Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion-Blind Blur Removal for CT Images with Wasserstein Generative Adversarial Networks

Advanced deblurring techniques for computed tomography (CT) images are necessary and crucial to the improvement of accuracy of patient diagnosis in radiology and patient setup and treatment response assessment in radiation oncology. Currently, medical image deblurring is a challenging technical problem due to the unpredictability of patient motion. This paper introduces a new method of computed tomography image deblurring based on Conditional Generative Adversarial Networks (CGAN) that have been broadly implemented in computer vision research. A Wasserstein Generative Adversarial Network (WGAN) with adversarial loss and l1 perceptual loss was proposed and trained by a blur-sharp image pair dataset created in-house and evaluated by Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). These experiments showed the effectiveness of the approach, which outperforms other competing deblurring techniques both quantitatively and qualitatively.
High Performance Thin Film Transistors With Sputtered In–Al–Zn–O Channel and Different Source/Drain Electrodes

High performance thin film transistors (TFTs) based on sputtered In–Al–Zn–O (IAZO) channel layer with excellent photoelectric properties were fabricated and characterized. The IAZO films remained amorphous with low-surface roughnesses both before and after annealing. The Hall mobility increased from 27.1 to 74.2 cm<sup>2</sup>/Vs after annealing. High average transmittances over 95% in the visible range and wide optical band gaps around 4.1 eV were obtained for both unannealed and annealed IAZO films. The energy level diagrams of IAZO films before and after annealing were derived based on the ultraviolet photoelectron spectroscopy measurements. A high field-effect mobility of 20.57 cm<sup>2</sup>/Vs, a high ON–OFF current (<inline-formula> <tex-math notation="LaTeX">${I}_{\textsf {on}}/{I}_{\textsf {off}}$ </tex-math></inline-formula>) ratio of <inline-formula> <tex-math notation="LaTeX">$\textsf {4.02}\times \textsf {10}^{\mathbf {7}}$ </tex-math></inline-formula>, and a subthreshold swing (<italic>SS</italic>) of 0.55 V/dec were obtained for the IAZO TFT with Au electrodes. In comparison, though a lower mobility of 9.54 cm<sup>2</sup>/Vs was exhibited, a higher <inline-formula> <tex-math notation="LaTeX">${I}_{\textsf {on}}/{I}_{\textsf {off}}$ </tex-math></inline-formula> ratio of <inline-formula> <tex-math notation="LaTeX">$\textsf {7.39}\times \textsf {10}^{\textsf {7}}$ </tex-math></inline-formula>, a lower SS, and a much lower hysteresis were obtained for the TFT with Ti electrodes.
RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework

In this paper, a learning based salient object detection method for RGBD images is introduced. With the assistance of depth information, the silhouette features of an object can be retrieved primarily, and it can lead to better detection of salient objects. In addition, many recent works still rely on some image post-processing methods to improve their performance. We develop a more efficient end-to-end model with a modified design of loss function used in our training network. The design of the new loss function is to increase the spatial coherence of detected salient objects. From the evaluation results, the proposed approach shows good performance compared with the methods that are considered to be state-of-the-art.
3.8 W/mm RF Power Density for ALD Al2O3-Based Two-Dimensional Hole Gas Diamond MOSFET Operating at Saturation Velocity

This letter reports the small-signal and large-signal performances at high drain voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {DS}}$ </tex-math></inline-formula>) ranging up to 60 V for a 0.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> gate length two-dimensional hole gas diamond metal-oxide-semiconductor field-effect transistor with a 100-nm-thick atomic-layer-deposited Al<sub>2</sub>O<sub>3</sub> film on a IIa-type polycrystalline diamond substrate with (110) preferential surfaces. This diamond FET demonstrated a cutoff frequency (<inline-formula> <tex-math notation="LaTeX">$\text{f}_{\textsf {T}}$ </tex-math></inline-formula>) of 31 GHz, indicating that its carrier velocity was reaching <inline-formula> <tex-math notation="LaTeX">$\textsf {1.0}\times \textsf {10}^{\textsf {7}}$ </tex-math></inline-formula> cm/s for the first time in diamond. In addition, a <inline-formula> <tex-math notation="LaTeX">$\text{f}_{\textsf {T}}$ </tex-math></inline-formula> of 24 GHz was obtained at <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {DS}}=-\textsf {60}$ </tex-math></inline-formula> V, thus giving a <inline-formula> <tex-math notation="LaTeX">$\text{f}_{\textsf {T}}\times \textsf {V}_{\textsf {DS}}$ </tex-math></inline-formula> product of 1.44 THz<inline-formula> <tex-math notation="LaTeX">$\cdot \text{V}$ </tex-math></inline-formula>. This diamond FET is promising for use as a high-frequency transistor under high voltage conditions. Under application of a high voltage, a maximum output power density of 3.8 W/mm (the highest in diamond) with an associated gain and power added efficiency were 11.6 dB and 23.1% was obtained when biased at <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {DS}}=-\textsf {50}$ </tex-math></inline-formula> V using a load-pull system at 1 GHz.
An Information Theoretic View on Learning of Artificial Neural Networks

Deep learning based on Artificial Neural Networks (ANNs) has achieved great successes over the last years. However, gaining insight into the fundamentals and explaining their functionality is an open research area of high interest. In this paper, we use an information theoretic approach to reveal typical learning patterns of ANNs. For this purpose the training samples, the true labels, and the estimated labels are considered as random variables. Then, the mutual information and conditional entropy between these variables are studied. We show that the learning process of ANNs consists of essentially two phases. First, the network learns mostly about the input samples without significant improvement in the accuracy, thereafter the correct class allocation becomes more pronounced. This is based on investigating the conditional entropy of the estimated class label given the true one in the course of training. We next derive bounds on the conditional entropy as a function of the error probability, which provide interesting insights into the learning behavior of ANNs. Theoretical investigations are accompanied by extensive numerical studies on an artificial data set as well as the MNIST and CIFAR benchmark data using the widely known networks LeNet-5 and DenseNet. Amazingly, in all cases the bounds are nearly attained in later stages of the training phase, which allows for an analytical measure of the training status of an ANN.
Adaptive Transmit Antenna Selection Based on PCA for Millimeter Wave LOS MIMO Channel

Uniform rectangular antenna array (URA) is a commonly used antenna array in multiple-input multiple-output (MIMO) system. However, the existing works rarely concentrate on URA in millimeter-wave (mmWave) line-of-sight (LOS) MIMO channels. In this paper, we conduct research on the point-to-point mmWave LOS MIMO channel based on URA. We derive the optimal antenna deployment for URA to obtain the maximum multiplexing gain in mmWave LOS MIMO channel. For the URA with fixed parameters, as the distance between transmitter and receiver increases, some singular values of the channel matrix tend to be 0. In this case, it is not necessary to use all of the transmit antennas. Therefore, according to the two kinds of channel-state information feedback schemes, we propose two adaptive transmit antenna selection (ATAS) schemes based on the principal component analysis for this mmWave LOS MIMO channel. The computational complexity for the proposed algorithm is also derived. Numerical results show that the channel capacity of this proposed ATAS system can approach or even surpass that of the full transmit antenna system with equal power allocation in mmWave LOS MIMO channel. Compared with transmit antenna selection based on greedy search and exhaustive search algorithm, when the distance is longer, the proposed algorithm can achieve the near-optimal performance with much lower computational complexity.
Design of a Gate Diode Triggered SCR for Dual-Direction High-Voltage ESD Protection

A novel gate diode triggered silicon-controlled rectifier (GDTSCR) with dual-direction high-voltage (HV) electrostatic discharge (ESD) protection and a low snap-back voltage is proposed and investigated. Compared to conventional MOS triggered SCR (MTSCR), the GDTSCR has two gate diodes and one additional n-p-n. Superior to the MTSCR, the GDTSCR exhibits a high holding voltage of 15 V, a small trigger voltage of 17 V, and a strong ESD robustness of 6000 V in an area of $1600~\mu \text{m}^{\textsf {2}}$ , benefitting from the gate-controlled field effect diodes and the weakened regenerative feedback of the SCR. As such, the proposed GDTSCR is an attractive device for constructing effective and latch-up immune solutions for HV ESD protection.
Co-Simulation Method for Hardware/Software Evaluation Using Xilinx System Generator: A Case Study on Image Compression Algorithms for Capsule Endoscopy

Testing using the co-simulation method of an algorithm is important to visualize the hardware performance of the algorithm. This study aims to introduce a method for comparing the hardware performance of two different algorithms with a focus on the existing compression system for capsule endoscopy as a case study. The size limitation of capsule endoscopy which constrains the power source and the complexity of the hardware utilization are the main reasons for selecting the capsule endoscopy compression system for this case study. Undertaking a comparison between different algorithms from the existing image compression algorithms in capsule endoscopy can highlight the main testing steps of the co-simulation testing method. To allow a fair comparison, the hardware co-simulation is implemented using the same FPGA device for the methods compared in this study and the results acquired are discussed.
Analytical Performance Assessment of THz Wireless Systems

This paper is focused on providing the analytical framework for the quantification and evaluation of the joint effect of misalignment fading and hardware imperfections in the presence of multipath fading at terahertz (THz) wireless fiber extenders. In this context, we present the appropriate system model that incorporates the different operation, design, and environmental parameters. In more detail, it takes into account the transceivers antenna gains, the operation frequency, the distance between the transmitter (TX) and the receiver (RX), the environmental conditions, i.e., temperature, humidity, and pressure, the spatial jitter between the TX and RX antennas that results to antennas misalignment, the level of transceivers’ hardware imperfections, and the stochastic characteristics of the wireless channel. Based on this model, we analyze and quantify the joint impact of misalignment and multipath fading by providing novel closed-form expressions for the probability and cumulative density functions of the composite channel. Moreover, we derive exact closed-form expressions for the outage probability for both cases of ideal and non-ideal radio frequency (RF) front-end. In addition, in order to quantify the detrimental effect of misalignment fading, we analytically obtain the outage probability in the absence of misalignment cases for both cases of ideal and non-ideal RF front-end. In addition, we extract the novel closed-form expressions for the ergodic capacity for the case of the ideal RF front-end and tight upper bounds for both the cases of ideal and non-ideal RF front-end. Finally, an insightful ergodic capacity ceiling for the non-ideal RF front-end case is provided.
Stator Turn Fault Modeling for a Triple Redundant 3 × 3-Phase PMA SynRM

Accurate stator turn fault (STF) modeling technique is of paramount importance to assess the machine fault behavior and develop fault detection and mitigation strategies for a fault-tolerant machine drive system. In this paper, an STF model is proposed for a triple redundant 3 × 3-phase permanent-magnet-assisted synchronous reluctance machine. The effect of turn fault current is represented by equivalent inputs to a general flux linkage model for each three-phase set. Subsequently, the flux linkage of the fault turns is derived according to the coil location and slot position of the fault. The complete model is obtained by combining the flux linkage model with the governing voltage equations. The effectiveness and flexibility of the proposed model have been validated by extensive finite element simulations and experimental tests in various operation conditions. It is demonstrated that the model can predict the machine behavior with and without the mitigation action in the form of terminal short circuit.
CPW Fed Wideband Corner Bent Antenna for 5G Mobile Terminals

A co-planar waveguide fed slot antenna element with 34% impedance bandwidth and 2–3 dBi broadside gain is proposed. The corner bent topology of the element is investigated for reduced physical footprint and easier integration with the mobile terminals operating in the 28-GHz band. The front-to-back ratio of the corner bent antenna is 1 dB in the <inline-formula> <tex-math notation="LaTeX">$xy$ </tex-math></inline-formula> plane, which indicates a high specific absorption rate. A compact wideband reflector is proposed which has transmission of less than −25 dB and a linear phase ±90° in the 27–33-GHz bandwidth. The wideband reflector is integrated with the corner bent antenna at <inline-formula> <tex-math notation="LaTeX">$0.135\lambda $ </tex-math></inline-formula> offset from the radiating aperture. The corner bent antenna integrated with the reflector operates in the 27–33-GHz band, with a front-to-back ratio of more than 14 dB across the band and a forward gain of 6–7.2 dBi. The radiation patterns are stable for 20% bandwidth. The simulated and measured results are presented.
A Novel Framework Called HDU for Segmentation of Brain Tumor

It is well known that U-net is the most advanced medical image processing framework, but it performs poorly in processing complex images. DenseNet is a framework for improvement based on U-net, which has been proposed in recent years, with well performance but large parameters compared with U-net. This paper proposes a Half Dense U-net network, which combines the advantages of DenseNet and U-Net, reduces the number of DenseNet parameters and improves the segmentation accuracy. Compared with U-Net, DenseNet and ResNet proposed in recent years, our proposed model can precisely locate the tumor boundary of brain tumors, thus obtaining higher recognition quality.
X/Ku Dual-Band Single-Layer Reflectarray Antenna

In this letter, X/Ku dual-band single-layer reflectarray antenna is presented. The proposed reflectarray antenna operates in two separated bands with separate feeds. The reflectarray element is composed of a split ring with two phase delay lines and a phoenix patch. The split ring and phoenix patch are designed for operation at X- and Ku-bands, respectively. The split ring is selected as the lower frequency element because of the large enough internal space. The phase shift of these two elements does not depend on variation of external size, which provides a feasible method for designing dual-band elements. The lower band phase shift is obtained by changing the rotation angle of the phase delay lines. The movement of the inner loop of phoenix patch provides the required phase shift for the higher band. The proposed element realizes 360° phase shift range in both bands. The reflectarray is fabricated and measured to validate the design. The measured gains are 22.9 dBi at 8.5 GHz with aperture efficiency of 40%, and 28.8 dBi at 16 GHz with aperture efficiency of 44.8%. Besides, the 1 dB gain bandwidths are 20.5% (7.9–9.7 GHz) at lower band and 13.3% (14.7–16.8 GHz) at higher band.
A Unified Matrix-Based Convolutional Neural Network for Fine-Grained Image Classification of Wheat Leaf Diseases

Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification.
Experimental Verifications of Low Frequency Path Gain ( $PG$ ) Channel Modeling for Implantable Medical Device (IMD)

With the development of microelectronics and sensor technologies, implantable electronic devices are employed in many applications. These devices are distributed on or in the human bodies and are used to transmit signals wirelessly to external equipment. In conventional wireless communications, the antennas need a lot of space and power, and their strong electromagnetic interference limits the available locations for implantable devices. In the more recently developed galvanic coupling intra-body communication technology, human tissues are used as the media of signal transmission, and this method has therefore been applied to resolve the spatial limitations of conventional wireless communications methods. This paper presents a mathematical model of multi-layer galvanic coupling based on the volume conductor theory to analyze the transmission mechanism of these implantable intra-body communication devices. The proposed model is based on the quasi-static approximation conditions of Maxwell’s equations, the field and potential are solved from Poisson’s equation, and an equation was obtained to model the channel attenuation. The channel gain in a model of human limbs can be used to calculate within the frequency range of lesser than 1 MHz. To verify the accuracy and applicability of the model, the computed results were compared with the physiological saline and porcine tissue experimental results in the 100-kHz frequency.
MRI-Based Diagnostics of Depression Concomitant with Epilepsy: In Search of the Potential Biomarkers

In the present work, we study the candidate biomarkers for the depression disorder and the depression + epilepsy comorbidity. Building on the advanced data analysis pipeline, we identify candidate biomarkers, compare them across tasks and to the previous research. The classification performance achieved by our system compares favourably to the one reported in literature, where longer scanning sessions and stronger magnetic fields were employed.
Behavioral Modeling of Photon Arrival Time for Time-of-Flight Measurement Circuit Simulation

This letter proposes a behavioral model to estimate the statistical photon arrival time for time-of-flight (TOF) measurement circuit simulation. A theoretical model is derived to calculate the photon arrival rate by full consideration of a variety of properties of a TOF measurement system, such as laser source, optics, single-photon avalanche diode detector, target distance, and ambient illumination. For the first time, the newly developed model is implemented in behavioral Verilog-A hardware description language and successfully operated on Cadence Spectre simulator for TOF circuit simulation. It is capable of accurately predicting the statistical performance parameters such as photon arrival time and achievable distance, providing a new candidate tool for the ease of design and simulation of various high-performance TOF measurement circuits.
A SIMULATION AND ONLINE OPTIMIZATION APPROACH FOR THE REAL-TIME MANAGEMENT OF AMBULANCES

Emergency Medical Service is one of the most important health care services as it plays a vital role in saving people’s lives and reducing the rate of mortality and morbidity. A peak of emergency demand can determine overcrowding at the emergency department. In this operative context, a challenge is the definition of proper real-time dispatching, routing and redeployment policies (DRRP) in such a way to maximize the number of emergency requests served within a time threshold, and to minimize the waiting times. The contribution of this paper is twofold. The former is a simulation model capable to deal with the real-time management of the ambulances, and to generate new ad hoc instances. The latter is a set of simple online algorithms to implement several DRRP. An extensive comparison among different DRRP is also provided.
Design of Hidden-Property-Based Variable Universe Fuzzy Control for Movement Disorders and Its Efficient Reconfigurable Implementation

One of the challenging problems in real-time control of movement disorders is the effective handling of time-variant brain activities that involve stochastic functional networks with nonlinear dynamics. For such challenges in neuromodulation tasks, fuzzy logic control (FLC) has shown significant potential. The objective of this paper is to present a FLC-based strategy to treat pathological symptoms of movement-disorder with higher performance. The strategy is two-fold: first, develop a design methodology for the FLC system that can robustly control pathological conditions and significantly improve control performance; and second, develop a hardware-efficient implementation for real-time neuromodulation applications. To enhance control performance, a hidden variable in the neural network that can be estimated using an unscented Kalman filter is identified as a feedback variable. In comparison with state-of-the-art schemes, the proposed design can adaptively optimize the control signals without requiring particular information of the controlled plant, thus avoiding repeated determinations of controller parameters. A field-programmable gate array is used for the reconfigurable realization of the entire control strategy based on a modification of the original neural network. The presented design, with enhanced control performance and higher hardware efficiency, has significant potential for clinical treatment of movement disorders and offers a new perspective on applications in the fields of neural control engineering and brain–machine interfaces.