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Publisher Copyright: Author In this paper, a single source five-level boost inverter has been proposed and analyzed. The proposed structure includes a five-level inverter and a single-input multi-output (SIMO) boost converter. In this structure, the DC link voltage has been controlled by controlling the implemented DC-DC boost converter. In addition, the inverter switching pattern has been obtained based on the pick current control (PCC) method. In order to test and prove the performance of the proposed structure, this system is tested with the local grid. Since the active and reactive powers have been controlled based on the PCC method, the generated energy in a renewable energy source can be transferred to the local grid with a controlled and high-quality current. In this paper, the proposed structure has been introduced and its operation in different modes has been analyzed completely. Furthermore, the design considerations of the required elements have been investigated. Also, the power loss analysis and the comparison results of the proposed converter with other similar structures have been presented. Finally, in order to test the practical performance of the proposed structure and prove the theoretical analysis, a 620 W laboratory prototype of this structure has been assembled and its performance with the local grid has been tested.

This paper presents an efficient control allocation (CA) strategy which significantly improves the flight performance of a planar hexacopter. This allocation maps the desired control vector composed of the thrust and torques in roll, pitch, and yaw axis, respectively, to propellers' speed. This paper shows that a CA strategy based on the classical approach of pseudo-inverse matrix only exploits a limited range of the vehicle capabilities to generate thrust and moments. A novel approach is presented, which is based on a weighted pseudo-inverse matrix method, called WCA, capable of exploiting a much larger domain in the control vector. The three weights involved in WCA are adapted online according to nonlinear laws which are analytically derived by solving symbolically the equivalent constraint least-squares problem, thus removing the need for online-optimization calculations. This solution allows for very fast real-time operations, suitable for autopilots with limited computing resources. This paper provides 1) a detailed analysis of the limitations of the classical control allocation scheme, 2) the mathematical development of the WCA algorithm, 3) simulations and real experiments which show that this WCA strategy outperforms the classical CA approach in terms of a) capability to generate the maximum roll and pitch torques possible, without generating undesired yaw torques, b) prioritizing the generation of thrust over attitude torques, thus achieving better altitude tracking despite aggressive maneuvers or the presence of a payload, and c) better behavior in case of actuator saturation, faults or even failures., IEEE Access, 11, ISSN:2169-3536

In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers. Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important in mission-critical applications, e.g., health care. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism and split learning, which facilitate DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented., 21 pages

Steer-by-wire systems represent a key technology for highly automated and autonomous driving. In this context, robust steering control is a fundamental precondition for automated vehicle lateral control. However, there is a need for improvement due to degrees of freedom, signal delays, and nonlinear characteristics of the plant which are unconsidered in the design models for the design of current steering controls. To be able to design an extremely robust steering control, suitable optimal models of a steer-by-wire system are required. Therefore, this paper presents an innovative nonlinear detail model of a steer-by-wire system. The detail model represents all characteristics of a real steer-by-wire system. In the context of a dominance analysis of the detail model, all dominant characteristics of a steer-by-wire system, including parameter dependencies, are identified. Through model reduction, a reduced model of the steer-by-wire system is then developed that can be used for a subsequent robust control design. Furthermore, this paper compares the steer-by-wire system with a conventional electromechanical power steering and shows similarities as well as differences.

With the development of Information and Communication Technologies, trust has been applied more and more in various scenarios. At the same time, different organizations have published a series of trust frameworks to support the implementation of trust. There are also academic paper discussing about these trust standards, however, most of them only focus on a specific application. Unlike existing works, this paper provides an overview of all current available trust standards related to communication networks and future digital world from several main organizations. To be specific, this paper summarizes and organizes all these trust standards into three layers: trust foundation, trust elements, and trust applications. We then analysis these trust standards and discuss their contribution in a systematic way. We discuss the motivations behind each current in forced standards, analyzes their frameworks and solutions, and presents their role and impact on communication works and future digital world. Finally, we give our suggestions on the trust work that needs to be standardized in future., 7 pages, 3 figures, Magazine paper under review

This paper investigates facilitating remote collection of a patient’s data in distributed system while protecting the security of the data, preserving the privacy of the patient’s ID, and preventing inference attack. The paper presents a novel framework called SPID stand for a Secure, ID Privacy, and Inference Threat Prevention Mechanisms for Distributed Systems. In designing this framework, we make the following novel contributions. The SPID presents a novel architecture that supports the use of a distributed set of servers owned by different service providers. The SPID allows the patient to access these servers using certificates generated by the patient. The SPID allows the patient to select one server to be the home server, and select a number of servers to be the foreign servers. The patient uses the foreign servers to upload data. The home server is responsible for collecting the patient’s data from the foreign servers and sending them to the healthcare provider. The SPID proposes a method for efficient verification of each request from the patient without searching in the server’s database for the verification key. This is done by using some of the Elliptic Curves Cryptography (ECC) properties. The SPID has been analyzed using a bench-marking tool and evaluated using queuing theory. The evaluation results indicate an efficient performance when the number of servers increases. We uses Shannon entropy method to measure the likelihood of the inference attack.

Non-linearity of wireless transceivers, specifically power amplifier (PA) non-linearity, could pose major limitations towards having high throughput, and cost and energy efficient wireless communication systems. Such limitations from the PA is typically compensated in the transmitter, e.g. by applying power back-off or performing digital-pre-distortion (DPD) aiming to linearize the transmitter. However, applying PA power back-off leads to lower energy efficiency, and lower output power, and hence lower coverage; and performing DPD results in higher complexity of the transmitters. This paper presents an alternative approach based on a receiver method to perform signal detection in the presence of distortions due to PA non-linearity. We propose a receiver technique using artificial neural networks (ANN) to compensate for the PA non-linearity at the receiver side. The paper presents link-level simulation results using pre-trained neural network models based on synthesized training data. The simulation results confirm that the designed receiver can tolerate higher distortions, hence allow the PA output power back-off to be reduced, leading to higher output power improving coverage, spectral efficiency, energy efficiency, and throughput.

This paper presents a comprehensive study of radio channels related to capsule endoscopy communications in ultra wideband wireless body area networks (UWB-WBAN) utilizing multiple antenna systems. The research includes 363 channel realizations, obtained through simulations using an anatomically realistic human voxel model and a capsule model. A capsule position is modelled in such a way that it moves throughout the entire intestinal tract. The study examines the frequency and time domain characteristics of the channels in various capsule locations, including the most challenging positions deep inside the tissues or far away from most of the antennas. Additionally, the propagation characteristics inside the abdominal tissue are studied by calculating the most obvious propagation paths based on power flow illustrations and reflecting the results with the channel impulse response analysis. The impact of capsule rotation is also studied. It is shown how small changes in capsule location can greatly impact on the channel characteristics if the thickness of the tissues between the capsule and the on-body antenna changes significantly. The paper concludes with statistical analysis of the channel data, including path loss and root mean square (RMS) delay spread. The results provide valuable insight into how the signal propagates inside different parts of the gastrointestinal tract. They show that channel attenuation remains moderate along most of the gastrointestinal tract, and that even the deepest locations in the small intestine area can be resolved with the use of directional on-body antennas and receivers with higher sensitivity.

Publisher Copyright: Author This paper focuses on the design aspects of the key components for a scalable phased-array system over the 200 GHz frequency range. A high-gain phase shifter chain for 220 to 240 GHz frequency range and a high-gain power amplifier (PA) with a high output power are designed in a 0.13-μm SiGe BiCMOS technology. The phase shifter chain includes a low-noise amplifier (LNA), a vector modulator phase shifter (PS), and a gain-enhancing amplifier. The LNA is a five-stage cascode design. The vector modulator core is realized by two variable gain amplifiers based on the Gilbert cell architecture. A four-stage cascode design is used for the gain-enhancing amplifier. The phase shifter chain shows a measured gain of 18 dB at 230 GHz with a 360° phase tuning range and more than 10 dB of gain control. The chip achieves a minimum measured noise figure of 11.5 dB at 230 GHz and shows a wideband noise characteristic. The complete phase shifter chain chip consumes a dc power of 153 mW and occupies a 1.41 mm2 area.A high-power PA that is critical for a large phased-array system is designed. This paper presents a unique 4-way power combining technique utilizing a differential quadrature coupler. The realized balanced PA occupies an area of 0.67 mm2 and shows a measured peak gain of 21 dB at 244 GHz. The PA consumes 819 mW of dc power and delivers a maximum saturated output power (Psat) of 7.1 dBm at 244 GHz and more than 4.3 dBm of Psat from 230 to 255 GHz.

In the process of digital image acquisition, color inconsistency is a common issue due to the influence of photographic conditions. The inconsistency of color affects the quality of digital orthophoto products and subsequent applications significantly, and this issue remains challenging both in the academia and industry. This paper proposes a color equalization method for aerial image based on block adjustment and an improved color balance model based on the analysis of causes for color deviation. Two nonlinear functions are used to simulate the process of color deviation and correct the color of images. Due to the existence of error, the color corrected images still have minor deviations in the color of the overlapped regions. Therefore, this research introduces an error equation to address this issue and uses the least square principle to calculate the adjustment. The simulation experiments and real experiments are carried out on several groups of aerial image data respectively. The results show that the method not only achieves better visual effect but also has a higher adjustment precision. Finally, the rationality and parameters of the model proposed in this paper are discussed.

The sharing of data is becoming increasingly important for the process and manufacturing industries that are using data-driven models and advanced analysis to assess production performance and make predictions, e.g., on wear and tear. In such environments, access to data needs to be accurately controlled to prevent leakage to unauthorized users while providing easy to manage policies. Data should further be shared with users outside trusted domains using encryption. Finally, means for revoking access to data are needed. This paper provides a survey on attribute-based approaches for access control to data, focusing on policy management and enforcement. We aim to identify key properties provided by attribute-based access control (ABAC) and attribute-based encryption (ABE) that can be combined and used to meet the abovementioned needs.We describe such possible combinations in the context of a proposed architecture for secure data sharing. The paper concludes by identifying knowledge gaps to provide direction to future research on attribute-based approaches for secure data sharing in industrial contexts. Validerad;2023;Nivå 2;2023-02-13 (hanlid);Funder: Arrowhead Tools Research Project (826452)

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas., Following a fruitful discussion with Professor Igor Mezic, the authors are providing a more detailed account of the roots of the modern-day engineering applications of the Koopman Operator Theory in this arXiv version

Interior permanent magnetic synchronous motors (IPMSMs) adopting concentrated windings have been widely used in industrial applications. To reduce operating costs, it is an important issue to enhance the efficiency of an IPMSM as much as possible while maintaining manufacturing costs. In general, an IPMSM used for an industrial application always operates in a specific operating area according to the required load. Therefore, this paper has two purposes. The first purpose is to propose a novel rotor structure which can enhance efficiency at the target wide-speed middle-torque operating area without additional manufacturing costs. The second purpose is to clarify the design method for a suitable rotor structure depending on its target operating area. Reducing losses is the key to enhancing efficiency. This paper first examines the effects of adopting large flux barriers and a disproportional airgap on copper and iron losses, and clarifies their merits and respective high-efficiency operating areas. Furthermore, to take advantage of the two rotor structures, a novel rotor structure which employs both large flux barriers and a disproportional airgap has been proposed. 2D-FEM (Finite-Element Method) is used for discussion first, and a prototype machine is manufactured to verify the 2D-FEM results. Both 2D-FEM and experimental results show that the proposed rotor structure can enhance the efficiency of an IPMSM most effectively at the target operating area. Moreover, for a low-speed high-torque operating area, adopting only large flux barriers is most suitable. And for a high-speed low-torque operating area, adopting only a disproportional airgap is most suitable.

As autonomous platforms become more advanced, it is useful to obtain more information about the environment. Classic vision sensors such as RGB-D cameras, LiDAR, or acoustical imaging cameras are capable of accurately displaying the location of an object in 3D space and are widely used for this purpose. However, a problem arises when the measurement environment becomes more complex and measurement data might begin to suffer in terms of accuracy. Imaging sonar sensors are developed specifically for these environments but are limited in terms of frame rate due to the inherent slow nature of sound. This paper will propose a method to increase the amount of useful information obtained from the available data from a single measurement in post-processing. Utilizing a signal in combination with a Doppler velocity-tuned matched-filter bank it is possible to estimate the radial velocity of an object with respect to the sensor’s location. This allows the robot to make better decisions when it comes to path planning and collision avoidance, as a rapidly approaching object requires a different action than a stationary object or one moving away from the sensor. Another advantage of this system is that a single seemingly-large object might be identified as two close lying objects with different speeds. This paper serves as a proof-of-concept with results from a realistic simulation environment using an imaging sonar sensor and shows that the proposed method is perfectly suitable for making accurate estimations of an object’s radial velocity.

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system. As an integral part of the EHR electronic model, public cloud servers store medical data. Unfortunately, public cloud servers are prone to security and privacy breach. This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment. The main objective of the proposed anonymization algorithm is to obscure the patient’s cardiac information during transmission and to protect information stored in the cloud database. Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method. Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data. Therefore, confidentiality feature is preserved. This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation. Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods. Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud. Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points. In a real-time experimental testbed, the implemented proposed system was successful.

International audience; This paper develops an original design method of high-pass (HP) negative group delay (NGD) integrated circuit (IC). The considered HP-NGD IC is based on a passive topology which is essentially composed of resistor-inductor (RL) network. The paper presents the first time that an unfamiliar HPtopology is designed in miniaturized circuit implemented in 130-nm CMOS technology. The theory of unfamiliar HP-NGD topology based on the voltage transfer function (VTF) analysis is elaborated. The design equations with synthesis formulas of the resistor and inductor are established. The HP-NGD IC CMOS design methodology is introduced. The feasibility of the miniature NGD IC implementation is approved by design rule check (DRC) and layout versus schematic (LVS) approaches. The HP-NGD passive IC is designed in 130-nm CMOS technology. The HP-NGD topology is constituted by RL-network based on CMOS high Ohmic unsalicided N + poly resistor and symmetrical high current spiral inductor. Then, the schematic and layout simulations are presented. The validity of the 130-nm CMOS HP-NGD design is verified by the investigation of 225 µm × 215 µm chip two different miniature circuit proofs-of-concept (POC). The HP-NGD behavior is validated by comparison between the calculated, and schematic and post-layout simulations of the HP-NGD POCs carried out by a commercial tool. As expected, the group delay and VTF magnitude diagrams are in very good correlation. HP-NGD optimal value, NGD cutoff frequency and attenuation, of about (−31 ps, 141 MHz, −3 dB) and (−47 ps, 204 MHz, −5 dB) are obtained from the miniature POCs. INDEX TERMS 130-nm CMOS technology, design method, negative group delay (NGD), high-pass (HP) NGD function, HP-NGD theory, integrated circuit (IC) design, synthesis equation, RL-network passive topology, miniature circuit.

Software code is present on multiple levels within current Internet of Things (IoT) systems. The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the project to which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author’s team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.

This paper presents a non-iterative online approach to identify the modeling parameters of a photovoltaic (PV) module. It is motivated by the fact that accurate and reliable modeling of distributed energy resources (DERs) in DC Microgrids improves their stability and efficiency under a wide range of operational conditions. In particular, the case where PV modules are used as DERs in their current-source region is considered. The proposed method addresses the limitations associated with parameter identification in these settings. Specifically, the method works under varying temperature and insolation conditions relying only on current and voltage sensors that already exist in the power electronic converters tying the PV DER to the Microgrid. Also, its algorithm is practical and reliable as it does not rely on a priori knowledge or an initial guess, and it is non-iterative, so it does not risk divergence (or require proof of convergence) as other iterative algorithms. Moreover, it is both fast and of a low computational complexity, which enables its implementation on microcontrollers within PV DER systems. The development of this method is detailed in the paper along with its application steps to facilitate its adoption. Furthermore, an experimental setup was used to test the proposed method under different ambient conditions and demonstrated its efficacy with algorithm execution times of under 1 second and high modeling accuracy on a microcontroller.

Power inversion algorithms (PI) are widely used by ant-jamming receivers in harsh environment, but it also induces pseudo-range measurement error which is fatal to high precision receivers. In this paper, it proposes a more accurate method based on the S Curve Bias(SCB) to evaluate the pseudo-range bias which can actually affect the positioning results, as it excludes a fixed time delay contained in the directly differing method. When using this method to assess the bias, it is found that pseudo-range errors can be influenced by many factors including the type of interference. Simulation as well as experiment based on a 4-element array antenna are designed and carried out. The simulation has shown that both SOP-PI and STAP-PI will bring bias to pseudo-range measurement. Specifically, the maximum bias caused by SOP-PI is 1m while the bias caused by STAP-PI is up to 10m. Moreover, comparing to the method which directly differs the pseudo-range of output signal and the reference signal,the method proposed in this paper has better performance. Furthermore, the experimental results verify the effectiveness of the proposed evaluating method.

Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy.

Visible Light Communication (VLC) is a key technology for the sixth-generation (6G) wireless communication thanks to the possibility of using artificial environmental lights as a data transfer channel. Although VLC systems are more resistant against interference and less susceptible to security vulnerabilities like most wireless networks, VLC is even inherently susceptible to eavesdropping attacks. Moreover, since VLC is considered an enabling technology for 6G, specific mechanisms are needed to enforce data security. This paper considers improving the security of the next generation of wireless communications by using the Watermark Blind Physical Layer Security (WBPLSec) in VLCs. The main intuition is that RGB LEDs offer the possibility for Wavelength Division Multiplexing (WDM) as a useful support for the Spread-Spectrum (SS) watermarking. In this paper, we propose an approach that aims at obtaining VLC Physical Layer Security (PLS) by combining watermarking with an RGB LED jamming. We provide a performance analysis of the proposed security architecture based on the secrecy capacity in terms of its existence and outage probability. We prove that WBPLSec can be used to significantly improve confidentiality in the next generation of wireless communications. The results offer the possibility of creating a secure region around the legitimate receiver by leveraging the jamming optical power.

With the rapidly increasing deployment of distributed energy resources (DERs), electric power systems across the world are moving towards more distributed designs and operations. While not every distributed device will be part of a microgrid, microgrids do offer solutions for scalability and interoperability, and for coordination with centralized utility control systems. The challenge is that the microgrids will be heterogenous with respect to ownership, operational objectives, resource mix, and control systems. Due to these variations within the population of microgrids, there will be significant challenges with coordinating their operations to achieve global objectives using existing centralized controls and optimizations. This paper presents a framework for the coordination of heterogeneous groups of microgrids using consensus algorithms. The framework enables the active engagement of utility and non-utility microgrids using a distributed architecture to support end-use loads during the loss of the bulk power system. Results from laboratory testing and preliminary implementations, as well as details of an ongoing operational deployment at the Electric Power Board of Chattanooga are presented in this paper.

This paper proposed a cross-domain self-authentication scheme to address the “information isolated island” problem of users’ identities storage in servers and the “redundant registration problem” of users’ identities for Autonomous Valet Parking (AVP). This scheme adopts a decentralized anonymous authentication method to relieve the authentication center’s service load. Users are segregated into two categories to increase authentication efficiency: inexperienced and regular users. For the former, the paper explores a self-authentication mechanism based on verification parameters. Then, its valid personal information, pseudonym and public key, were stored in a consortium blockchain (PseIDChain) as the transaction records so that they can be securely shared among servers located in different domains. For the latter (regular users), an efficient authentication mechanism, searching users’ personal information on PseIDChain by the smart contract, was proposed. Security proof and simulation results show that the designed scheme has superior security to the existing schemes. Its authentication efficiency is 80.29% and 50.45% higher than the traditional anonymous and batch authentication schemes.

This paper analyzes and evaluates various topologies of yokeless rotary-linear (RL) machine with permanent-magnet (PM) mover targeting for the achieving high dynamic performance. For the conventional yokeless RL machines, the single-stator topology features limited slot area for armature winding excited for both motions, and the coupled magnetic fields of both motions requires complex control strategy. In order to mitigate these issues, a double-stator RL machine with hybrid PMs array, which adopts the PMs with six magnetization directions, is proposed in this paper. An improved PMs array is introduced for the double-stator topology to achieve decoupled control for both motions. In addition, an analytical model of the radial flux density is derived to reveal the characteristics of the magnetic fields in different topologies. The parametric studies towards key dimensions are conducted to provide design guidelines and obtain the optimal design. Finally, the electromagnetic performances of the proposed design are compared systematically with the conventional single-stator counterparts. It is revealed that the proposed topology can enhance the performances in both motions, namely rotary and linear motions. Ministry of Education (MOE) Published version This work was supported by the Ministry of Education (MOE), Singapore, under its MOE Academic Research Fund (AcRF) Tier 1 Program under Grant 2019-T1-002-064.

Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

During the New Space era small countries are becoming important players in the space business. While space activities are rapidly increasing globally, it is important to make operations in a sustainable and safe way in order to preserve satellite services for future generations. Unfortunately, the sustainability aspect has been largely overlooked in the existing surveys on space technologies. As a result, in this survey paper, we discuss the multi-layer networking approaches in the 6G era specifically from the sustainability perspective. Moreover, our comprehensive survey includes aspects of some interesting industrial, proprietary, and standardization views. We review the most important regulations and international guidelines and revisit a three-dimensional architecture vision to support the sustainability target for a variety of application areas. We then classify and discuss space safety paradigms that are important sustainability enablers of future satellite communications. These include space traffic management, debris detection, environmental impacts, spectrum sharing, and cyber security aspects. The paper also discusses advances towards a planned European connectivity constellation that could become a third flagship infrastructure along with the Galileo and Copernicus systems. Finally, we define potential research directions into the 2030s., Accepted for IEEE Access

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic data generators have been introduced in the last decade fueled by advancement in machine learning and by the increased demand for fast and inclusive data sharing, yet their utility is not well understood. Prior research tried to compare the utility of synthetic data generators using different evaluation metrics. These metrics have been found to generate conflicting conclusions making direct comparison of synthetic data generators very difficult. This paper identifies four criteria (or dimensions) for masked data evaluation by classifying available utility metrics into different categories based on the measure they attempt to preserve: attribute fidelity, bivariate fidelity, population fidelity, and application fidelity. A representative metric from each category is chosen based on popularity and consistency, and the four metrics are used to compare the overall utility of four recent data synthesizers across 19 datasets of different sizes and feature counts. The paper also examines correlations between the selected metrics in an attempt to streamline synthetic data utility.

Copyright © 2022 The Author(s). Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill this gap, this paper proposes Intelligent SDN Multi Spike Neural System (IMSNS) by implementing Moderately Multi-Spike Return Neural Networks (MMSRNN) controller with time based coding achieving remarkable reduction on energy consumption and accurate traffic identification to predict the most appropriate network slice. In addition, this paper proposes another intelligent Recurrent Neural Network (RNN) controller for load balancing and slice failure condition. The current researchers have adopted the: accuracy, precision, recall and F1-Score, the simulation results revealed that the proposed model could provide the accurate 5G network slicing as compared with a convolutional neural network (CNN) by 5%. 10.13039/501100007914-Brunel University London, U.K.

Computerisation and digitalisation are shaping the world in fundamental and unpredictable ways, which highlights the importance of computing education research (CER). As part of understanding the roots of CER, it is crucial to investigate the evolution of CER as a research discipline. In this paper we present a case study of a Finnish CER conference called Koli Calling, which was launched in 2001, and which has become a central publication venue of CER. We use data from 2001 to 2020, and investigate the evolution of Koli Calling's scholarly communities and zoom in on it's publication habits and internalisation process. We explore the narrative of the development and scholarly agenda behind changes in the conference submission categories from the perspective of some of the conference chairs over the years. We then take a qualitative perspective, analysing the conference publications based on a comprehensive bibliometric analysis. The outcomes include classification of important research clusters of authors in the community of conference contributors. Interestingly, we find traces of important events in the historical development of CER. In particular, we find clusters emerging from specific research capacity building initiatives and we can trace how these connect research spanning the world CER community from Finland to Sweden and then further to the USA, Australia and New Zealand. This paper makes a strategic contribution to the evolution of CER as a research discipline, from the perspective of one central event and publication venue, providing a broad perspective on the role of the conference in connecting research clusters and establishing an international research community. This work contributes insights to researchers in one specific CER community and how they shape the future of computing education

Machine learning algorithms have been used for detection (and possibly) prediction of Alzheimer’s disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer’s disease using genetic data is still in its primitive stage. The aim of this paper was to evaluate the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer’s disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer’s disease. A systematic review of quantitative studies was conducted using three search engines (PubMed, Web of Science and Scopus), and included studies between 1st of January 2010 and 31st December 2021. Keywords used were ‘Alzheimer’s disease(s)’, ‘GWAS, ‘Artificial intelligence’ and their synonyms. After applying the inclusion/exclusion criteria, 24 studies were included. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). The main findings showed that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.

Data Access Statement: This research did not use any experimentally generated data or data from any publicly available dataset. Model definitions (including the specified probability distributions) and parameter values (including the initialization parameters) provided in the paper are adequate for reproducing the exact qualitative behavior of the algorithms illustrated in the paper. © Copyright 2022 The Authors. Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously. 10.13039/501100001409-Department of Science and Technology (DST), Government of India, through Innovation in Science Pursuit for Inspired Research (INSPIRE) Faculty Award (Grant Number: DST/INSPIRE/04/2018/000089)

In this paper, a profitable strategy of utilizing solar and bio-energy available in a rural location for meeting the energy deficit is discussed. The proposed model can provide 24-hour continual power supply to the vicinity without the need for any battery. The land requirement is minimal in the proposed model making it cost-effective for implementation. The paper provides a step by step procedure to optimally size the solar and bio-power plants and schedule their operation for maximum utility. A programmable smart meter connected with a central system having a user-friendly control algorithm has also been proposed at every home for end-user profitability. The smart meter will indicate energy consumption in local home, its solar energy generation, vicinity power demand, current tariff of power usage, energy imported/exported and other required details. The proposed model is discussed in detail for comprising of two adjacent villages named Gaddikheri and Tajamajra in Rohtak (Haryana), North India (having 768 houses). The technical and financial analysis at this location is done using RETScreen expert software and the results are presented. The system is found to provide 24-hr power supply and also be profitable with the capital cost being recovered in less than 8 years time. Apart from this, the proposed system also provides other benefits like reduction in greenhouse gas emission (by 93%), auto power theft control, bio-fertilizers, and a clean environment.

With the development of mature communication network technologies such as 5G, edge computing and slicing, business applications in areas such as Telematics and intelligent transportation also need the support of vehicle-road collaboration technologies. Due to the mobility of vehicles and the heterogeneity of communication interfaces, it brings many challenges for data transmission and processing of Telematics. Therefore, network resources, roadside unit devices and sunken edge nodes need to work together to guarantee real-time network communication. Thus, they can jointly help improve the quality of network services and user experience, and show the level of vehicle-road collaboration wisdom in terms of safety, efficiency and service. In view of the future 5G network-related technologies and C-V2X development trend, this paper provides a comprehensive technical solution framework for Telematics based on end-edge collaboration, combining network resource deployment optimization and edge caching technologies for video transmission tasks in Telematics application scenarios. By adding 5G base station type RSU and UAV mobile base station to work jointly, network resources are better allocated for scalable coded video, and a relatively better edge caching strategy is obtained based on user mobility analysis. Aiming to improve the network system performance, the vehicle-side, road-side and edge-side are jointly considered to improve the utilization efficiency of limited network resources. In this paper, we hope to provide a practical and effective service architecture for Telematics video application scenarios through a convergence scheme of network resource allocation and edge caching configuration.

Pulswidth modulation (PWM) methods were developed to grant the voltage source inverter (VSI) output signal better quality measures. The PWM method that VSI relies on must consider the utilization of the direct current (DC) source, especially when the drive system works in the overmodulation region. In this paper, different zero-sequence harmonic injection PWM methods are proposed. Two essential objectives are optimized by minimizing total harmonic distortion and maximizing the DC bus utilization. All schemes’ results are compared with each other and with some of the commonly used PWM schemes. This paper concludes the usage of third, ninth, and fifteenth harmonic injections and any combination of them to reach a new PWM method that overtakes the disadvantages of other methods. Meanwhile, it provides a handy tool to operate VSI optimally. Ultimately, this work concludes to what extent is the zero-sequence injection is helpful. The work is evaluated experimentally, and the practical results match the simulation outcomes.

After reviewing the current state of explainable Artificial Intelligence (XAI) capabilities in artificial Intelligence (AI) systems developed for critical domains like criminology, engineering, governance, health, law and psychology, this paper proposes a domain-independent Accountable explainable Artificial Intelligence (AXAI) capability framework. The proposed AXAI framework extends the XAI capability to let AI systems share their decisions and adequately explain the underlying reasoning processes. The idea is to help AI system developers overcome algorithmic biases and system limitations through incorporation of domain independent AXAI capabilities. Moreover, existing XAI methods would neither separate nor quantify measures of comprehensibility, accuracy and accountability so incorporating and assessing XAI capabilities remains difficult. Assessment of the AXAI capabilities of two AI systems in this paper demonstrates that the proposed AXAI framework facilitates separation and measurement of comprehensibility, predictive accuracy and accountability. The AXAI framework allows for the delineation of AI systems in a three-dimensional AXAI space. It measures comprehensibility as the readiness of a human to apply the acquired knowledge. The system accuracy is measured as the ratio of the test and training data, training data size and the observed number of false-positive inferences. Finally, the AXAI framework measures accountability in terms of the inspect ability of the input cues, the processed data and the output information, for addressing any legal and ethical issues.

Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a three-step preprocessing is proposed to enhance the quality of MRI images, along with a new Deep Convolutional Neural Network (DCNN) architecture for effective diagnosis of glioma, meningioma, and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 98.22% overall, 99% in detecting glioma, 99.13% in detecting meningioma, 97.3% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.

The preparation of a quantum superposition is the key to the success of many quantum algorithms and quantum machine learning techniques. The preparation of an incomplete or a non-uniform quantum superposition with certain properties is a non-trivial task. In this paper, an $n$-qubits variational quantum circuit using partial negation and controlled partial negation operators will be proposed to prepare an arbitrary quantum superposition. The proposed quantum circuit follows the symmetries of the unitary Lie group. The speed of the preparation process and the accuracy of the prepared superposition has a special importance to the success of any quantum algorithm. The proposed method can be used to prepare the required quantum superposition in $\mathcal{O}(n)$ steps and with high accuracy when compared with relevant methods in literature., Comment: 15 pages. This paper was supported financially by the Academy of Scientific Research and Technology (ASRT), Egypt, under initiatives of Science Up Faculty of Science Grant o 6558). (ASRT) is the 2nd affiliation of this research

Brain-inspired architectures are gaining increased attention, especially for edge devices to perform cognitive tasks utilizing its limited energy budget and computing resources. Hyperdimensional computing (HDC) paradigm is an emerging framework inspired by an abstract representation of neuronal circuits’ attributes in the human brain. That includes a fully holographic random representation, high-dimension vectors representing data, and robustness to uncertainty. The basic HDC pipeline consists of an encoding, training and comparison stages. The encoding algorithm maps different representations of inputs into a single class and stores them in the associative memory (AM) throughout the training stage. Later, during the inference stage, the similarity is computed between the query vector, which is encoded using the same encoding model, and the stored classes in the AM. HDC has shown promising results for 1D applications using less power, and lower latency than state-of-the-art digital neural networks (DNN). While in 2D applications, convolutional neural network (CNN) still achieves higher classification accuracy at the expense of more computations. In this paper, a comprehensive study on the HDC paradigm, main algorithms, and its implementation is presented. Moreover, the main state-of-the-art HDC architectures for 1D and 2D applications are highlighted. The article also analyzes two competing paradigms, namely, HDC and CNN, in terms of accuracy, complexity, and the number of operations. The paper concluded by highlighting challenges and recommendations for future directions on the HDC framework.

Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each other in clutters. Unlike to the existing MTT systems, the linear multi-target (LM) algorithm modifies the measurement detection followed by neighbored tracks as a clutter, hence, it updates the track without the influence of other tracks. Thus, LM technique is a computationally efficient algorithm that allows the multi-target system to play like a single target tracking algorithm. Smoothing maximizes the state estimation accuracy and reduces estimation error based on future scan measurement. However, only few research paper focused on the LM algorithm without utilizing the benefits of the smoothing. This paper presents Rauch-Tung-Striebel Smoothing in the linear multi-target based on integrated probabilistic data association (RTS-LMIPDA). The RTS-LMIPDA algorithm fuses forward and backward LM track predictions to obtain the smoothing prediction which is required to calculate the smoothing multi-target state estimates in the forward track. Numerical analysis is presented to illustrate the estimation accuracy and false track discrimination (FTD) performances of RTS-LMIPDA in comparison to the existing MTT algorithms using the simulations.

Lately, gamification (i.e., employing game-design elements and game principles in non-game contexts) has gained massive popularity and widespread usage in various areas, including education. However, gamification deployment in education in general and remote education in particular still faces many challenges that mainly influence user quality of experience. These challenges include lack of dedicated communication-based systems, potential additional load on teachers, absence of customization and personalization for users, and no support for advanced technology-enhanced learning (TEL). This paper investigates the use of gamification for networked delivery of science, technology, engineering and mathematics (STEM) subjects. It proposes an innovative gamification framework, the NEWTON-enhanced gamification model (N-EGM), which was designed as part of the European Horizon 2020 project NEWTON. The paper also describes the proposed N-EGM model deployment in the gamification engine of a real learning management system and its associated communication and networking solution. The communication support provides easy-to-use gamification configuration functionality and efficient data collection and processing in a heterogeneous technology context. Finally, the paper evaluates the proposed N-EGM model as part of a NEWTON project pilot deployed in a Romanian school. The results demonstrate the effectiveness of the proposed gamification solution in improving both students’ learning experience and their engagement, while also increasing student knowledge gain.

For the last few years, cases of applying articial intelligence (AI) to engineering activitiestowards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematicallyreach higher and productivity levels, owing to its holistic perspective and consideration of economic andenvironmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE)we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AItechniques, (2) the current AI-improved LCE subelds and (3) the subelds with highly enhanced by AI.A specic set of inclusion and exclusion criteria were used to identify and select academic papers fromseveral elds, i.e. production, logistics, marketing and supply chain and after the selection process describedin the paper we ended up with 42 scientic papers. The study and analysis show that there are manyAI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, andInfrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall,the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance andRepair are the top explored LCE subelds whereas logistics and Procurement are the least explored subareas.Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strongresearch funding and focus on Industry 4.0; Germany is standing out with numbers of publications. Thein-depth analysis of selected and relevant scientic papers are helpful in getting a more correct picture ofthe area which enables a more systematic approach to AI-LCE in the future. Funding: Adapt 2030 (Adaptive lifecycle design by applying digitalization and AI techniques to production) (Grant Number: 2019-05589)10.13039/501100001858-VINNOVA, Sweden’s Innovation Agency Adapt 2030

Smart cities often rely on technological innovations to improve citizens’ safety and quality of life. This paper presents a novel smart mobility system that facilitates people’s access to public mobility while preserving their privacy. In contrast to several well-known smart mobility systems discussed in this paper, the one we propose combines privacy guarantees with user friendliness. Specifically, the system is based on a zero-interaction approach whereby a person can use public transport services without any need to perform explicit actions. Operations related to ticket purchases and validation were fully automated. The system is also designed with the privacy-by-design paradigm to preserve user privacy as much as possible. Throughout the paper, several technical details are discussed as well to describe a prototype version of the system that was implemented. The prototype was successfully tested in the city of Imola (Emilia Romagna, Italy) to prove the validity of the system in the field.

Targets of cyber crime are not exclusive to the private sector. Successful cyber attacks on nation-states have proved that cyber threats can jeopardize significant national interests. In response, nation-states have begun to handle cyber threats at the national defense level, which is titled ‘cyber defense.’ The cyber defense sector is related to national security, therefore requires robust security technology. Contrary to normal systems, blockchain provides strong security properties without a centralized control entity, and as such its application in the cyber defense field is under the spotlight. In this paper, we present opportunities blockchain provides for cyber defense, research and national projects, and limitations. We constructed a survey of government documents, interviews, related news, technical reports, and research papers from 2016 to 2021. As a result, our research contributes to reducing the gap in blockchain for cyber defense by systematically conducting research and analysis. In our research, we found that not only research but also government-led plans are actively promoting blockchain, which demonstrates that blockchain will play a remarkable role in cyber defense. This paper concludes with suggestions for future research in aspects of the blockchain technology, evaluation, and survey.

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.

DevOps is a set of practices and a cultural movement that aims to break down barriers between development and operation teams to improve collaboration and communication. Different organizations have embraced DevOps principles due to the massive potential, such as a much shorter time to production, increased reliability and stability. However, despite the widespread adoption of DevOps and its infrastructure, there is a lack of understanding and literature on the key concepts, practices, tools, and challenges associated with implementing DevOps strategies. The main goal of this research paper is to explore and discuss challenges related to DevOps culture and practices. Moreover, it describes how DevOps works in an organization, provides a detailed explanation of DevOps, and investigates the cultural challenges that organizations face when implementing DevOps. The proposed paper reveals ten critical challenges that need to be addressed in adopting the DevOps culture. The challenges are further analyzed on the basis of the various continents. According to the findings, the following critical challenges are considered during the implementation of a DevOps culture: lack of collaboration and communication, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.

Conventional theory for designing strictly nonblocking networks, such as crossbars or Clos networks, assumes that these networks have a centralized topology. Many new applications, however, require networks to have a distributed topology, like 2-D mesh or torus. In this paper, we present a new theoretical framework for designing such nonblocking networks. The framework is based on a linear programming formulation originally proposed for solving the hose-model traffic routing problem. The main difference, however, is that the link bandwidths in our problem must be discrete. This makes the problem much more challenging. We show how to apply the developed theorems to tackle the problem of designing bufferless NoCs (networks-on-chip). The proposed bufferless NoCs are deadlock/livelock-free and consume significantly less power than their buffered counterparts. We also present a multi-slice technique to reduce node capacity variations. This can make the proposed NoC architecture more cost efficient in a VLSI (very large-scale integration) implementation. In addition, we offer a detailed delay/throughput performance evaluation of the proposed bufferless NoC in the paper.

Perception is the fundamental task of any autonomous driving system, which gathers all the necessary information about the surrounding environment of the moving vehicle. The decision-making system takes the perception data as input and makes the optimum decision given that scenario, which maximizes the safety of the passengers. This paper surveyed recent literature on autonomous vehicle perception (AVP) by focusing on two primary tasks: Semantic Segmentation and Object Detection. Both tasks play an important role as a vital component of the vehicle’s navigation system. A comprehensive overview of deep learning for perception and its decision-making process based on images and LiDAR point clouds is discussed. We discussed the sensors, benchmark datasets, and simulation tools widely used in semantic segmentation and object detection tasks, especially for autonomous driving. This paper acts as a road map for current and future research in AVP, focusing on models, assessment, and challenges in the field.

This paper first reviews the related concepts of agricultural supply chain and the current research status of agricultural inventory models; proposes the classification standard of supply chain structure from four different aspects: the complexity of the supply chain node enterprises, the reliability of the supply chain and the optimization goal. In addition, this article starts from the characteristics of agriculture itself, according to the actual needs of the agricultural supply chain, considers the feasibility of the solution strategy from different sides and gives related models, which solves the related problems in the agricultural supply chain to a certain extent. At present, there are many researches on resource allocation algorithms at the edge of mobile networks, but the existing resource allocation algorithms still have the problem of high computational complexity and can still solve the above problems in resource utilization optimization. In order to solve these problems, this paper proposes a system architecture of the agricultural industry Internet of Things based on edge computing. The main motivations of this article are to improve the stability and usability of the system, and to enhance agricultural wisdom, so as to provide a reference for the development of precision agriculture. According to this method, Firstly, from the perspective of how to improve the energy efficiency of mobile terminals, methods to improve the energy efficiency of mobile terminals are studied in multi-user systems where mobile terminals compete for MEC servers with limited computing resources. Then, this paper focuses on the waste of computing resources caused by user movement under the scenario of limited server coverage in MEC system, and studies the problem from the perspective of effective management of computing resources. Experimental results show that the proposed algorithm has good performance.

Based on people’s enthusiasm for swimming and their pursuit of swimming skill at a higher level, this paper proposes a non-contact motion guidance system based on optical motion capture technology, which aims to improve their swimming technique conveniently, scientifically and quickly. The swimming actions can be captured using one smartphone underwater, and the app on the phone will process the video and give suggestions based on the reference postures derived from statistics of professional swimmers for improvement of the swimming performance. In this system, the Canny operator was applied to detect the edge of the image captured from the swimming video, and the Hough line was implemented to recognize and locate the key position information which refers to the angle between knee and hip joints, and the angle between the trunk and horizontal plane. Furthermore, by comparing the key position information with the data of standard swimming, improvement measures can be given to improve the swimming skills of swimmers. As the result of the experiments, the system can accurately capture the swimmer’s movement state, locate the incorrect posture of the swimmer, and provide the corrective information in real-time. Although the paper only took breaststroke as an example, the proposed method can be extended to the other swimming styles.

This paper presents a new method for computationally effective implementation of a discrete-time fractional-order proportional–integral–derivative (FOPID) controller. The proposed method is based on a unique representation of the FOPID controller, where fractional properties are modeled by a specific finite impulse response (FIR) filter. The balanced truncation model order reduction method is applied in the proposed approach to obtain an effective, low-order model of the FOPID controller. The time-invariant FOPID controller implementation is presented first, and then the methodology is extended to the controller with time-varying gains. A comparative analysis shows that the proposed methodology leads to the effective modeling of discrete-time FOPID controllers. In addition to simulation runs, the effectiveness of the introduced methodology is confirmed in a real-life experiment involving the control of the DC motor servo system. The paper concludes with the implementation tools developed in the Matlab/Simulink environment.