the key stream. R cycles of encryption can be achieved to increase complexity. Finally, the blocks are concatenated to form an encrypted image. The algorithm is designed, implemented, and validated on the Xilinx Zynq FPGA platform using the Vivado/System Generator tool. The hardware design is well optimized for pipeline processing and low resource utilization. The experimental synthesis indicates that the provided architecture achieves high performance in terms of frequency and throughput. The encryption scheme is evaluated and analyzed by several tools and tests using different images. The experimental simulation results demonstrate that the hardware implementation is faster than a software implementation while maintaining the technique's effectiveness. The proposed hardware implementation is extremely adopted for secret image encryption and decryption that can be used in real-time applications. [ABSTRACT FROM AUTHOR]

Prolonging the lifetime of wireless sensor networks (WSN) is the biggest challenging issue. In wireless multimedia sensor networks (WMSNs), sensor nodes have limited energy resource and to increase the lifetime of the network, it is necessary to design an effective fast algorithm that aims at reducing the consumed power. This paper proposes an energy-efficient discrete cosine transform (DCT) approximation requiring only 12 additions. Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, but above all, a very low computational complexity and significant compatibility with the exact DCT. Simulation results clearly show that the proposed fast transform algorithm achieves a better trade-off between image quality, computational complexity and energy consumption compared to any existing pruned DCT approximations. Furthermore, it is very suitable for the resource constrained wireless visual sensor networks (WVSNs) requiring low bitrates. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are connecting several Mobile Stations (MSs) to form the Mobile Communication Network (MCN). MSs are moving through macrocell and picocell recurrently due to the discontinuous deployment of the picocell over the macrocell. Each time they cross the cell boundary, they need to register with the Mobility Management Entity (MME). It incurs a high registration cost and elevates the signaling overhead ratio. To reduce the signaling overhead ratio, earlier techniques were attempted either by forming closed subscriber groups or by delaying the registration process, but that may create inconsistency in user location information. With this motivation, in this paper, MSs with similar mobility pattern which are moving from the picocell to macrocell, are grouped in clusters. A representative MS, called Cluster Representative (CR), performs the location management task on behalf of the whole cluster. The stochastic process has been employed to analyze the model. This study proposes Cluster-Based Location Management (CBLM) scheme and compares it with two state-of-the-art algorithms, named Group Mobility Management (GMM) and Closed Subscriber Group (CSG). On average, the CBLM scheme exhibits approximately 70% better performance than the CSG scheme whereas, 30% better than the GMM algorithm to mitigate the signaling overhead. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) together with sorting is one of the key goals for reducing image distortion. This paper presents an improvement in sorting based on the double-layered RDH algorithm by modifying the dynamic variance mean (DVM) sorting models and applying an effective predictor. Seven models are investigated to achieve suitable sorting with the best prediction error (PE) for each capacity and image. The fitting weight rhombus (FWR) predictor and two-pass testing (TPT) strategy are significantly utilized to achieve small PEs and reduce distortion. The performance of the proposed RDH algorithm is compared with other similar methods using eight different images. Experiment results demonstrate that the sorting performance of the proposed method is superior to those of the previous methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an analytic approach to the variable sifting based on weighting of qubits and gates. The proposed scheme allows us to optimally sift gates (multi-control single-target reversible gates) within a linear number of steps of computation and provides, in general, that a smaller number of SWAP gates are required to transform a reversible circuit into an Linear Nearest Neighbor (LNN) model than other competing approaches. The method is analyzed for two different models of implementations; it is verified on the experimental data and results are compared with the state-of-the-art algorithms for the design of LNN circuits. [ABSTRACT FROM AUTHOR]

In the modern System on Chip (SoC)-based designs, embedded memory occupies the majority of the area. Therefore, the demand for fast self-testing plays a vital role in the SoC device as its memory density increases. The focus of this research study is to provide a self-testing mechanism integrated with the SoC design for fault diagnosis and failure analysis. In particular, this paper proposes a controller design to test memories at SoC devices, called a memory built-in self-test (MBIST) controller. This controller works on the principle of the proposed March-ee (enhanced elements) algorithm with the primary objective to improve the test speed, fault coverage, and power consumption at a low area overhead. The complete design of the MBIST controller with the associated March-ee algorithm is minimal and easy to be integrated into any SoC device to provide a vibrant feature of memory fault detection. The results obtained are compared with that provided by the existing March algorithms, using the same design specifications, where the proposed March-ee MBIST controller has shown better results in terms of power consumption, fault coverage, timing, and area. [ABSTRACT FROM AUTHOR]

Ultra high definition (UHD) video is considered as a technology that generates a video image with a resolution 16 times as many pixels as high definition television. Extensive and complex computational difficulties are there to generate a video in super-resolution (SR). Some extensive floating-point computations are needed for the direct implementation of area-pixel scaling, which is a factor for calculating one target pixel. In this paper, a hybrid Convolutional Neural Network and Cuckoo Search (CNN-CS) algorithm are proposed, which can replace the large scale of complex training and time-consuming machine learning technique. Cuckoo Search (CS) algorithm is based on bird's levy-flight approach that can optimize the CNN. CS forecasting model isolates the redundant or irrelevant image patches and, therefore, it reduces the required memory space. The SR is performed effectively in hardware with less hardware utilization and memory space, where LR input data are processed line-by-line using the proposed hybrid CNN-CS algorithm. To optimize the design constraints, CS forecasting model estimates the required patches. Parallel sub pipeline (PSP) technique improves the parallelism process thereby reducing the computational complexity and improving the throughput rate and speed of execution. Finally, the proposed hybrid CNN-CS SR design is coded in Verilog HDL and is implemented with the XCKU040 FPGA device. To validate the proposed CNN-CS SR algorithm, standard datasets, such as Set-5, Set-14, BS-100 and Urban-100, are used. The visual quality of the proposed CNN-CS SR design is evaluated by the standard evaluation metrics such as peak signal-to-noise ratio (PSNR) and SSIM. The proposed hybrid CNN-CS SR design is compared with the existing state-of-the-art techniques both qualitatively and quantitatively. Experimental results demonstrate that the proposed method improved the throughput of 42.83%, memory space and power consumption are reduced to 16.16%, 63.81%, respectively, over the existing techniques. [ABSTRACT FROM AUTHOR]