Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Phase Five-Level Flying-Capacitor Rectifier Using Three Switches

In this paper, a unidirectional single-phase five-level PWM rectifier based on flying-capacitor topology operating as PFC is proposed. The main feature is the fact that the proposed converter has three active switches, therefore offering a cost reduction compared to traditional five-level topologies. The multilevel voltage operation reduces the weight and bulk of the magnetic devices. Moreover, 82% of the semiconductors are subject to one fourth to DC output voltage which allows the use of low voltage semiconductor devices. An extensive mathematical analysis, control-oriented modeling in addition to experimental results are presented in order to demonstrate the feasibility of the structure.
Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Road extraction from high-resolution remote sensing images is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem. This article provides a comprehensive review of these existing approaches. We classified the methods into heuristic and data-driven. The heuristic methods are the mainstream in the early years, and the data-driven methods based on deep learning have been quickly developed recently. With regard to the heuristic methods, the road feature model is first introduced, then, the classic extraction methods are reviewed in two subcategories: semiautomatic and automatic. The principles, inspirations, advantages, and disadvantages of these methods are described. In terms of the data-driven methods, the road extraction methods based on deep neural network, particularly those based on patched convolutional neural network, fully convolutional network, and generative adversarial network are reviewed. We perform subjective comparisons between the methods inner each type. Furthermore, the quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data-driven methods. Finally, the conclusion and prospects are summarized.
Structure Aware Generative Adversarial Networks for Hyperspectral Image Classification

Generative adversarial networks (GANs) have shown striking performances in computer vision applications to augment virtual training samples (VTS). However, the VTS generating by GANs in the context of hyperspectral image classification suffer from structural inconsistency due to the insufficient number of training samples in order to learn high-order features from the discriminator. This work addresses the scarcity of training samples by designing a GAN, in which the performance of discriminator is improved to produce more structurally coherent VTS. In the proposed method, by splitting the discriminator into two parts, GAN undertakes two tasks: the main task is to learn to distinguish between real and fake samples, and the auxiliary task is to learn to distinguish structurally corrupted and real samples. With this setup, GAN will produce real-like VTS with a higher variation than conventional GAN. Furthermore, in order to reduce the computational cost, subspace-based dimension reduction was performed to obtain the dominant features around the training samples to generate meaningful patterns from the original ones to be used in the learning phase. Based on the experimental results on real, and well-known hyperspectral benchmark images, the proposed method improves the performance compared with GANs-related, and conventional data augmentation strategies.1
Compensation of Motion-Induced Phase Errors and Enhancement of Doppler Unambiguity in TDM–MIMO Systems by Model-Based Estimation

Utilization of multiple input multiple output (MIMO) systems in radar and channel sounding has gained increased attention in recent years. Quite often, time-division multiplexing (TDM) is employed to realize orthogonal waveforms at the transmitter. Apart from its advantages, TDM has two severe drawbacks. First, motion-induced phase variations become indistinguishable from phase migration due to the signal's arrival direction. This is termed angle-Doppler coupling, which causes ambiguities in angle, and Doppler estimation. Second, the unambiguously resolvable Doppler, i.e., the Doppler bandwidth, is reduced. In this letter, a model-based estimation approach will be proposed, which compensates for angle-Doppler coupling, and restores the Doppler bandwidth. A data model for the MIMO observations is derived, which is exploited by a maximum likelihood estimator to infer angle, delay, and Doppler from the observations. The performance of the proposed approach will be testified by simulations.
Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction With Low-Dose Scans

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with low-dose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object. In a TTV model, the sparsity structures of the contrast-medium concentration (CMC) across PCT frames are assumed to be isotropic with identical, and independent distribution. However, this assumption is inconsistent with practical PCT tasks wherein the sparsity has evident variations, and correlations. Such modeling deviation hampers the performance of TTV-based PCT reconstructions. To address this issue, we developed a novel contrast-medium anisotropy-aware tensor total variation (CMAA-TTV) model to describe the intrinsic anisotropy sparsity of the CMC in PCT imaging tasks. Instead of directly on the difference matrices, the CMAA-TTV model characterizes sparsity on a low-rank subspace of the difference matrices which are calculated from the input data adaptively, thus naturally encoding the intrinsic variant, and correlated anisotropy sparsity structures of the CMC. We further proposed a robust, and efficient PCT reconstruction algorithm to improve low-dose PCT reconstruction performance using the CMAA-TTV model. Experimental studies using a digital brain perfusion phantom, patient data with low-dose simulation, and clinical patient data were performed to validate the effectiveness of the presented algorithm. The results demonstrate that the CMAA-TTV algorithm can achieve noticeable improvements over state-of-the-art methods in low-dose PCT reconstruction tasks.
BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Semantic segmentation is a fundamental task in remote sensing image understanding. Recently, Deep Convolutional Neural Networks (DCNNs) have considerably improved the performance of the semantic segmentation of natural scenes. However, it is still challenging for very high-resolution remote sensing images. Due to the large and complex scenes as well as the influence of illumination and imaging angle, it is particularly difficult for the existing methods to accurately obtain the category of pixels at object boundaries—the so-called boundary blur. We propose a framework called Boundary-Aware Semi-Supervised Semantic Segmentation Network (BAS4Net), which obtains more accurate segmentation results without additional annotation workload, especially at the object boundaries. The Channel-weighted Multi-scale Feature (CMF) module balances semantic and spatial information, and the Boundary Attention Module (BAM) weights the features with rich semantic boundary information to alleviate the boundary blur. Additionally, to decrease the amount of difficult and tedious manual labeling of remote sensing images, a discriminator network infers pseudolabels from unlabeled images to assist semisupervised learning, and further improves the performance of the segmentation network. To validate the effectiveness of the proposed framework, extensive experiments have been performed on both the ISPRS Vaihingen dataset, and the novel remote sensing dataset AIR-SEG with more categories, and complex boundaries. The results demonstrate a significant improvement of accuracy especially on boundaries and for small objects.
An Adaptive Power Allocation and Coding Scheme for Improving Achievable Rate of the Gaussian Interference Channel

The best coding scheme for Gaussian interference channel (GIC) is still an open problem so far, and has attracted much attention in the field of wireless communication due to it plays an important role in combating co-channel interference. In this paper, an adaptive power allocation and coding scheme based on time sharing (TS) strategy is proposed for the twouser GIC to coordinate the interference and achieve a higher sum-rate. In the proposed scheme, the codewords are divided into some segments, and the power of each segment is jointly optimized for the two users to maximize the sum-rate. To solve the power allocation problem, a heuristic search algorithm based on the optimal path planning algorithm is proposed. Simulation results show that the proposed scheme can achieve a higher sum-rate compared with the conventional coding schemes.
An $H_{i}/H_{\infty }$ Optimization Approach to Event-Triggered Fault Detection for Linear Discrete Time Systems

In this article, a new event-triggered <inline-formula><tex-math notation="LaTeX">$H_{i}/H_{\infty }$</tex-math></inline-formula> optimization fault detection (FD) scheme is proposed for linear discrete time systems. A linear discrete time system with varying sampling periods is first presented to describe the dynamics of an event-triggered system. Based on this, an observer-based fault detection filter (FDF) is constructed as an event-triggered residual generator so that the generated residual signal is completely decoupled from the event-triggered transmission error. The design of event-triggered FDF is formulated as a multiobjective optimization problem denoted by <inline-formula><tex-math notation="LaTeX">$H_{i}/H_{\infty }$</tex-math></inline-formula> and an optimal solution can be obtained by recursive calculation of Riccati equations. In addition, an event-triggered residual evaluation strategy is also presented. Comparing with the existing event-triggered FD schemes, the major contribution of this article is to decouple the residual signals from the event-triggered transmission errors completely and, consequently, the FD performance can be improved significantly. Moreover, an optimal tradeoff between the robustness of residual to unknown input and the sensitivity of residual to fault is achieved in the sense of <inline-formula><tex-math notation="LaTeX">$H_{i}/H_{\infty }$</tex-math></inline-formula> optimization, while the design of FDF and event generator can be carried out independently. To demonstrate the effectiveness of the proposed method, a vehicle lateral dynamic system is used as a simulation example.
A Blockchain-Assisted Verifiable Outsourced Attribute-Based Signcryption Scheme for EHRs Sharing in the Cloud

The sharing of electronic health records (EHRs) has shown great advantages in the accurate treatment of patients and the development of medical institutions. However, it is easy to cause some security problems in the process of medical data sharing. Generally, after a patient’s EHRs are generated by different medical institutions, they are outsourced to the cloud server (CS) by the authorized medical institutions for storage, which causes the patient to lose control of EHRs. Moreover, malicious medical institutions and semi-trusted cloud servers may collude to tamper with EHRs to seek benefits, which threatens the integrity of EHRs. Therefore, we propose a blockchain-assisted verifiable outsourced attribute-based signcryption scheme (BVOABSC) which realizes the secure sharing of EHRs in a multi-authority cloud storge environment. Firstly, we use the attribute-based signcryption algorithm to realize the confidentiality and unforgeability of the EHRs and protect the privacy of the signer. Secondly, it greatly reduces the computational burden of users by using verifiable outsourcing computation mechanism. Most of the designcryption calculation is performed by the cloud server, and the correctness of the generated partial designcryption ciphertext is verified by users. Furthermore, we use blockchain technology to protect outsourced EHRs from tampering by illegal users. Specifically, each operation on outsourced EHRs is stored as a transaction on the public blockchain, which ensures that EHRs cannot be modified. At the same time, the auditor can verify the integrity of the outsourced EHRs by checking the corresponding transactions. In addition, the smart contract created by the patient can solve the problems in cloud storage, such as tampering EHRs and returning incorrect results. Finally, security analysis and performance evaluation show that the proposed BVOABSC scheme satisfies stronger security and higher efficiency than similar schemes.
Hf₀.₅Zr₀.₅O₂ Ferroelectric Embedded Dual-Gate MoS₂ Field Effect Transistors for Memory Merged Logic Applications

In this letter, a combination of multi-gate field effect transistor with ferroelectric is proposed for a new concept of memory merged logic device. For the first time, dual-gate MoS2 field effect transistor (FET) with a Hf0.5Zr0. 5O2 (HZO) back gate insulator is fabricated. Because of the manipulation of charge density in the channel by both electric field from the top/back gates and the polarization field form the HZO, the ferroelectric embedded dual-gate (FEDG) MoS2 FET can work as a normal n-type top-gate MOSFET or a ferroelectric memory with 1 V memory window and retention time up to 103 s, separately or simultaneously. In memory merged logic operation, the output current of the devices depends on the top-gate voltage, back-gate voltage and the polarization of the HZO, which greatly extend the possible function that one transistor can implement. The FEDG FET has the benefits of diminishing the power dissipation of inter connection between logic and memory arrays in the integrated circuit, and reducing the number of transistors in circuits comparing to standard MOSFET configurations, which shows a great potential in the ultra-low power consumption in memory computing (IMC) and neuromorphic computing (NC) applications.
Research and Implement of Just-In-Time Dual Buffer-Queues in LoRaWAN Gateways

Firstly, giving an overview of LoRa wireless communication technology and LoRaWAN specification based on this technology. In view of the problem that the capacity of the LoRaWAN is seriously restricted by the gateway’s throughput under actual deployment, introducing a dual buffer-queues structure based on JIT (Just-In-Time) scheduling into the gateway. By this means, the probability of threads blocking in the gateway is reduced, and the working efficiency of the gateway is improved, thereby optimizing the capacity to forward downlink data of the gateway. Experiments done in this paper have proved that the throughput of the gateway using this JIT dual buffer-queues mechanism is significantly enhanced and is more suitable for Class C packets forwarding, which effectively solves the actual deployment problem for LoRaWAN such as low network capacity caused by different types of nodes and gateway paralysis caused by sudden increase in downstream traffic.
Image Segmentation for Pavement Crack Detection System

Pavement distress refers to the condition of pavement surface in terms of its general appearance. Cracks is a type of pavement distress and commonly occur in a road infrastructure. Crack on a pavement surface shows an early sign of pavement problems and aging. Thus, it is important to detect a serious crack as soon as possible to avoid any road accident that might occur. This study shows a comparison of three popular methods of image segmentation; watershed, k-means clustering and Otsu thresholding for pavement crack detection system in terms of it overall performance. Sample of crack images from three different types of crack such as transverse, longitudinal and crocodile crack are captured manually using digital camera and from online sources. The image is then imported into MATLAB software where it will be compressed but without reducing its quality and pixels intensity. The compressed image is then converted into grayscale to make it easier for analyzing as the system only need to work with one layer instead of three layers (RGB). The contrast of the image is then stretched to increase the level of contrast between the crack and the background. Then, the image will be segmented using three different segmentation method that are mentioned above. Lastly, morphological operation is used to reduce the noise from the image segmented. The result of the segmented image will be analyzed in term of its Structural Similarity Index (SSIM) and Mean Squared Error (MSE). The performance of the system is measure using images with a high level of contrast between the crack and the surface and images with a low level of contrast between the crack and the surface.
SCADA Design System with pH Effluent Monitoring on Sewage Treatment Plant in Mawar’s College at UiTM Shah Alam using Tbox LT-12 RTU

The current system for monitoring the pH effluent for Sewage Treatment Plant (STP) at Mawar’s college in Universiti Teknologi Mara (UiTM) Shah Alam is done manually and takes a longer time to get the results. In this work SCADA system is proposed for STP with pH effluent monitoring system using Remote Terminal Unit (RTU) with TBox LT2. This system can speed up and simplify the online monitoring process for data monitoring. To achieve this, the system will send the pH data to the RTU and the data can be viewed via IP address in the form of mimic diagram. This system also gives an alarm notification to the user remotely if the pH value is not in the range of 5.5 and 9.0. Through this SCADA system it will assist user in the process of directly monitoring the pH value of the STP pH effluent. From the result obtained, the developed SCADA system is capable of remotely supervise and data monitoring via mobile phone.
Sub-1-V BGR and POR Hybrid Circuit With 2.25-μA Current Dissipation and Low Complexity

Based on three groups of different offset voltages, a bandgap reference (BGR) and power-ON reset (POR) hybrid circuit is implemented in 65-nm CMOS. The BGR using amplifier offset voltage and current matching, and the POR using offset voltage comparison and loop settling feature, respectively, are proposed. This circuit, with low-quiescent current, not only generates a stable reference voltage independent of voltage and temperature variations, but also provides a high-robust supply-ramp-rate-tolerant power-ON/brown-out reset signal with a hysteretic window of 18–24 mV. Experimental results show that the circuit with line regulations (LNRs) of 2.63%–4.28%/V and temperature coefficients (TCs) of 22–32 ppm/°C across multiple chips has a power dissipation around <inline-formula> <tex-math notation="LaTeX">$2.25~\mu \text{W}$ </tex-math></inline-formula> from a 1-V supply voltage. The circuit achieves a low noise density of 18.5 nV/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz at 100-Hz offset frequency and a good figure of merit (FoM) performance. With an active core area of 0.014 mm<sup>2</sup>, the circuit has fixed reset trip voltages under the supply ramp time of 0.4–100 ms.
Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness

This letter addresses the modeling of a personal tendency by utilizing the data collected from a manned control system. In the control system, it is assumed that a control operator, namely a human controller, determines the control actions based on his/her tendency toward laziness. The tendency is described by a cost function that includes the <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> norm of the state and the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm of the control action. Then, the operator behavior is modeled by the solution to the optimization problem formulated with the <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-state/<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-action cost function and the plant model. The tendency modeling is reduced to the problem of estimating the cost function. The estimation problem is further extended by taking into account the operator dynamics caused by the recognition and motion to derive an MPC-based formulation. Finally, the estimation method is demonstrated via an actual manned control experiment with a toy game.
A Hybrid Network for Facial Age Progression and Regression Learning

Facial age transformation is an attractive application on an entertainment or amusement robot. With this application, the robot can transform an input face to the same face but in different ages. We propose a new algorithm for age transformation. Due to recent progresses made by state-of-theart deep learning approaches, the facial age progression and regression has become an attractive research topic in the fields of computer vision. Many existing approaches require paired data which refer to the face images of the same person at different ages. As the cost of collecting such paired datasets is expensive, some emerging approaches have been proposed to learn the facial age manifold from unpaired data. However, the images generated by these approaches suffer from the weakness or loss in generating some age traits, for example wrinkles and creases. We propose a hybrid network that is composed of a generator and two discriminators. The generator is trained to disentangle the age from the identity of the face so that it can generate a face of the same identity as of the input face but at a different age. One of the discriminator is designed for handling multitasks, including the identification of real vs. fake (generated) faces and the classification of the identities and ages of the faces. The other discriminator is designed to make the latent space satisfy the requirement so that the generated image can be made more realistic. Experiments show that the proposed network can generates better facial age images with more age traits compared with other state-of-the-art approaches.
Enhanced Performance Organic Light Emitting Diode With CuI:CuPC Composite Hole Transport Layer

Composite materials have attracted significant attention owing to their effective application in producing high-efficiency organic light emitting diodes (OLEDs). Hole transport layers are important and play a noteworthy character in improving the efficiency and stability of OLED. In this work, we have investigated the doping effect of the copper iodide (CuI) into copper (II) phthalocyanine (CuPC) hole transport layer on the performance of OLED. The composite hole transport layer was employed in solution processed OLED consisting 4,4′-Bis(N-carbazolyl)-1,1′-biphenyl (CBP) as a host, Tris (2-phenylpyridine) iridium (III) (Ir(ppy)3) as dopant, 2,2',2"-(1,3,5-Benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi) as electron transport layer, lithium fluoride as electron injection layer, and aluminum as cathode. We perceived that doping of CuI into CuPC led to a higher performance enhancement for the OLED than the use of PEDOT:PSS as hole transport layer. The OLED reveals maximum power efficiency (PE), current efficiency (CE) and external quantum efficiency (EQE) 74.1 lmW−1, 70 cdA−1 and 18.5%, respectively, with doping 20 wt % CuI into CuPC, while PEDOT:PSS based OLED exhibits PE of 48 lmW−1, CE of 49 cdA−1 and EQE of 14%. The enhancement of PE, CE and EQE of 33.3, 34.6 and 32%, which may be attributed to the optimized doping ratio of the CuPC and the CuI showing improved hole transport ability, optical properties, surface roughness, and the reduced hole injection barrier. Our outcomes deliver an efficient tactic to replace PEDOT:PSS and enhance the performance of OLED by incorporating CuI as dopants into CuPC.