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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]

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]

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]

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]

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]

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]

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]

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]

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]

A person's emotional information, needs and cognitive processes can be described by eye movement states and concerns, so gaze tracking was first applied in the field of psychology. With the continuous development of information technology, the application range of gaze tracking has expanded from psychology to medical, military, commercial and many other fields. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. First, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block dataset. For 12 classified data, the lowest recognition error rate is 5.34%. [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]

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]

The present prevailing loop closure detection algorithm is mainly applicable for simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon environmental conditions, which can fluctuate due to variations in lighting or the surrounding scenario. Vision-based algorithms, while adept during daylight hours, may falter in nocturnal settings. Conversely, lidar methods hinge on the sparsity of the given scenario. This paper proposes an algorithm that comprehensively utilizes lidar and image features to assign weighted factors for loop closure detection based on multi-modal sensor fusion. First, we use k -means clustering to produce a point cloud spatial global bag of words. Second, an improved deep learning method is used to train feature descriptors of images while scan context is also used to detect candidate point cloud features. After that, different feature-weighted factors are assigned for homologous feature descriptors. Finally, the detection result related to the maximum weight factor is designated to the optimal loop closure. The adaptive weighted loop closure (AWLC) algorithm we proposed inherits the advantages of different loop closure detection algorithms and hence it is accurate and robust. The AWLC method is compared with popular loop detection algorithms in different datasets. Experiments show that the AWLC can maintain the effectiveness and robustness of detection even at night or in highly dynamic complex environment. [ABSTRACT FROM AUTHOR]

In addition to Power Quality (PQ) problems caused by solid state devices in the distribution side of grid connected Photo Voltaic (PV) system, quality of power also suffers in the source side by the usage of converters. Generally, PQ problems mitigation strategy has turned into a critical issue in grid-connected PV systems. This paper proposes a control topology for PQ issues mitigation scheme in grid connected PV systems. The primary goal of this study is to enhance the PQ in power distribution system through eradicating the voltage interruption, voltage swell and sag as well as voltage fluctuation. To accomplish these changes, Unified Power Quality Conditioners (UPQCs) with controllers were utilized. Then the faults are mitigated by the utilization of UPQC with Tree Seeds Algorithm (TSA) — Adaptive Neuro-Fuzzy Interference System (ANFIS). At first, the normal properties of the grid-connected PV systems are dissected. From that point forward, a few interruptions are made in the system and the abnormal characteristics are analyzed. Then the abnormal characteristics of PQ problems are diminished in the utilization of UPQC and TSA–ANFIS controllers. UPQC is one of the best filters topologies which can mitigate and control all PQ problems. The combined algorithms of TSA-ANFIS are used to keep the dc-link voltage in UPQC at consistent level. The proposed hybrid approach is executed in MATLAB/Simulink platform and the performance is studied. Further, the proposed method is compared with existing Elephant Herding Optimization (EHO) technique and also with hybrid EHO-ANFIS techniques. The comparison results showed that the proposed technique is superior to the EHO and EHO-ANFIS techniques. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable image confusion scheme, which uses Linear Congruential Generators (LCGs)-based Pseudorandom Bits Generator (PRBG). The PRBG is based on the variable input-coupled LCG with a reconfigurable clock divider. The proposed algorithm encrypts the input image up to four times successively using different random sequences in every attempt. This new scheme aims to efficiently extract statistically strong pseudorandom sequences from a proposed PRBG with a large keyspace and simultaneously increase the security level of the encrypted image. This PRBG was initially designed on Virtex-5 (XC5VLX110T), Virtex-7 (XC7VX330T) and Artix-7 (XC7A100T) Field Programmable Gate Arrays (FPGAs). The statistical properties of the proposed PRBG for four different configurations are verified by the National Institute of Standards and Technology (NIST) tests. Thereafter, a reconfigurable encryption/decryption algorithm that uses the proposed PRBG is developed for secure image encryption. The encryption process was accomplished using the MATLAB tool after obtaining the PRBG keys from the FPGA. To show the quality and strength of the encryption process, security analysis [correlations and Number of Pixels Change Rate (NPCR)] is performed. Security analysis results are compared with the conventional encryption algorithm to show that the developed reconfigurable encryption scheme provides better results in security tests. [ABSTRACT FROM AUTHOR]

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2 ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods. [ABSTRACT FROM AUTHOR]

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot's identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; K -means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84 fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection. [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]

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]

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 one-dimensional Otsu method is an adaptive threshold method. It obtains the optimal threshold for image segmentation by the maximum between-class variance, without considering the minimum within-class variance. As the background of water surface image is mostly uniform, using this feature, the threshold selection tactics adopt the combination of the one-dimensional Otsu method and the uniformity measurement, proposes the threshold segmentation method based on uniformity measurement, and adopts the performance evaluation method based on GT image to compare the segmentation result. Experimental results demonstrate that effectiveness of the improved Otsu method is generally better than the traditional Otsu method, and the other four commonly used threshold segmentation methods for the water surface image, which improves the segmentation accuracy of such images and reduces the segmentation error rate. At the same time, as the water surface image is usually affected by light intensity, water ripple and other factors, this paper also adopts the relevant correction algorithm to further improve the segmentation accuracy. [ABSTRACT FROM AUTHOR]

Complex network reconfiguration has always been an important task in complex network research. Simple and effective complex network reconstruction methods can promote the understanding of the operation of complex systems in the real world. There are many complex systems, such as stock systems, social systems and thermal power systems. These systems generally produce correlated time series of data. Discovering the relationships among these multivariate time series is the focus of this research. This paper proposes a Spearman coefficient reconstruction network (SCRN) method based on the Spearman correlation coefficient. In the SCRN method, we select entities in the real world as the nodes of the network and determine connection weights of the network edges by calculating the Spearman correlation coefficients among nodes. In this paper, we selected a stock system and boiler equipment in a thermal power generation system to construct two complex network models. For the stock network model, we used the classic Girvan–Newman (GN) algorithm for community discovery to determine whether the proposed network topology is reasonable. For the boiler network model, we built a predictive model based on an support vector regression (SVR) model in machine learning, and we verified the rationality of the boiler model by predicting the amount of boiler steam. [ABSTRACT FROM AUTHOR]

In this paper, the Levy flight-based chicken swarm optimization (LFCSO) is proposed to follow the highest power of grid-joined photovoltaic (PV) framework. To analyze the grid-associated PV framework, the characteristics of current, power, voltage, and irradiance are determined. Because of the low yield voltage of the source PV, a big advance up converter with big productivity is required when the source PV is associated with the matrix power. A tale great advance up converter dependent on the exchanged capacitor and inductor is illustrated in this paper. The LFCSO algorithm with the adaptive neuro-fuzzy inference system is used to generate the control pulses of the transformer-coupled inductor DC–DC converter-less switched capacitor. While using the switched capacitor-coupled inductor, the voltage addition is expanded in the DC–DC converter and the power of PV is maximized. Here, the normal CSO algorithm is updated with the help of Levy flight functions to generate optimal results. To get the accurate optimal results, the output of the proposed LFCSO algorithm is given as the input of the ANFIS technique. After that, the optimal results are generated and they provide the pulses for the system. The working guideline is analyzed and the voltage addition is derived with the utilization of the proposed technique. From that point forward, it predicts the exact maximum power of the converter according to its inputs. Under the variety of solar irradiance and partial shading conditions (PSCs), the PV system is tested and its characteristics are analyzed in different time instants. The proposed LFCSO with ANFIS method is actualized in Simulink/MATLABstage, and the tracking executing is examined with a traditional method such as genetic algorithm (GA), perturb and observe (P&O) technique–neuro-fuzzy controller (NFC) and fuzzy logic controller (FLC) technique. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc NETworks (VANETs) are typically termed as a wireless ad-hoc network that contains extreme node mobility and also the network carries a great significance in various traffic-oriented commercial applications and safety services. Due to its high mobility, routing in VANET has been a challenging work and also proving a higher rate of packet delivery ratio with reduced packet loss has been more important to be considered in route formations. With that note, this paper contributes to developing a clustering model called Middle-Order Vehicle-based Clustering (MOVC) model for managing the frequent topological change and high vehicle mobility, and efficiently handling the typical road traffic scenario. Moreover, the algorithm is intended to maintain the cluster to be constant for managing the vehicles in effective ways and also to provide uninterrupted communication between the vehicles. An algorithm for Effective Cluster Head Election (ECHE) is also derived in this paper for proficiently handling the frequency variation on the highways. Further, the model is simulated and evaluated on the basis of various metrics of VANET routing, specifically packet loss, packet delivery ratio, network lifetime and throughput. The results show that the proposed mechanism outperforms the results of existing models. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optimize the thinning of an elliptical cylindrical array (ECA) composed of uniformly stimulated, isotropic antennas with the objective of achieving a directed beam characterized by a significantly reduced relative Sidelobe Level (SLL). The optimization process employs the Novel Particle Swarm Optimization (NPSO) method, which offers a fresh perspective on addressing electromagnetic optimization challenges by its ability to effectively explore solution spaces. By employing the NPSO algorithm, which emulates the collective behavior of swarming particles to search for optimal solutions, this study addresses complex optimization challenges inherent in antenna array design. This study focuses on identifying the optimal combination of ON–OFF components (use the minimum number of ON antenna elements) within the antenna array to produce a radiation pattern exhibiting the greatest decrease in SLL. Additionally, the First Null Beam Width (FNBW) is targeted for optimization without predefined values. The optimization approach also considers the effect of thinned array element spacing on the overall performance metrics. Out of total 36 elements, only 15 elements are switched ON and the remaining of the elements are OFF, so the total reduction or thinning of ECAA is 41.66%. Simulation results demonstrate that the proposed methodology enables a simultaneous reduction of more than half of the antenna array elements while achieving superior SLL minimization. This significant reduction in antenna elements not only contributes to simplifying the array design, but also enhances the array’s beamforming capabilities. [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]

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]

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, 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]

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]

As China's distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model's input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection. [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]

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]

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]

A design flow using the / methodology with the adaptive particle swarm optimization (PSO) algorithm is proposed for the modern analog circuit in this paper. For the advanced CMOS process, / methodology is suitable to the long channel and short channel design in all transistor operation regions. Different from the classical PSO algorithm, the adaptive PSO algorithm features the better search efficiency and faster convergence speed over the global search. Two amplifiers were designed and implemented in a standard 0.11m CMOS process using MATLAB and HSPICE. Using the thermal noise coefficient and the corner frequency , this paper explored the noise design budget of low-power multistage amplifier in different saturation modes. Detailed optimization of the objective function and constraints are classified into the mono-objective case and the multi-objective case. The total running times of simulations are 5649 s and 6813 s while the errors are less than 9% and 10%, respectively. Compared with CODE, GAPF and DEPF algorithms, it can save more running time and improve the accuracy of the design. Moreover, it provides more design freedom for the trade-off among gain, the gain-bandwidth (GBW) product, noise and the phase margin under worst cases without extra tweaking. Not only can the methodology work in the 0.18m CMOS process, but also be migrated to the 0.11m CMOS process, even in the nanometer analog circuit. [ABSTRACT FROM AUTHOR]

Cloud computing has become a growing technique due to its on-demand service and scalable features. Most of the applications of the cloud today are in data storage. Data protection and privacy have thus become a major concern. There are huge risks of storing transaction data on third-party hosts. Service providers need to make right investments in proving and ensuring appropriate security over time. Malicious users can exploit weak spots in the data protection model to increase unauthorized access to data. To overcome the problem, in this paper, security-aware data transaction on the cloud using the blowfish algorithm (BA) is proposed. To improve the security of the system, initially, the proposed system verifies the authentication of the user. After the authentication process, the uploaded data are initially split using a pattern-matching algorithm. Then, the separated data are encrypted with the help of BA. Finally, the encrypted data are stored in the cloud at an optimal location. This method is more secure as the data are column-separated and optimized in the cloud, so it is difficult to attack. This method is highly guaranteed because the user cannot recover the document without authentication checking. The performance of the proposed approach is analyzed in terms of different metrics, namely, memory, encryption and decryption time. The experimental results clearly say that the proposed method took minimum encryption time compared with the existing approach. [ABSTRACT FROM AUTHOR]

In mobile cloud computing, the computing resources of mobile devices can be integrated to execute complicated applications, in order to tackle the problem of insufficient resources of mobile devices. Such applications are, in general, characterized as workflows. Scheduling workflow tasks on a mobile cloud system consisting of heterogeneous mobile devices is a NP-hard problem. In this paper, intelligent algorithms, e.g., particle swarm optimization (PSO) and simulated annealing (SA), are widely used to solve this problem. However, both PSO and SA suffer from the limitation of easily being trapped into local optima. Since these methods rely on their evolutionary mechanisms to explore new solutions in solution space, the search procedure converges once getting stuck in local optima. To address this limitation, in this paper, we propose two effective metaheuristic algorithms that incorporate the iterated local search (ILS) strategy into PSO and SA algorithms, respectively. In case that the intelligent algorithm converges to a local optimum, the proposed algorithms use a perturbation operator to explore new solutions and use the newly explored solutions to start a new round of evolution in the solution space. This procedure is iterated until no better solutions can be explored. Experimental results show that by incorporating the ILS strategy, our proposed algorithms outperform PSO and SA in reducing workflow makespans. In addition, the perturbation operator is beneficial for improving the effectiveness of scheduling algorithms in exploring high-quality scheduling solutions. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low efficiency and long running time caused by the traditional Zernike moment method for convolution calculation of the whole image, this paper combines the canny detection algorithm with the Zernike moment method. First, the canny edge detection algorithm, which combined with the Otsu threshold method, is used to extract the pixel edge of the image. Then an improved Hough transform method is used to fit the geometric edge in the image. Based on this, the Zernike moment method is applied to realize sub-pixel positioning of images. The algorithm improves the deficiencies of direct sub-pixel detection, improving accuracy and reducing running time. To verify the effectiveness of the proposed algorithm, the algorithm is applied to the dimension measurement experiment of T-type guide way. The results clearly show that the algorithm is superior to the traditional algorithm in accuracy. [ABSTRACT FROM AUTHOR]

Effective resistance to intellectual property (IP) piracy, overproduction and reverse engineering are becoming more and more necessary in the integrated circuit (IC) supply chain. To protect the hardware, the obfuscation methodology hides the original function by adding a large number of redundant states. However, existing hardware obfuscation approaches have hardware overhead and efficiency of obfuscation limitations. This paper proposed a novel methodology for IP security using the minimum spanning tree finite state machine (MST-FSM) obfuscation. In the minimum spanning tree (MST) algorithm, the Hamming distance defines the cost of obfuscated states. The Kruskal algorithm optimizes the connection relationship of obfuscated states by computing the Hamming distance of the MST-FSM. The proposed MST-FSM is automatically generated and embedded in the hardware IP with the self-building program. Finally, the MST-FSM is applied on the itc99 benchmark circuits and encryption standard IP cores. Compared with other state-of-the-arts, the obfuscation potency is improved by 3.57%, and the average hardware cost is decreased by about 6.01%. [ABSTRACT FROM AUTHOR]

One of the important parameters in the brain–computer interface (BCI) system is speed. Therefore, it is always desirable to design a high-speed system that has an acceptable performance, simultaneously. The main idea of this paper is the use of evolutionary algorithms (EAs) to select the optimal features for epilepsy diagnosis by processing the electroencephalogram (EEG) signals. The lesser the number of features is, the higher will be the usefulness of accuracy of the system to us. Therefore, here, using EAs, some of the features that are redundant in the data and do not contain a lot of information and only increase the complexity of the system are eliminated and the best features are chosen. We select this choice by EAs. Running the feature selection step is after the feature extraction step. In fact, the features were extracted using the common spatial pattern (CSP) algorithm, and then the optimal features were selected from the extracted feature set. This can save a lot of system complexity and reduce system execution time considerably. Finally, at the diagnostic stage, these selected features are given to a simple neural network (NN). The results showed that when the combination of EA and CSP is used, the precision of the system is much higher than when the CSP method is only used, although it contributes significantly to the complexity of the system. [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]

Reconfiguring a very large scale integration (VLSI) array with faults is to construct a maximum logical sub-array (target array) without faults. A large target array implies a good harvest of the corresponding reconfiguration algorithm. Thus, a tight upper bound of the harvest can be directly used to evaluate the performance of the reconfiguration algorithm. This paper presents a new approach to calculate the upper bound of the harvest for the VLSI arrays with clustered faults. The latest upper bound is successfully reduced and the proposed technique to calculate the upper bound is bound into a reconfiguration algorithm cited in this paper. Simulation results show that the upper bound is reduced up to 20% on 256 × 256 array with clustered faults, and the corresponding reconfiguration process is significantly accelerated over 30%, without loss of harvest. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm is proposed for computing the node-disjoint optimal transmission energy consumption route for coded cooperative mobile IoT networks. Inspired by the potential benefits user cooperation can provide, we incorporate user cooperation to the mobile ad-hoc multi-hop IoT networks. Our results include a novel ant colony system-based node-disjoint energy-efficient routing algorithm. Ant colony system can approximate the optimal route by local information and is thus very suitable for mobile IoT network environment. In particular, ant algorithm makes history-sensitive choice and thus can significantly outperform the greedy algorithm. In addition, it can efficiently handle the case of multiple sources and multiple destinations. For a large IoT network, we investigate the multi-scale ant colony system and when compared to Dijkstra algorithm, such an algorithm usually shortens the runtime by a factor of several hundred. [ABSTRACT FROM AUTHOR]

Differential Power Analysis (DPA) attacks are known as viable and practical techniques to break the security of cryptographic algorithms. In this type of attack, an adversary extracts the encryption key based on the correlation of consumed power of the hardware running encryption algorithms to the processed data. To address DPA attacks in the hardware layer, various techniques have been proposed thus far. However, current techniques generally impose high performance overhead. Especially, the power overhead is a serious issue which may limit the applicability of current techniques in power-constrained applications. In this paper, combinational counters are explored as a way to address the DPA attacks. By randomizing the consumed power in each clock cycle of the circuit operation, these counters can enhance the resistance of the cryptographic cores against DPA attacks with low power overhead as well as zero timing overhead. Experimental results for an AES S-Box module in 45 nm technology reveal that the proposed technique is capable of achieving higher level of security in comparison to two other approaches while preserving the power and performance overhead at a same or lower level. [ABSTRACT FROM AUTHOR]

Data security, privacy and authenticity are crucial in wireless data transmission. Low power consumption is the main requirement for any chip design targeting the Internet of Things (IoT) applications. In this research paper, a comparative study of eight authenticated encryption and decryption algorithms, selected from the "Competition for Authenticated Encryption: Security, Applicability and Robustness" (CAESAR), namely, ACORN, ASCON, CLOC, JOLTIK, MORUS, PRIMATEs, SCREAM and SILC, is presented. The FPGA and ASIC implementations of these eight algorithms are synthesized, placed and routed. Power, area, latency and throughput are measured for all algorithms. All results are analyzed to determine the most suitable algorithm for IoT applications. These results show that ACORN algorithm exhibits the lowest power consumption of the eight studied at the expense of lower throughput and higher latency. MORUS algorithm gives the highest throughput among the eight selected algorithms at the expense of large area utilization. [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]