Miracle-dz/UniFAR-FTUs huggingface dataset:
IRNN-GDX: An improved random neural network using GDX for Intrusion Detection Systems

Anintrusion detection system is one of the methods that help when intrusion is detected to determine scheme safety. In this paper, NSL-KDD data set efficiency is assessed using enhanced RNN with momentum gradient descent and adaptive learning rate (GDX). Before destabilizing the core network, an intelligent IDS should be used to achieve this goal, and we propose a new RNN-GDX algorithm.Based on multiple performance measures, results are analyzed and better accuracy has been discovered. We achieved minimum (MSE) mean square error rate and maximum accuracy Using the RNN-GDX algorithm for NSL-KDD data set results showed that RNN-GDX learned better as well as overall efficiency isincreased to 93.3 percent & 97.68 percent respectively, with 29 input and 29 hidden layer neurons and 41 input & 41 hidden layer neurons changing to the lowest value of 0.01.
Theoretical Investigations on Forward-Wave and Backward-Wave Operation of a Frequency-Tunable Gyrotron

Frequency-tunable gyrotron is a promising terahertz (THz) radiation source for many modern applications. Actually, a resonator-based gyrotron oscillator can play dual functions: gyromonotron and reflected-type gyro-backward-wave oscillator (BWO) corresponding to the forward-wave and backward-wave operation. The performances of the gyromonotron and the reflected-type gyro-BWO for a 0.5-THz frequency-tunable gyrotron have been theoretically investigated in detail. The calculated results indicate that the frequency tunability, the field profile of the axial mode, and the beam–wave interaction efficiency of the gyromonotron and the reflected-type gyro-BWO are dramatically different from each other. The gyromonotron state can switch to the reflected-type gyro-BWO state by means of increasing the operating magnetic field. In addition, we have systematically investigated the characteristics of the 0.5-THz frequency-tunable gyrotron with different beam–wave interaction structures, especially for the reflected-type gyro-BWO. It is found that the tapered angle of the output section has a significant effect on the frequency tunability of the reflected-type gyro-BWO, and the multisection cavity can improve the performance of the reflected-type gyro-BWO. The results of this study may be beneficial for getting a deeper insight into the physics of the forward-wave and backward-wave operation of a frequency-tunable gyrotron.
Hybrid Deblur Net: Deep Non-Uniform Deblurring With Event Camera

Despite CNN-based deblur models have shown their superiority when solving motion blurs, restoring a photorealistic image from severe motion blur remains an ill-posed problem due to the loss of temporal information and textures. Event cameras such as Dynamic and Active-pixel Vision Sensor (DAVIS) can simultaneously produce gray-scale Active Pixel Sensor (APS) frames and events, which can capture fast motions as events of very high temporal resolution, <italic>i. e. </italic>, <inline-formula> <tex-math notation="LaTeX">$1~\mu s$ </tex-math></inline-formula>, can provide extra information for blurry APS frames. Due to the natural noise and sparsity of events, we employ a recurrent encoder-decoder architecture to generate dense recurrent event representations, which encode the overall historical information. We concatenate the original blurry image with the event representation as our hybrid input, from which the network learns to restore the sharp output. We conduct extensive experiments on GoPro dataset and a real event blurry dataset captured by DAVIS240C. Our experimental results on both synthetic and real images demonstrate state-of-the-art performance for <inline-formula> <tex-math notation="LaTeX">$1280\times 720 $ </tex-math></inline-formula> images at 30 fps.
Many-Tier Vertical GAAFET (V-FET) for Ultra-Miniaturized Standard Cell Designs Beyond 5 nm

The GAAFET (gate-all-around FET) is expected to replace FinFETs in future nodes due to its excellent channel controllability. It is also expected to be an impressive device due to its horizontal or vertical transistor structures. Vertical GAAFETs (V-FETs) are expected to be a promising device compared to horizontal GAAFETs (H-FETs) due to their structure, which allows area reduction and significant parasitic reduction. Besides, V-FETs are positioned on top of each other and thus allow more significant size reductions. Therefore, this paper studies the overall potential of many-tier V-FETs by investigating the essential design factors from the layout perspective. First, we study the factors that should be considered for designing many-tier V-FETs. Second, we propose an interconnect structure that maximizes the advantages of many-tier V-FETs. Third, we compare 2-tier V-FET standard cells to one-tier V-FET cells and visualize the advantages that many-tier V-FET cells provide. Our study shows that 2-tier V-FET standard cells provide a −35.6% area reduction with a cost of +16.5% wirelength and +13.2% parasitic capacitance increase compared to 1-tier V-FET cells. Compared to H-FETs and FinFETs, our cells show −50.1% area reductions with −0.3% wirelength reductions and −18.9% parasitic capacitance reductions. We emphasize that the design freedom to place transistors on top of each other and proper interconnect structures lead to ultra-scale miniaturized standard cell designs. We note that the increase in wirelength and capacitance is due to the vertical size increases and detours that must exist in the designs. Thus, careful circuit design is required to obtain the maximum advantages from V-FETs.
Magnetic Circuit Analysis and Fluid Flow Modeling of an MR Damper With Enhanced Magnetic Characteristics

A novel design of a magnetorheological (MR) damper is developed, fabricated, modeled, and tested. The design includes some features that enhance the magnetic characteristics of the damper. The iron–cobalt–vanadium “Vacoflux-50” alloy and the “AMT-Smartec+” MR fluid, whose magnetic characteristics have been predicted to enhance the performance of the damper, are used in the new design. Moreover, the location of the MR fluid region in the piston construction has been chosen so that the magnetic field maximizes. To evaluate the impact of the proposed design improvements, an approach to modeling the performance of a previously tested MR damper of a different design, different magnetic material, and different MR fluid has been developed. The approach combines a finite element analysis (FEA) of the magnetic circuit and a nonlinear analytical model of fluid flow. The results of the FE/analytical approach have been validated using the available published results of the same damper. Hence, the approach has been used to predict the performance of the same damper due to the use of the proposed design improvements. The FE/analytical approach accounts for the nonlinear characteristics caused by the magnetic saturation of materials and the effects of fluid compressibility and aeration in the damper. It has been found that the implementation of the proposed design features leads to a remarkable increase in the magnetic field and the fluid yield stress. Also, the inclusion of the nonlinear magnetic and fluid flow characteristics have been found to affect the magnetic field distribution and the fluid yield stress greatly.
A Deformable 3D-3D Registration Framework Using Discrete Periodic Spline Wavelet and Edge Position Difference

Neck pain is one of the most common symptoms of cervical spine disease, and segmenting neck muscles to create volumetric measurements may assist clinical diagnosis. While image registration is used to segment medical images, registration is highly challenging for neck muscles due to their tight proximity, shape and size variations among subjects, and similar appearance. These challenges cause conventional multi resolution-based registration methods to be trapped in local minima due to their low degree of freedom geometrical transforms. This article presents a novel object-constrained hierarchical registration framework for aligning inter-subject neck muscles. First, to handle large scale local minima, the proposed framework uses a coarse registration technique, which optimizes the new edge position difference (EPD) similarity measure, to align large mismatches. Also, a new transformation based on the discrete periodic spline wavelet (DPSW), affine and free-form-deformation (FFD) transformations are exploited. Second, to avoid monotonous nature of using transformations in multiple stages, a fine registration technique is designed for aligning small mismatches. This technique uses a double-pushing system by changing edges in the EPD and switching transformation resolutions. The EPD helps in both coarse and fine techniques to implement object-constrained registration via controlling edges, which is not possible when using traditional similarity measures. Experiments are performed on clinical 3D magnetic resonance imaging (MRI) scans of the neck, with the results showing that the EPD is more effective than the mutual information (MI) and sum of squared difference (SSD) measure in terms of volumetric dice similarity coefficient (DSC). Additionally, the proposed method is compared with the diffeomorphic Demons and SyN state-of-the-art approaches with ablation studies in inter-subject deformable registration. The proposed method achieves better accuracy, robustness and consistency than the reference methods, with an average volumetric DSC of 0.7029 compared to 0.6654 and 0.6606 for the Demons and SyN methods, respectively.
Personality2vec: Network Representation Learning for Personality

Online social networks have made tremendous progress in recent years. People generate a large amount of text information in which the linguistic and the semantic information could convey a great deal of information about the users’ personality. Studies on personality analysis based on text information from online social networks have gradually increased in recent years. However, the previous works do not leverage the text information comprehensively and thus limits the prediction accuracy. In this paper, we propose personality2vec, a novel model that is based on network representation learning and makes full use of the semantic, the personality, and the structural information in users’ texts. The model utilizes a new biased walk algorithm and an improved skip-gram algorithm to train on the network and eventually generates a personality vector for each user. We feed the personality vectors into regression algorithms to predict users’ Big Five personality scores. Experimental results show that personality2vec outperforms seven popular methods on three personality datasets.
Cyber Physical System Framework: An Apropos Study

The next generation of engineered systems is specified as the cyber-physical systems (CPS) with the incorporation of communiqué, computation, and controller to achieve the targets of constancy, routine, sturdiness, and competence in physical systems. Cyber-Physical systems are a feedback system that requires cybersecurity and improved design tools that enables design methodology. Cyber-Physical system becomes a significant track intended for the advance of the smart engineering business. The control trial of wide-ranging and organically disseminated cyber-physical systems is intended to be sturdy and consistent with very multifaceted disseminated in addition to the vital controller. The safety difficulties in addition protective measures the aforementioned aspects are progressively advancing the concentration of exploration in the arena. This paper presents a study of different application framework that is helpful to industry and academia in the direction of design a strong security framework to improve these systems.
Convolutional Neural Networks With Class-Driven Loss for Multiscale VHR Remote Sensing Image Classification

Because land covers always have different scales, multiscale methods are widely used in very-high-resolution (VHR) remote sensing image classification. Traditional multiscale methods usually capture multiscale information by using rectangular windows of different sizes. Each scale contains the same number of training samples and is independently trained. Hence, the training process is time-consuming. In this article, a novel convolutional neural network with a class-driven loss (CNNs-CDL) model is proposed for multiscale VHR remote sensing image classification. First, a multiscale sample construction method is proposed to select a training sample and capture the relationships among different scale samples. The lowest-scale samples are selected on the lowest-resolution image and are mapped to the higher-resolution image without additional label information. Then, a CNN with class-driven loss is trained with the lowest-scale training samples. Class-driven loss can effectively learn the spatial dependence between the nonadjacent samples to promote classification accuracy. Finally, the CNN model is fine-tuned with the higher-scale samples. Although the number of higher-scale training samples increases, the fine-tuning process requires only a small number of iterations to converge. Hence, the proposed model can effectively reduce the training time. The experimental results for three VHR remote sensing images show that the proposed method performs better than several recently proposed methods.
FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory

Cloud computing has long been the main backbone that Internet of Things (IoT) devices rely on to accommodate their storage and analytical needs. However, the fact that cloud systems are often located quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. Given that fog nodes possess low resource capabilities compared to the cloud, matching the IoT services to appropriate fog nodes while guaranteeing minimal delay for IoT services and efficient resource utilization on fog nodes becomes quite challenging. In this context, the main limitation of existing approaches is addressing the scheduling problem from one side perspective, i.e., either fog nodes or IoT devices. To address this problem, we propose in this paper a multi-criteria intelligent IoT-Fog scheduling approach using game theory. Our solution consists of designing (1) preference functions for the IoT and fog layers to enable them to rank each other based on several criteria latency and resource utilization and (2) centralized and distributed intelligent scheduling algorithms that capitalize on matching theory and consider the preferences of both parties. Simulation results reveal that our solution outperforms the two common Min-Min and Max-Min scheduling approaches in terms of IoT services execution makespan and fog nodes resource consolidation efficiency.
Toeplitz-Based Underwater Acoustic Channel Blind Deconvolution

In this paper, we propose a blind channel deconvolution method based on the Least Absolute Shrinkage and Selection Operator (LASSO). Using a Toeplitz structured formulation of the received signal, we form an iterative blind deconvolution scheme, alternatively estimating the underwater impulse response, and alternatively the transmitted waveform. The resulting optimizations are convex, and we formulate a computationally efficient solver using the Alternating Direction Method of Multipliers (ADMM). We illustrate the performance of the resulting estimator using both simulated and measured underwater signals.
Object Recognition of Three-dimensional SAR Based on PointNet

Object recognition of three-dimensional (3-D) SAR is one of the hot areas in the SAR application field. In order to improve the performance of 3-D SAR target recognition, this paper proposes a novel deep learning method for 3-D point-cloud recognition based on PointNet classification network. In the scheme, a point cloud extraction model is used to 3-D SAR image, then a PointNet network is trained with the extracted point cloud data. The effectiveness of the approach is verified by 3-D SAR simulation images with three different airplane targets. The results demonstrate that the method can obtain a high recognition accuracy in the case of a low 3-D SAR resolution.
Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

To survive in the competitive environment, most organizations have adopted component-based software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).
Design and Development of Rugged Wide Beam-width Miniaturized Bi-static Patch Antenna for X-Band FMCW System

In the wireless communication technologies, the space constrains, ease of manufacturing, material selection and mechanical stability influenced many researcher and antenna designers for compact antenna design. Antenna for specific application may trade-offs competing requirements. In this paper, the design and development of rugged wide beam-width miniaturized bi-static antenna for X-band FMCW (Frequency Modulated Continuous Wave) system is presented. Full wave simulation CST Microwave Studio 2015 software is used for design and ZVB14 model VNA (Vector Network Analyzer) has been used for antenna test measurement. For mechanical stability and to protect antenna from harsh environmental conditions, the antenna has been covered with PUF (Polyurethane Foam) injected Teflon radome. The measured and simulated results show good agreement with slight deviations.
Insect Ultra-Structures as Effective Physical-Based Bactericidal Surfaces

Bacteria colonization of substrates is a key issue threatening the health of humans worldwide. This review summarized recent bactericidal activity evidence of different structures developed by insects. We evaluated different mechanisms in bactericidal activity among several insect species, and we also provided a glimpse at attempts at fabricating bactericidal bioinspired structures. In addition, we investigated the link between species with mechano-bactericidal surfaces and their evolution and ecological dynamics. It emerged that mostly hemimetabolous insects evolved bactericidal nanostructures on their wings, probably due to development of their wings externally during postembryonal development. Whereas, holometabolous insects, which develop their wings internally during metamorphosis, have their wings exposed to the environment just in the adult instar, and thus subjected to bacteria for a shorter period compared to hemimetabolous insects. Bio-inspired high-aspect-ratio nanopillared surfaces with bactericidal properties might broaden the application area of bactericidal surfaces, to mechanically decrease the number of bacterial infections.
Coexistence Analysis of Multiple Asynchronous IEEE 802.15.4 TSCH-Based Networks

Low-power Wireless Sensor Networks (WSNs) play a key role in realization of the Internet-of-Things (IoT). Among others, Time Slotted Channel Hopping (TSCH) is a Medium Access Control (MAC) operational mode of the IEEE 802.15. 4 standard developed for communications in short range IoT networks. TSCH provides high level reliability and predictability by its channel hopping mechanism and time division channel access nature. In many applications, a number of TSCH networks may coexist in the same neighborhood. Several vehicles close to one another, each including a TSCH network for its in-vehicle communications, serve as an example. Since such networks are running independent of one another, they are not expected to be synchronized in time, and they are not scheduled to operate in exclusive frequency channels. This may lead to inter-TSCH interferences deteriorating the reliability of the networks, which is an important requirement for many IoT applications. This paper analyzes the impact of multiple asynchronous TSCH networks on one another. An analytical model is developed that estimates the chance of such interferences, and the expectation of the number of affected TSCH channels when a number of them are in the vicinity of one another. The developed model is verified using extensive simulations and real-world experiments. Also, a scalable and fast multi-TSCH coexistence simulator is developed that is used to get insight about coexistence behaviors of any number of TSCH networks with various configurations.
A Novel Average Torque Control of Switched Reluctance Motor Based on Flux–Current Locus Control

This article develops a novel average torque control (ATC) scheme for switched reluctance (SR) motor on the basis of a brand-new microstep flux–current locus controller. The proposed ATC is based on the calculation of the average torque from the converted mechanical energy that can be illustrated as an enclosed area in a flux–current plane. It has the superior advantage that it is able to control the average torque over any arbitrary small-angle intervals, whereas the conventional ATC can control only the average torque for a whole stroke. In order to realize the proposed ATC, a flux–current locus controller, which consists of a hybrid flux controller and current controller as well as involve the microstep process, is introduced in this article. The flux–current locus of the energy conversion loop is controlled for the first time in the literature. With the locus controller, it is possible to achieve a better energy conversion ratio and the ATC. In order to verify both the proposed locus control method and the ATC for SR motor, detailed simulation results and discussion are provided.
E-mode p-n Junction/AlGaN/GaN HEMTs with Enhanced Gate Reliability

in this work, we investigate the gate reliability of E-mode ${p-n}$ junction gate (PNJ) HEMT, which features an n GaN/p-GaN/AlGaN/GaN gate stack. Both electric-field and thermal-accelerated time-dependent gate breakdown (TDGB) tests were conducted on the PNJ-HEMTs with constant voltage stress (CVS). Compared with the Schottky-junction p-GaN gate HEMT (SJ-HEMT), the PNJ-HEMT delivers lower gate leakage, larger gate swing and longer TDDB lifetime. The estimated maximum applicable $V _{\mathbf{GS}}$ of PNJ-HEMT is 10 V for 10 years’ lifetime at 63% failure level.