In the current IoT era, the key to sports intelligence is the effective collection and analysis of sports data. Sports data can accurately reflect an athlete’s athletic status and help coaches to develop competitive tactics and training programs. Wearable electronic devices used to collect sports data currently have several drawbacks, including their large size, heavy weight, complex wiring, high cost, and need for frequent power replacement. In this work, transparent polyamide-66 (PA-66) and transparent polytetrafluoroethylene (PTFE) films were used as friction layers, polydimethylsiloxane (PDMS) was used as a support layer, and conductive hydrogels were used as electrodes, which were simply combined to create stable and durable triboelectric nanogenerators (SD-TENG) with good mechanical and triboelectric properties. In the test, the output power was 1mW under a load resistance of 10MΩ. In addition, the integrated intelligent speed skating land training assistance system monitors the changes in the joints and joint chains of skaters during land training in real time. The successful demonstration of the use of SD-TENG in speed skating land training will help to promote the development and application of TENG in the fields of intelligent sport monitoring, smart wearable devices, and big data analysis. Full article

Motion planning has been used in robotics research to make movement decisions under certain movement constraints. Deep Reinforcement Learning (DRL) approaches have been applied to the cases of motion planning with continuous state representations. However, current DRL approaches suffer from reward sparsity and overestimation issues. It is also challenging to train the agents to deal with complex task specifications under deep neural network approximations. This paper considers one of the fragments of Linear Temporal Logic (LTL), Generalized Reactivity of rank 1 (GR(1)), as a high-level reactive temporal logic to guide robots in learning efficient movement strategies under a stochastic environment. We first use the synthesized strategy of GR(1) to construct a potential-based reward machine, to which we save the experiences per state. We integrate GR(1) with DQN, double DQN and dueling double DQN. We also observe that the synthesized strategies of GR(1) could be in the form of directed cyclic graphs. We develop a topological-sort-based reward-shaping approach to calculate the potential values of the reward machine, based on which we use the dueling architecture on the double deep Q-network with the experiences to train the agents. Experiments on multi-task learning show that the proposed approach outperforms the state-of-art algorithms in learning rate and optimal rewards. In addition, compared with the value-iteration-based reward-shaping approaches, our topological-sort-based reward-shaping approach has a higher accumulated reward compared with the cases where the synthesized strategies are in the form of directed cyclic graphs. Full article

Relation extraction in the threat intelligence domain plays an important role in mining the internal association between crucial threat elements and constructing a knowledge graph (KG). This study designed a novel document-level relation extraction model, FEDRE-KD, integrating additional features to take full advantage of the information in documents. The study also introduced a teacher–student model, realizing knowledge distillation, to further improve performance. Additionally, a threat intelligence ontology was constructed to standardize the entities and their relationships. To solve the problem of lack of publicly available datasets for threat intelligence, manual annotation was carried out on the documents collected from social blogs, vendor bulletins, and hacking forums. After training the model, we constructed a threat intelligence knowledge graph in Neo4j. Experimental results indicate the effectiveness of additional features and knowledge distillation. Compared to mainstream models SSAN, GAIN, and ATLOP, FEDRE-KD improved the F1score by 22.07, 20.06, and 22.38, respectively. Full article

Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%. Full article

Users can “voluntarily” control the size of their pupil by switching focus from a far target A (large pupil size) to a near target B (small pupil size), according to the pupillary accommodative response (PAR). Pupil size is governed by smooth muscles and has been suggested as communication pathway for patients affected by paralysis of skeletal muscles, such as in amyotrophic lateral sclerosis (ALS). We here present a video game that relies on PAR: a 2d side-scroller game where the user, by varying pupil size, controls the height at which a spaceship is moving aiming at colliding with bubbles to burst them and score points. The height at which the spaceship flies inversely depends on pupil area. The game is implemented on a Raspberry Pi board equipped with a IR camera and may record the time course of pupil size during the game, for off-line analysis. This application is intended as a tool to train and familiarize with the control of pupil size for alternative augmentative communication. Full article

The Montgomery modular multiplication is an integral operation unit in the public key cryptographic algorithm system. Previous work achieved good performance at low input widths by combining Redundant Binary Representation (RBR) with Montgomery modular multiplication, but it is difficult to strike a good balance between area and time as input bit widths increase. To solve this problem, based on the redundant Montgomery modular multiplication, in this paper, we propose a flexible and pipeline hardware implementation of the Montgomery modular multiplication. Our proposed structure guarantees a single-cycle delay between two-stage pipeline units and reduces the length of the critical path by redistributing the data paths between the pipelines and preprocessing the input in the loop. By analyzing the structure and comparing the related work in this paper, our structure ensures a lower area-time product while achieving a controllable and small area consumption. The comprehensive results under different Taiwan Semiconductor Manufacturing Company (TSMC) processes demonstrate the advantages of our structure in terms of flexibility and area-time product. Full article

Seasonal influenza (also known as flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to the WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of the influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results indicate that optimal vaccination campaigns with compromise tradeoffs between the two conflicting objectives can be designed effectively using CENSGA, providing policymakers with a number of alternatives to accommodate the best strategies. The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns. Full article

Transportation systems need more accurate predictions to further optimize traffic network design with the development and application of autonomous driving technology. In this article, we focus on highway traffic flow systems that are often simulated by the modified Greenshields model. However, this model can not perfectly match the true traffic flow due to its underlying simplifications and assumptions, implying that it is inexact. Specifically, some parameters affect the simulation accuracy of the modified Greenshields model, while tuning these parameters to improve the model’s accuracy is called model calibration. The parameters obtained using the $L_2$ calibration have the advantages of high accuracy and small variance for an inexact model. However, the method is calculation intensive, requiring optimization of the integral loss function. Since traffic flow data are often massive, this paper proposes a fast $L_2$ calibration framework to calibrate the modified Greenshields model. Specifically, the suggested method selects a sub-design containing more information on the calibration parameters, and then the empirical loss function obtained from the optimal sub-design is utilized to approximate the integral loss function. A case study highlights that the proposed method preserves the advantages of $L_2$ calibration and significantly reduces the running time. Full article

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds. Full article

One-dimensional (1D) and two-dimensional (2D) materials represent the emerging technologies for transistor electronics in view of their attractive electrical (high power gain, high cut-off frequency, low power dissipation) and mechanical properties. This work investigates the integration of carbon-nanotube-based field-effect transistors (CNT-FETs) and molybdenum disulphide (MoS₂)-based FETs with standard CMOS technology for designing a simple analog system integrating a power switching circuit for the supply management of a 10 GHz transmitting/receiving (T/R) module that embeds a low-noise amplifier (LNA) and a high-power amplifier (HPA), both of which loaded by nanocrystalline graphene (NCG)-based patch antennas. Verilog-A models, tuned to the technology that will be used to manufacture the FETs, were implemented to perform electrical simulations of the MoS₂ and CNT devices using a commercial integrated circuit software simulator. The obtained simulation results prove the potential of hybrid CNT-MoS₂-FET circuits as building blocks for next-generation integrated circuits for radio frequency (RF) applications, such as radars or IoT systems. Full article

The software industry has witnessed a growing interest in DevSecOps due to the premises of integrating security in the software development lifecycle. However, security compliance cannot be disregarded, given the importance of adherence to regulations, laws, industry standards, and frameworks. This study aims to provide an overview of compliance aspects in the context of DevSecOps and explore how compliance is ensured. Furthermore, this study reveals the trends of compliance according to the extant literature and identifies potential directions for further research in this context. Therefore, we carried out a systematic literature review on the integration of compliance aspects in DevSecOps, which rigorously followed the guidelines proposed by Kitchenham and Charters. We found 934 articles related to the topic by searching five bibliographic databases (163) and Google Scholar (771). Through a rigorous selection process, we selected 15 papers as primary studies. Then, we identified the compliance aspects of DevSecOps and grouped them into three main categories: compliance initiation, compliance management, and compliance technicalities. We observed a low number of studies; therefore, we encourage further efforts into the exploration of compliance aspects, their automated integration, and the development of metrics to evaluate such a process in the context of DevSecOps. Full article

AC loads may demand a fixed or variable voltage at their input terminals. When using inverters to power such loads, the response of the inverter must be precisely controlled to suit the demands of the AC loads. Inverters with higher efficiency and sensitivity will play an increasingly essential role as the need for solar PV applications in prospective green technology grows. To increase power quality and provide a reliable power source, an inverter architecture with harmonic reduction approaches is proposed. The multilevel inverter (MLI), unlike conventional inverters, is developed by cascaded single inverter units and is often used to connect renewable energy sources. As a result, they can be utilized to efficiently reduce harmonics. Among the three topologies, the most widely used in industries is the neutral-point clamped MLI. When the levels are raised, however, they demand a larger number of diodes. When the level of the flying capacitor exceeds three, several capacitors are necessary. As a result, the optimum option for synthesizing the right output voltage from several DC sources is a cascaded multilevel inverter (CMLI). Each link in a CMLI is connected by a single DC source; therefore, there is no voltage imbalance. However, getting equal DC voltages at the input of each unit is once again a limitation. In this work, various existing multilevel inverter topologies including hybrid topologies with different switching strategies are investigated and reported. The performance of a solar PV-based seven-level quasi-Z-source cascaded H-Bridge MLI (qZS-CHBMLI) has been thoroughly examined with the best switching scheme and best topology of multilevel inverters using MATLAB/Simulink. Full article

Filter optimization problems involve time-consuming simulations and many variables in the design. These problems require a large amount of computation. This paper proposes an adaptive online updating 1D convolutional autoencoders (AOU-1D-CAE) surrogate model for solving this computationally expensive problem. In the optimization process, an adaptive update surrogate mapping between input variables and output objectives is constructed within the surrogate model AOU-1D-CAE framework. AOU-1D-CAE can replace electromagnetic (EM) simulation software for data collection, and select and automatically use the accumulated data as training samples to train the AOU-1D-CAE surrogate model. With more and more training samples, the learning ability of the surrogate model is also becoming stronger and stronger. The experimental results show that the data collection efficiency of AOU-1D-CAE is greatly improved, and the automatic update of the sample set improves the prediction performance of the surrogate model. In this paper, the optimization framework is AOU-1D-CAE-assisted particle swarm optimization (PSO), and the surrogate model assists PSO to find the global optimal solution. In the PSO stage, PSO automatically updates and saves the optimal solution, and takes the optimal solution of each stage as the initial solution of the next optimization stage to avoid falling into the local optimal solution. The optimization time is greatly saved and the optimization efficiency is improved. The continuous iteration of PSO also improves the prediction accuracy of the surrogate model. The efficiency of the proposed surrogate model is demonstrated by using two cavity filters as examples. Full article

Digital designs complexity has exponentially increased in the last decades. Heterogeneous Systems-on-Chip integrate many different hardware components which require a reliable and scalable verification environment. The effort to set up such environments has increased as well and plays a significant role in digital design projects, taking more than 50% of the total project time. Several solutions have been developed with the goal of automating this task, integrating various steps of the Very Large Scale Integration design flow, but without addressing the exploration of the design space on both the software and hardware sides. Early in the co-design phase, designers break down the system into hardware and software parts taking into account different choices to explore the design space. This work describes the use of a framework for automating the verification of such choices, considering both hardware and software development flows. The framework automates compilation of software, cycle-true simulations and analyses on synthesised netlists. It accelerates the design space exploration exploiting the GNU Make tool, and we focus on ensuring consistency of results and providing a mechanism to obtain reproducibility of the design flow. In design teams, the last feature increases cooperation and knowledge sharing from single expert to the whole team. Using flow recipes, designers can configure various third-party tools integrated into the modular structure of the framework, and make workflow execution customisable. We demonstrate how the developed framework can be used to speed up the setup of the evaluation flow of an Elliptic-Curve-Cryptography accelerator, performing post-synthesis analyses. The framework can be easily configured taking approximately 30 min, instead of few days, to build up an environment to assess the accelerator performance and its resistance to simple power analysis side-channel attacks. Full article

Path tracking plays a crucial role in autonomous driving. In order to ensure the real-time performance of the controller and at the same time improve the stability and adaptability of the path tracking controller, a lateral path control strategy based on the improved LQR algorithm is proposed in this paper. To begin with, a discrete LQR controller with feedforward and feedback components is designed based on the error model of vehicle lateral dynamics constructed by the natural coordinate system. Then, a fuzzy control method is applied to adjust the weight coefficients of the LQR in real time according to the state of the vehicle. Furthermore, an update mechanism based on cosine similarity is designed to reduce the computational effort of the controller. The improved LQR controller is tested on a joint Simulink–Carsim simulation platform for a two-lane shift maneuver. The results show that the control algorithm improves tracking accuracy, steering stability and computational efficiency. Full article