Showing papers in "Electronics in 2023"
DOI•
TL;DR: In this article , a scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning.
Abstract: The unregulated proliferation of counterfeit news creation and dissemination that has been seen in recent years poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. This scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in publications since 2016, and this dissemination of fake news is still an issue from a global perspective. Thematic analysis of papers reveals that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped, while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute niche areas. Furthermore, the results show that China and the USA have the strongest international collaboration, despite India writing more articles. This paper also examines the current state of the art in deep learning techniques for fake news detection, with the goal of providing a potential roadmap for researchers interested in undertaking research in this field.
4 citations
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TL;DR: Based on the analysis of existing image encryption algorithms, a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system was proposed in this article , which has high security and good encryption effect.
Abstract: Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect.
3 citations
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TL;DR: In this article , the authors proposed a fuzzy-SFOSMC (smoothing fractional order sliding mode controller) to improve the robustness of a 3DOF surgical robot manipulator.
Abstract: In the era of digital OTs (operating theatres), the developments in robot-assisted surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses robotic articulations to assist in complicated surgeries. Its implementation improves the ability of the specialized doctor to perform surgery to a great extent. The paper addresses the dynamics and control of the highly non-linear 3DOF surgical robot manipulator in the event of external disturbances and uncertainties. The integration of non-linear robust SMC (sliding mode control) with a smoothing mechanism, a FOPID (fractional-order proportional integral derivative) controller, and a fuzzy controller provides a high degree of robustness and minimal chatter. The addition of fuzzy logic to the controller, named intelligent fuzzy-SFOSMC (smoothing fractional order sliding mode controller) improves the system’s performance by ruling out the disturbances and uncertainties. The prototype model is developed in a laboratory and its outcomes are validated on OP5600, a real-time digital simulator. Simulation and experimental results of the proposed fuzzy-SFOSMC are compared with conventional controllers, which illustrates the efficacy and superiority of the proposed controller’s performance during the typical surgical situations. The proposed fuzzy-SFOSMC outperforms conventional controllers by providing greater precision and robustness to time-varying nonlinear multi-incision trajectories.
2 citations
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TL;DR: In this paper , the authors presented the D.O.T. PAQUITOP project, which aims at developing a mobile robotic assistant for the hospital environment, which is composed of a custom omnidirectional platform, named PACHOP, a commercial 6DoF robotic arm, sensors for monitoring vital signs in patients, and a tablet to interact with the patient.
Abstract: The use of robotic technologies for caregiving and assistance has become a very interesting research topic in the field of robotics. Towards this goal, the researchers at Politecnico di Torino are developing robotic solutions for indoor assistance. This paper presents the D.O.T. PAQUITOP project, which aims at developing a mobile robotic assistant for the hospital environment. The mobile robot is composed of a custom omnidirectional platform, named PAQUITOP, a commercial 6 dof robotic arm, sensors for monitoring vital signs in patients, and a tablet to interact with the patient. To prove the effectiveness of this solution, preliminary tests were conducted with success in the laboratories of Politecnico di Torino and, thanks to the collaboration with the Onlus Fondazione D.O.T. and the medical staff of Molinette Hospital in Turin (Italy), at the hematology ward of Molinette Hospital.
1 citations
DOI•
TL;DR: In this paper , the authors proposed a distributed advanced ring routing strategy for the mobile wireless sensor network, in which the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, reduces the control overhead while preserving the benefits of mobile sink, thereby optimizing the energy and improving the network life span.
Abstract: The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand.
1 citations
TL;DR: In this article , an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods was developed, which is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function.
Abstract: The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.
1 citations
DOI•
TL;DR: In this paper , the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management are explored in terrestrial, maritime, and aerial logistics networks.
Abstract: Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.
1 citations
TL;DR: In this article , the authors proposed a federated learning approach for predicting smart home consumption, which takes into consideration the age of the time series datasets of each client and aggregates local models trained on each smart home device to produce a global prediction model via a novel weighting scheme.
Abstract: Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client. The proposed method is based on federated averaging but aggregates local models trained on each smart home device to produce a global prediction model via a novel weighting scheme. Each local model contributes more to the global model when the local data are more recent, or penalized when the data are older upon testing for a specific residence (client). The approach was evaluated on a real-world dataset of smart home energy consumption and compared with other machine learning models. The results demonstrate that the proposed method performs similarly or better than other models in terms of prediction error; FedTime achieved a lower mean absolute error of 0.25 compared to FedAvg. The contributions of this work present a novel federated learning approach that takes into consideration the age of the datasets that belong to the clients, experimenting with a publicly available dataset on grid import consumption prediction, while comparing with centralized and decentralized baselines, without the need for data centralization, which is a privacy concern for many households.
TL;DR: In this article , the authors proposed a novel metamaterial (MTM) slow wave structure (SWS) for a miniaturized traveling wave tube (TWT) with four blend edges to weaken the corresponding longitudinal electric field.
Abstract: A miniaturized traveling wave tube (TWT) was studied by proposing a novel metamaterial (MTM) slow wave structure (SWS). The dispersion results show that n = −1 space harmonic of the fundamental mode exhibits the “forward” wave properties, which is the foundation of the MTM-inspired TWT. Meanwhile, the interaction impedance for mode 2 of the novel MTM SWS can be sharply decreased by introducing four blend edges to weaken the corresponding longitudinal electric field. Also, two coaxial couplers are presented to input/output the signals. The transmission results show that the reflection is as low as −15 dB from 2.90 GHz to 3 GHz, which ensures the amplified signal can be effectively outputted. The MTM-inspired TWT exhibits miniaturized superiority for its compact high frequency structure including the MTM SWS and the coaxial couplers. Especially, for the high-frequency structure, the transverse and longitudinal sizes are ~λ/5 and ~3λ, respectively (λ is the free-space wavelength at the operating frequencies). The simulation of the beam wave interaction shows that the proposed MTM-inspired TWT yields output powers of kW levels from 2.90 GHz to 3 GHz, with a gain of 23.5–25.8 dB and electronic efficiency of 14–22% when the beam current is 0.5 A and the beam voltage is 13 kV. The results indicate that the gain per wavelength is as high as 8.5 dB in the operating bands. The simulation results confirm that it is possible to weaken the backward wave oscillation from the higher mode in the miniaturized MTM-inspired TWT.
TL;DR: In this paper , the root causes of bearing failure in IGBT inverter-fed locomotives and EMUs are investigated, and theoretical analysis and laboratory tests are carried out.
Abstract: Three current paths are proposed, and theoretical analysis and laboratory tests are carried out to investigate the root causes of bearing failure in IGBT inverter-fed locomotives and EMUs. The three types of current paths that run through the drive unit bearings and axle box bearings used on EMUs and electric locomotives are classified as the primary side current path, the main traction system current path, and the current path between the vehicles of the EMU or electric locomotive and the vehicles it hauls. The research found that the EDM current path in the main traction system caused by common mode voltage is distinguished as the main cause resulting in the failure of the bogie motor bearings or the bearings of the load connected to the motor shaft. The cause of common mode voltage is analyzed, and the thresholds of current density and voltage without causing bearing damage are analyzed and presented. The lab tests carried out on the bearings on the main traction system’s current path verified that the current path does exist. The proof to identify electric erosion, such as craters and washboards, and corresponding measures to prevent the failure of bogie bearings are proposed. Further research about the other two current paths is urgent and necessary.
TL;DR: In this paper, an overview of the possibilities of sourcing battery field data from Android devices is presented, and an Android device study featuring multiple devices is conducted, evaluating signal quality and differences.
Abstract: Operating Lithium-ion batteries requires a deep understanding of their performance. Laboratory experiments are conducted mostly under controlled ambient conditions and repetitive loads, whereas real-world operations feature a large spectrum of operating conditions. This leads to a large gap, which in-operation battery field data aims to close. Literature states the necessity for large datasets for field data studies; however, these are not available today. In this article, an overview of the possibilities of sourcing battery field data from Android devices is presented. Today, there are two billion Android devices in active use, and most of them are equipped with a lithium-ion battery and are also connected to the internet. An Android device study featuring multiple devices is conducted, evaluating signal quality and differences. Exemplary data analysis with a focus on state estimation algorithms is then tested on the sourced data.
TL;DR: In this article , the authors propose a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing evaluation and recommendation frameworks, which is a set of techniques that allow organizations to have an X-ray view of their processes.
Abstract: In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.
TL;DR: In this paper , an optimized Support Vector Regression model, processed using the Swarm algorithm, is applied for data prediction and process optimization of HfAlO-based charge trapping memory devices.
Abstract: The production and optimization of HfAlO-based charge trapping memory devices is central to our research. Current optimization methods, based largely on experimental experience, are tedious and time-consuming. We examine various fabrication parameters and use the resulting memory window data to train machine learning algorithms. An optimized Support Vector Regression model, processed using the Swarm algorithm, is applied for data prediction and process optimization. Our model achieves a MSE of 0.47, an R2 of 0.98856, and a recognition accuracy of 90.3% under cross-validation. The findings underscore the effectiveness of machine learning algorithms in non-volatile memory fabrication process optimization, enabling efficient parameter selection or outcome prediction.
TL;DR: YOLOv7-UAV as discussed by the authors proposes a spatial pyramid network that utilizes concatenated small-sized max-pooling layers and depth-wise separable convolutions to extract feature information across different scales more effectively.
Abstract: Detecting small objects in aerial images captured by unmanned aerial vehicles (UAVs) is challenging due to their complex backgrounds and the presence of densely arranged yet sparsely distributed small targets. In this paper, we propose a real-time small object detection algorithm called YOLOv7-UAV, which is specifically designed for UAV-captured aerial images. Our approach builds upon the YOLOv7 algorithm and introduces several improvements: (i) removal of the second downsampling layer and the deepest detection head to reduce the model’s receptive field and preserve fine-grained feature information; (ii) introduction of the DpSPPF module, a spatial pyramid network that utilizes concatenated small-sized max-pooling layers and depth-wise separable convolutions to extract feature information across different scales more effectively; (iii) optimization of the K-means algorithm, leading to the development of the binary K-means anchor generation algorithm for anchor allocation; and (iv) utilization of the weighted normalized Gaussian Wasserstein distance (nwd) and intersection over union (IoU) as indicators for positive and negative sample assignments. The experimental results demonstrate that YOLOv7-UAV achieves a real-time detection speed that surpasses YOLOv7 by at least 27% while significantly reducing the number of parameters and GFLOPs to 8.3% and 73.3% of YOLOv7, respectively. Additionally, YOLOv7-UAV outperforms YOLOv7 with improvements in the mean average precision (map (0.5:0.95)) of 2.89% and 4.30% on the VisDrone2019 and TinyPerson datasets, respectively.
TL;DR: In this article , a dual reversible data hiding method was proposed to enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing block size.
Abstract: Data hiding and reversible data hiding research has primarily focused on grayscale and color images, because binary and halftone images are prone to visual distortion caused by a small number of errors in pixel representation. As a result, reversible data hiding is more useful than halftone-based data hiding. This study proposes an investigation of encrypted halftone images based on dual reversible data hiding, which improves the reversibility and security of the image by utilizing a dual cover image. Since halftone images are adequately compressed, they are beneficial in low-channel-bandwidth environments. Hamming code (HC) (7,4) is applied to each block of the halftone image to hide the secret data, and two halftone images are recorded and sent to different receivers at the end of the embedding process. Recipients can use the proposed method and the two marked images to extract the message and recover the cover halftone image. The proposed data hiding method can enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing the block size. The experimental results provide evidence of the effectiveness of the proposed method in terms of both image quality and the embedding rate.
TL;DR: In this article , a data mining approach is proposed to detect learning topics requiring attention in the improvement process of teaching materials by analyzing the discrepancy between formative and summative assessments, which can assist in identifying underperforming and overperforming students.
Abstract: It is crucial to review and update course materials regularly in higher education. However, in the course evaluation process, it is debatable what a difficult learning topic is. This paper proposes a data mining approach to detect learning topics requiring attention in the improvement process of teaching materials by analyzing the discrepancy between formative and summative assessments. In addition, we propose specific methods involving clustering and noise reduction using the OPTICS algorithm and discrepancy calculation steps. Intensive experiments have been conducted on a dataset collected from accurate assessment results of the data structures and algorithms (DSA) course for IT major students at our university. The experimental results have shown that noise reduction can assist in identifying underperforming and overperforming students. In addition, our proposed method can detect learning topics with a high discrepancy for continuously improving teaching materials, which is essential for question recommendation in adaptive learning systems.
TL;DR: In this paper , the achievable capacity of orthogonal and non-orthogonal multiple access (OMA and NOMA) schemes in supporting downlink satellite communication networks is theoretically studied.
Abstract: In this paper, we theoretically study the achievable capacity of orthogonal and non-orthogonal multiple access (OMA and NOMA) schemes in supporting downlink satellite communication networks. Considering that various satellite applications have different delay quality-of-service (QoS) requirements, the concept of effective capacity is introduced as a delay-guaranteed capacity metric to represent users’ various delay requirements. Specifically, the analytical expressions of effective capacities for each user achieved with the NOMA and OMA schemes are first studied. Then, approximated effective capacities achieved in some special cases, exact closed-form expressions of users’ achievable effective capacity, and the capacity difference between NOMA and OMA schemes are derived. Simulation results are finally provided to validate the theoretical analysis and show the suitable limitations of the NOMA and OMA schemes, such as the NOMA scheme is more suitable for users with better channel quality when transmit signal-to-noise (SNR) is relatively large, while it is suitable for users with worse link gain when transmit SNR is relatively small. Moreover, the influences of delay requirements and key parameters on user selection strategy and system performance are also shown in the simulations.
TL;DR: In this article , a suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain, and the practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program.
Abstract: Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.
TL;DR: In this paper , the authors proposed a YOLOv7 detection algorithm combining the image enhancement and convolutional block attention module to improve the feature extraction performance of the network and improve the detection accuracy.
Abstract: Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.
TL;DR: In this article , a method for minimizing the inductor current ripple of a DC-DC converter in a two-stage power conversion system consisting of a grid-connected PWM converter and an interleaved multiphase three-level DCDC converter is proposed.
Abstract: This paper proposes a method for minimizing the inductor current ripple of a DC–DC converter in a two-stage power conversion system consisting of a grid-connected PWM converter and an interleaved multiphase three-level DC–DC converter. To reduce the output voltage ripple, the three-level DC–DC converter is configured in parallel and operated interleaved. However, a circulating current generated by the interleaved operation increases the inductor current ripple of each DC–DC converter and causes system loss and inductor saturation. In this paper, the inductor and output current ripple of the interleaved three-phase three-level DC–DC converter is mathematically analyzed and the effect of the DC–DC converter’s duty ratio and output voltage on each current ripple is described. Based on this analysis, a method is proposed for controlling the optimal DC link voltage through the PWM converter, so that the DC–DC converter is controlled with the duty ratio that minimizes the inductor current ripple. The simulation and experimental results under various operating conditions are presented to verify the feasibility of the proposed control method.
TL;DR: In this article , a discrete-time incremental backstepping (DTIBS) controller with an extended Kalman filter (EKF) is proposed for unmanned aerial vehicles (UAVs) with unknown actuator dynamics.
Abstract: In this study, a discrete-time incremental backstepping (DTIBS) controller with an extended Kalman filter (EKF) is proposed for unmanned aerial vehicles (UAVs) with unknown actuator dynamics. The Taylor series and an approximate discrete method are employed, transforming the second-order continuous-time nonlinear system into a discrete-time nonlinear plant with an incremental input form. The incremental control laws are designed using the incremental nonlinear dynamic inversion (INDI) method and the time-delay control (TDC) method. The TDC is introduced to design the control law, eliminating the need for prior knowledge of the control effectiveness matrix involving some unknown aerodynamic coefficients. In addition, the airflow angle and body rotation rate are selected as key system states, and the EKF is used to design a state estimator to estimate the local state of the small unmanned aerial vehicle closed-loop flight control system under strong noise conditions. The effectiveness of the DTIBS control method with EKF is verified through numerical simulation. The results show that the proposed method can effectively estimate the state under the typical noise characteristics of low-cost sensors, and the closed-loop control systems has good tracking performance and can quickly and effectively track sudden commands.
TL;DR: In this article , a range of false target track deception method based on a phase-switched screen (PSS) is proposed, and the relationship between the matched filtering output, radar detection, and track processing is derived.
Abstract: Track processing is the foundation of radar multi-target tracking, and the processing performance for jamming has particular research significance when it comes to protecting high-value targets. At present, passive jamming using a modulated metasurface exhibits a fast response and a flexible operation mode. However, most research in this area has been carried out at the radar signal processing level and less at the data processing level. In this paper, a range of false target track deception method based on a phase-switched screen (PSS) is proposed, and the relationship between the matched filtering output, radar detection, and track processing is derived. This method uses PSS to generate multiple false targets with controlled spatial distribution and magnitude, which can form high-fidelity false tracking tracks. The number of false tracking tracks can be flexibly altered by controlling the modulation parameters. The simulation results validate the effectiveness of the proposed method.
TL;DR: Wang et al. as discussed by the authors proposed a cross-programming-language vulnerability detection method based on intermediate representation and combined features, which first converted programs in different programming languages into a unified LLVM intermediate representation (LLVM-IR), and then extracted the code sequences and control flow graphs of the samples, extracted the program semantic information and graph structure information, and concatenated them into semantic vectors.
Abstract: The most severe problem in cross-programming languages is feature extraction due to different tokens in different programming languages. To solve this problem, we propose a cross-programming-language vulnerability detection method in this paper, IRC-CLVul, based on intermediate representation and combined features. Specifically, we first converted programs in different programming languages into a unified LLVM intermediate representation (LLVM-IR) to provide a classification basis for different programming languages. Afterwards, we extracted the code sequences and control flow graphs of the samples, used the semantic model to extract the program semantic information and graph structure information, and concatenated them into semantic vectors. Finally, we used Random Forest to learn the concatenated semantic vectors and obtained the classification results. We conducted experiments on 85,811 samples from the Juliet test suite in C, C++, and Java. The results show that our method improved the accuracy by 7% compared with the two baseline algorithms, and the F1 score showed a 12% increase.
TL;DR: In this paper , a novel non-stationary clutter suppression method using an elevation oblique subspace projection method was proposed to solve the problem of clutter becoming nonstationary for a space-based surveillance radar.
Abstract: The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.
TL;DR: In this article , the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter (PECIMINV) is presented. But the authors focus on the safety of the inverter and propose a Switching Function that accepts easy-to-understand functional states as input, simplifying research on higher-level control algorithms and advanced single-cell battery management capabilities.
Abstract: Due to their high efficiency and advanced battery management capability, cascaded multilevel inverters are an exciting option for battery electric powertrains. A promising, new and highly efficient cascaded multilevel inverter is the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter. The inverter, with four semiconductor switches per submodule, can reconfigure individual battery cells in series and parallel and generate positive and negative phase voltages in regular four-quadrant operation. Therefore, emerging degrees of freedom in battery management and inverter operation must be managed and mapped into a specific Switching State for every switch. As controlling the high number of switches is safety-relevant, this publication profoundly explains the inverter’s functionality. We introduce a Switching Function that accepts easy-to-understand functional states as input, simplifying research on higher-level control algorithms and advanced single-cell battery-management capabilities. As the Switching Function guarantees safe operation and the correct contribution of every cell to the overall functionality of the inverter, it enables researchers to confidently use and thereby accelerate research on the promising new topology. The method we describe is fast, simple, deterministic and designed to convert setpoint specifications into an executable Switching Pattern. We prove that our Switching Function is operable on an FPGA with a twenty-kilohertz setpoint update operating a 17-level inverter.
TL;DR: Wang et al. as discussed by the authors proposed a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data.
Abstract: This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm.
TL;DR: In this article , an enhanced deep residual shrinkage network (EDRSN)-based voiceprint recognition by combining the traditional voice-print recognition algorithms with deep learning (DL) in the context of the noisy electric industry environment was proposed.
Abstract: Voiceprint recognition can extract voice features and identity the speaker through the voice information, which has great application prospects in personnel identity verification and voice dispatching in the electric industry. The traditional voiceprint recognition algorithms work well in a quiet environment. However, noise interference inevitably exists in the electric industry, degrading the accuracy of traditional voiceprint recognition algorithms. In this paper, we propose an enhanced deep residual shrinkage network (EDRSN)-based voiceprint recognition by combining the traditional voiceprint recognition algorithms with deep learning (DL) in the context of the noisy electric industry environment, where a dual-path convolution recurrent network (DPCRN) is employed to reduce the noise, and its structure is also improved based on the deep residual shrinkage network (DRSN). Moreover, we further use a convolutional block attention mechanism (CBAM) module and a hybrid dilated convolution (HDC) in the proposed EDRSN. Simulation results show that the proposed network can enhance the speaker’s vocal features and further distinguish and eliminate the noise features, thus reducing the noise influence and achieving better recognition performance in a noisy electric environment.
TL;DR: In this paper , the authors proposed a method that incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge, and employed multi-scale dilated convolution kernels to capture rich explicit interaction features across different scales of space.
Abstract: In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. The proposed method incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge. Furthermore, it utilizes a multi-layer convolutional neural network to fuse features at different levels, effectively capturing more abundant semantic information. Additionally, the method employs multi-scale dilated convolution kernels to capture rich explicit interaction features across different scales of space. In this study, the effectiveness of the proposed model was validated on the link prediction task. Experimental results demonstrated that, compared to the ConvE model, the proposed model achieved a significant improvement of 14.8% in terms of mean reciprocal rank (MRR) on public datasets. Additionally, the suggested model outperformed the ConvR model on the hydraulic dataset, leading to a 10.1% increase in MRR. The results indicate that the proposed approach exhibits good applicability and performance in the task of hydraulic knowledge graph complementation. This suggests that the method has the potential to offer significant assistance for knowledge discovery and application research in the field of hydraulics.
TL;DR: In this paper , the authors provide a comprehensive and novel review of the state-of-the-art (SOTA) in dropout regularization, from standard random dropout to AutoDrop dropout, and discuss their performance and experimental capabilities.
Abstract: Dropout is one of the most popular regularization methods in the scholarly domain for preventing a neural network model from overfitting in the training phase. Developing an effective dropout regularization technique that complies with the model architecture is crucial in deep learning-related tasks because various neural network architectures have been proposed, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and they have exhibited reasonable performance in their specialized areas. In this paper, we provide a comprehensive and novel review of the state-of-the-art (SOTA) in dropout regularization. We explain various dropout methods, from standard random dropout to AutoDrop dropout (from the original to the advanced), and also discuss their performance and experimental capabilities. This paper provides a summary of the latest research on various dropout regularization techniques for achieving improved performance through “Internal Structure Changes”, “Data Augmentation”, and “Input Information”. We can see that proper regularization with respect to structural constraints of network architecture is a critical factor to facilitate overfitting avoidance. We discuss the strengths and limitations of the methods presented in this work, which can serve as valuable references for future research and the development of new approaches. We also pay attention to the scholarly domain in the discussion in order to meet the overwhelming increase of scientific research outcomes by providing an analysis of several important academic scholarly issues of neural networks.
TL;DR: In this paper , a distributed cooperative automatic modulation classification (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks, where feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal.
Abstract: Automatic modulation classification (AMC) is an important component in non-cooperative wireless communication networks to identify the modulation schemes of the received signals. In this paper, considering the multipath effect in practical propagation environments, a distributed cooperative AMC (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks. Specifically, feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal. Then, a classifier based on the K-nearest neighbor (KNN) method is designed to obtain the local decision for modulation classification at each distributed node. Meanwhile, the reliability of the local decision is estimated by applying two loss functions to assess the quality of the local decision. Finally, the unified classification result is obtained to fuse the local decisions according to their reliabilities by applying a designed decision fusion algorithm based on the distributed weighted average alternating direction method of multipliers (DWA-ADMM). The simulation results demonstrate that the proposed Co-AMC network achieves superior classification accuracy compared to existing AMC methods across a range of modulation schemes and SNRs. More importantly, the proposed Co-AMC exhibits great flexibility and practicability since it is adaptive to wireless networks with various scales and topologies.