Miracle-dz/UniFAR-FTUs huggingface dataset:
Wearable inertial sensors to recognize basic human motion: What technology for what activity?

Cyber-Physical Systems (CPS) are more and more present in many domains such as Industry 4.0, Smart Farming and Smart Healthcare Systems, among others, mainly due to the advantages offered by the combination of embedded electronics and data processing methods, inherent to CPS. An application field for which the characteristics of CPS are well adapted is the animal or human activity recognition, that can be useful to detect health troubles of the individuals under study. In this frame, this paper evaluates the adequation of different combinations of inertial sensors, together with an Artificial Neural Network based algorithm, to recognize basic human motion. The obtained experimental results are analyzed to corroborate if devices embedding different sensors, like Inertial Measurement Units, offer better performances than accelerometers when recognizing locomotion movements.
Eliminative Argumentation for Arguing System Safety - A Practitioner’s Experience

Safety cases are an essential artifact for establishing the safety of complex systems. Industrial use of safety cases varies between industries. Due to inconsistent regulatory guidance, numerous different strategies, notations, and techniques have been developed for safety case construction. Eliminative Argumentation (EA) has been proposed as a technique to systematically improve confidence in a safety case via ‘defeasible reasoning’ wherein reasons to doubt safety claims are introduced and subsequently eliminated. Elimination of doubt results in increased confidence. This paper reports on the application of EA to seven different software-intensive systems in the automotive, rail, and industrial control industries. Our experiences suggest that EA’s doubt-driven approach to safety argumentation increases confidence in a safety case and can be used to support activities such as independent safety assessments and safety verification and validation. From our experiences we synthesized into a set of lessons learned.
Virtual Machine Dynamic Deployment Scheme Based on Double-Cursor Mechanism

In accordance with the dynamically changing characteristics of user application load demand, the optimization of energy consumption in the process of virtual machine automatic deployment on the basis of the effective integration of user personalized resource demand in cloud platforms and the dynamic migration technology of virtual machines is studied in this work. Aiming at the problem of energy consumption optimization, an energy-efficient virtual machine deployment framework, which can be dynamically adjusted in accordance with the user’s actual resource needs, is designed. In the virtual machine deployment framework, this paper presents a virtual machine deployment selection algorithm based on a double-cursor control mechanism for the resource usage state of CPU and memory, which achieves a binocular optimization balance to a certain extent. Simulation experiment results show that when the virtual machine deployment framework proposed in this paper and the virtual machine deployment selection algorithm based on a double-cursor control mechanism are combined, the number of necessary active physical nodes in the cloud data center can be effectively controlled, and the frequency of virtual machine migration synchronization can be reduced. Consequently, the cloud data center can maintain low energy costs.
Selecting Architecture and Parameters of Deep Neural Networks for Computer Attack Classification

The article states a problem of a multiclass network classification of computer attacks. To solve it, the authors consider an option of applying deep neural networks. For this research, the authors selected the architecture of a deep neural network based on the strategy combining a set of convolution and recurrent LSTM layers. The research suggests optimizing the neural network parameters on the basis of a genetic algorithm. The authors present the simulation results that show an opportunity to solve the task of network classification in real time.
Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.
New Perspectives on Ethics and the Laws of Artificial Intelligence in the Investigation of Incidents Related to DDoS Attacks

The main problems of the modern development of artificial intelligence are the lack of ethical norms and standardization. These problems should be resolved globally and at the level of dialogue between developed countries. This will allow the development and creation of systems based on artificial intelligence algorithms that can be implemented in any country. This paper discusses approaches to the ethical regulation of security systems.
Robust Adaptive Control of A Disturbed Chua’s Circuit with Circuit Implementation

In this paper, a circuit implementation scheme is designed for the adaptive algorithm applied to the disturbed Chua’s circuit system. Firstly, in order to effectively suppress the adverse effects of unknown disturbances on the system, the input of the system is designed by using the method of adaptive parameter adjustment. Then, the asymptotic stability of the ADRC system is proved by lyapunov theorem. Besides, the pure circuit implementation scheme of the control algorithm is given by using the combination of some basic analog components. Finally, the software Multisim is used to do the circuit simulation experiments, and the experiment results show that the circuit design scheme of the algorithm is correct.
Multi-Channel Calibration for Airborne Post-Doppler Space-Time Adaptive Processing

This paper presents a fast and efficient multi-channel calibration algorithm for along-track systems, which in particular is evaluated for the post-Doppler space-time adaptive processing (PD STAP) technique. The calibration algorithm corrects the phase and magnitude offsets among the receiving channels, estimates and compensates the Doppler centroid variation caused by atmospheric turbulences by using the attitude angles of the antenna array. Important parameters and offsets are estimated directly from the radar range-compressed data. The proposed algorithm is compared with the state-of-the-art Digital Channel Balancing technique based on real multi-channel X-band data acquired by the DLR's airborne system F-SAR. The experimental results are shown and discussed in the frame of traffic monitoring applications.
Adaptive and Predictive SDN Control During DDoS Attacks

While Distributed Denial of Service (DDoS) attacks continue to plague the Internet, Software Defined Networks (SDNs) offer the promise of configuration elasticity for all devices in the plant to mitigate these attacks and stabilize the network in real-time. Since it is difficult to distinguish between legitimate and attack traffic, we propose a closed-loop feedback control mechanism using a multi-loop proportional, integral (PI) controller and a model predictive controller (MPC) that will regulate the system characteristics with disturbance rejection. Results from our setup implemented in the SDN platform show that our dynamic and predictive control models provide for a graceful degradation of real-time services in the SDN environment in real-time.
User Evaluation of Mobile based Smart Mango Pest Identification

In Indonesia, mango is one of the main fruit products that support the economy, and the production includes many different types. The cultivation of mango is affected by pests due to the lack of self-protection mechanism, which is observed in other plants. Such infestation affects the farmers with severe financial loss due to unsuccessful and substandard harvest. The farmers rely on their knowledge to address the pest issue. However, the pests have been detected in the late stages, and the damage cannot be repaired. So the early detection of manifestation of pests is essential for farmers. We develop an application, namely Mango Pest Identifier, that can detect the pest from the indication in the leaves, and it can recognize the pest in the initial phase. Addressing the challenges of pest infestation faced by farmers, the paper presents an overview of a mobile application, which aids in the early-stage detection of different categories of pest, which is then subsequently evaluated for usability. The usability evaluation was carried out based on User Experience using the User Experience Questionnaire (UEQ). The parameters in that Questionnaires are attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The result is satisfactory, as all UEQ scales have been evaluated positively.
Isolated AC/AC Converter With LLC Resonant Converter High-Frequency Link and Four-Quadrant Switches in the Output Stage

An AC-AC converter with a high-frequency link employing an LLC resonant converter operating in the vicinity of the resonance frequency was studied, in which a single output power stage is used, formed by a high-frequency AC-AC converter employing four-quadrant switches. The topology, its operation and the modulation strategy are described. The high-frequency stage switches located on the primary side of the transformer operate with soft switching of the ZVS type, while the four-quadrant switches that form the output stage operate with soft switching of the ZCS type. Experimental data obtained with a 1.5 kW experimental prototype (input <inline-formula> <tex-math notation="LaTeX">$220~V_{RMS}$ </tex-math></inline-formula>, output <inline-formula> <tex-math notation="LaTeX">$220~V_{RMS}$ </tex-math></inline-formula> and switching frequency 40 kHz), which was designed, built and tested in the laboratory, are reported herein. This converter can be considered a candidate for the building block of medium voltage solid-state transformers (SST) for power distribution systems.
A Generalization of Lindstedt–Poincaré Perturbation Method to Strongly Mixed-Parity Nonlinear Oscillators

In this paper, generalization for the Lindstedt–Poincaré perturbation method for nonlinear oscillators to a class of strongly mixed-parity oscillating system is established. In this extended and enhanced approach, two new odd nonlinear oscillators are introduced in terms of the mixed-parity oscillator. By combining the analytical approximate solutions corresponding to the two new systems, the accurate approximate solutions of the original mixed-parity oscillator are obtained. Comparing with the existing methods such as the perturbation method, the new solution methodology for the singular nonlinear system introduced is not only simple, but the combinatory solution is straight forward and it yields very accurate and physically insightful solutions. Using two typical examples, we demonstrated that this new proposed approach is capable of establishing highly accurate approximate analytical frequency and periodic solutions for small as well as large amplitude of oscillation. The new analytical methodology established will potentially shed new insights to the physical interpretations of strongly nonlinear oscillators including optoelectronic oscillators, pendulums and spring-masses.
Sectoral Horn and Patch Array Antenna Design for a 76–81 GHz High-Resolution Tomographic Radar

Nondestructive testing is employed in a wide range of industrial applications for quality control and process optimization. Recently, tomographic millimeter-wave radar has been investigated for highly demanding scenarios such as imaging of gas distributions. In such systems, application specific antenna design is crucial to ensure a sufficient number of signal paths between the sensors while at the same time reducing unwanted reflections. This paper describes the design of both a sectoral horn and a patch array antenna producing a fan-shaped beam in the frequency range of 76–81 GHz. Prototypes are realized utilizing innovative additive manufacturing techniques and characterized in a semi-anechoic chamber. The impact of mounting the antennas behind quartz glass is investigated in both simulation and measurement. Finally, the antenna performance is compared with commercial standard gain horn antennas in a tomographic radar setup demonstrating a significant improvement in the quality of the reconstructed permittivity distribution.
Clutter-Ridge Matched SR-STAP Technique for Non-stationary Clutter Suppression

Non-stationary clutter suppression is an attractive and challenging problem in space-time adaptive processing(STAP) algorithms. Although sparse recovery(SR) STAP can effectively alleviate strong clutter non-stationary characteristic with a few number of samples, there are still difficulties where the clutter components are not located on the discrete spacetime grid points of the dictionary, which results in significant performance loss. To overcome the off-grid problem, a clutter-ridge matched SR-STAP(CRM-SR-STAP) is proposed. In the proposed algorithm, clutter ridge is calculated at first according to prior knowledge including radar system parameters and modern inertial navigation system information. Then, the clutter-ridge matched dictionary is reconstructed to calibrate the uniformly discrete dictionary. Finally, the non-stationary clutter is suppressed by SR-STAP with this dictionary and the off-grid problem has been well-overcome. The proposed algorithm can effectively match the nonlinear characteristics of clutter under the non-side-looking array scenario, and the signal-to-interference-plus-noise ratio(SINR) loss also improves significantly. Simulations are given to verify the effectiveness of the proposed method.
E-Business Adoption Models in Organizational Contexts on The TAM Extended Model: A Preliminary Assessment

Small and Medium Enterprises (SMEs) performance studies is a step that can be implemented and applied to several types of SMEs. One of them is developing a model by combining and integrating a pre-existing model, the e-business adoption model, with the TAM (Technology Acceptance Model) model. The development of this model is done by using the logic of the IPO (input-process-output) and the causal model by combining, adopting, and adapting the previous model. This analysis is a quantitative study. The research objective is to determine the psychometric properties and to test the questionnaires distributed before the next research stage. The questionnaire uses a Likert scale with 5 Likert scales. The influence of the path formed is composed of 12 links and produces seven variables. The formed variables are developed into 35 indicators, where one variable form five indicators. The results of the validity and reliability tests showed positive results with two indicator rejections because of both items unfulfilled the used threshold. These two indicators were no longer used at the next research stage. Based on this, this model is worthy of consideration for testing the inner model so that it can provide a further picture of the development of the model.
Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Collaborative filtering (CF) is a technique used in recommender systems to provide meaningful suggestions based on known feedback obtained from like-minded users. The measure of similarity plays a critical role in the performance of neighborhood-based CF methods. However, conventional similarity measures suffer from limitations because they only consider the direction of the rating vectors. We propose a novel similarity measure that considers the semantic nuances of the ratings; in particular, it weights the contributions of ratings in proportion to the users’ degree of indifference towards the items. Additionally, to address the sparsity problem that affects the performance of CF techniques, we propose a switching hybrid method that predicts user ratings based on either our custom similarity measure or through user and item biases. We evaluated the proposed method on six different datasets and compared it with other CF methods. The results show that the proposed recommender consistently outperforms those using conventional similarity measures when the sparsity of the dataset is high.
Digital Twin Experiments Focusing Virtualisation, Connectivity and Real-time Monitoring

Industry 4.0 is re-shaping the manufacturing world, and amongst the several associated emerging methods and technologies, Digital Twin is becoming a popular approach both in industry and academia. However, the lack of knowledge about the characteristics, functionalities, best practices and benefits that it can provide, especially for small and medium enterprises, constraints its wider adoption. The use of real applications and demonstrators can contribute to exposing stakeholders to these new and innovative technologies and approaches, showing the applicability of Digital Twins. This paper presents several experiments in implementing Digital Twin for automation scenarios, considering different technologies and functionalities, namely in terms of virtualisation, connectivity and monitoring. Lessons learnt and challenges are also provided as a result of the experimental implementations.
Quadruped Robot Locomotion in Unknown Terrain Using Deep Reinforcement Learning

This paper is concerned with locomotion problems for the quadruped robot in unknown and unstructured terrains, utilizing the emerging deep reinforcement learning technique. The state-of-the-art deep deterministic policy gradient (DDPG) algorithm is leveraged to acquire the gait policy by learning from interactions with the environment. The framework of learning system is presented based on the actor-critic architecture, and the additional domain-specific knowledge is exploited for shaping the reward function to enhance the learning efficiency during training. Moreover, a simulation study is implemented to validate the performance of the proposed DDPG-based controller.
Collaborative Task Scheduling for IoT-Assisted Edge Computing

The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains. Despite increasing demands, modern IoT devices do not fully utilize somewhat over-provisioned computing resources. In this work, we introduce a new concept of IoT-assisted Edge Computing which makes use of consolidated idle resources in IoT devices for edge services through offloading edge tasks to nearby IoT devices. For the IoT-assisted edge computing be beneficent, two important conditions should be satisfied: 1) offloaded edge tasks to IoT devices do not hurt normal execution of local IoT tasks, and 2) computing resources in IoT devices should be effectively exploited to increase the throughput of edge services. To that end, we propose a collaborative task scheduling for IoT-assisted edge computing, in which an edge node determines where to offload edge tasks among participating IoT devices based on the offloaded execution time and energy consumption, and each IoT device determines when to execute the offloaded tasks considering local tasks execution. Experimental results show that the proposed scheme not only achieves near-optimal task throughput but also outperforms other scheduling algorithms in terms of deadline satisfaction ratio of time critical tasks, while guaranteeing deadlines of local tasks in IoT devices.
Clustering the Wireless Sensor Networks: A Meta-Heuristic Approach

Lifetime is one of the most critical indexes of the Wireless Sensor Network (WSN). In this paper, we propose a clustering protocol based on the meta-heuristic approach (CPMA). CPMA takes the network lifetime as the primary consideration and consists of two parts. The first part focuses on the online cluster head selection and network communication coordination. The selection is based on the Harmony Search (HS) Algorithm, which aims to reduce the total energy dissipation and smooth the energy distribution throughout the network. Currently, most clustering protocols cannot automatically tune the corresponding protocol parameters according to the diversity of different WSNs. To solve such issue, the second part of CPMA uses the Artificial Bee Colony (ABC) algorithm to optimize its crucial parameters. The optimization is offline and will be executed only once before the network is working. We make a detailed comparison of CPMA with classical clustering protocols. The results show that CPMA can better prolong the network lifetime and improve network throughput under almost all the network conditions. Furthermore, our simulation also exhibits that CPMA has good adaptability and performs well under different network lifetime definitions. All the results prove that CPMA has the advantages of being suitable and efficient for a wide number of WSN applications.
Comparison of Passive Time Reversal communication technique with Extended Kalman Filter for estimation of Shallow water multipath communication channel

This paper explains about the algorithm to improve the performance of underwater acoustic communication in shallow water. Passive time reversal (pTR) will reduce the effect of ISI with less expensive hardware. However, in open ocean shallow water, pTR could not eliminate multipath effect completely due to the channel variation and hence adaptive filters are used to compensate the channel temporal variations. Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms are combined with DFE and pTR at the receiver for faster convergence. To compare the performance of pTR-DFE, Extended Kalman filter (EKF) is used for channel coefficient estimation from the training sequence. EKF tracks the time varying channel using the decisions of DFE. The communication signal obtained from the experiment conducted off Bay of Bengal during June 2018 is used for this study. The performance of Extended Kalman with DEF is compared with conventional pTR with DFE.