Miracle-dz/UniFAR-FTUs huggingface dataset:
SAR Image Change Detection Based on Nonlocal Low-Rank Model and Two-Level Clustering

Change detection (CD) has found a wide range of applications in many fields. In this article, we propose a novel nonlocal low-rank (NLR) based method for multitemporal synthetic aperture radar image CD. This method jointly exploits the powerful NLR-based despeckling and the effective cascade clustering. First, the NLR model is used to generate the difference image (DI), which consists of a patch grouping process and a low-rank minimizing process. Especially, the NLR minimization model contains a data fidelity term, which is based on the statistical distribution of speckle noise, and a regularization term, which uses the weighted nuclear norm. Then, the alternating direction methods of multipliers is introduced to solve this minimization problem. Second, after DI is generated, the principal component analysis is employed to extract the feature and a two-level clustering method is used to generate the final change map, which separates the intermediate class by using the neighbor information with Gaussian weighted distance. Experiment results demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.
Controlled Release of Dual Antibacterial Drug from Composite Hydrogels

Hydrogels with a controlled release of dual antibacterial drug have been developed to effectively prevent bacterial infections for biomedical applications. The synergistic dual drug aims to fight against drug resistant bacteria, which become more commonly found nowadays. In this study, we fabricated composite hydrogels using calcium carbonate microspheres as drug carriers to load a combination of tetracycline and ampicillin into the matrices for controlled drug release. The composite hydrogels were prepared using three different molar ratios between polymers and crosslinkers to investigate the effect of crosslink density on the physical properties and drug release. The drug release profiles were quantitatively analyzed to compare diffusivity and release mechanism. The results revealed that the crosslinker molar ratio influenced not only the physical properties, but also the drug release rate and mechanism. The different release characteristics of the drugs also suggested chemical interactions between the calcium carbonate microspheres and the small molecules. Such insight could assist designing of composite hydrogels for controlled dual drug release in biomedical applications.
Driver Drowsiness Detection by Yawn Identification Based on Depth Information and Active Contour Model

increasing road accidents, caused by sleepy drivers is an important transportation problem now a day. A high percentage of road accidents are caused by drivers' lack of awareness caused by fatigue. Hence, the development of an automated system that determines the driver's drowsiness and issues a warning as soon as the driver loses attention on the road is a necessary step to prevent road accidents. This system must also function under illumination variations that have previously been a complication in similar works. Specific postures of the body and face may be regarded as indications of driver's fatigue. These include yawning and eye closure which determine whether or not the driver is in an adequate condition for driving. In this paper, in order to detect a driver's yawning, her nose tip is first identified in the depth image of the face and then the lower area of the face is separated. In the resulting image, the approximate area of the mouth is found and using depth information and the active contour model, an open mouth is identified. Experiment results show that the presented method can detect yawning with 95% accuracy.
Spin-transfer torque MRAM with reliable 2 ns writing for last level cache applications

We report for the first time reliable 2 ns switching of spin-transfer torque magneto-resistive random access memory (STT-MRAM) devices by demonstrating 100% write-error-rate (WER) yield at 1e-6 write-error floor of 254 devices with tight distributions and steep WER slope at a nominal size of 49 nm. A single device was demonstrated with 2 ns write pulses to have less than 1e-11 write-error rate, limited only by test time. We further demonstrate reliable 3 ns switching performance, with 99% WER yield at 1e-6 write-error floor of 256 devices with nominal size of 43 nm and a single device with less than 1e-11 write-error rate with a completely different free layer materials design. These two different free layer materials designs address one of the major remaining challenges for STT-MRAM to replace SRAM for last level cache (LLC) applications.
Neutralization Line Decoupling Tri-Band Multiple-Input Multiple-Output Antenna Design

A pair of tri-band multiple-input-multiple-output (MIMO) antennas with high isolation is investigated in this paper. The proposed antenna consists of two monopole antenna elements with an edge to edge spacing of 4 mm ( $0.03~\lambda _{0}$ at 2.3 GHz). The monopole antenna element chose the bending line structure that can operate in tri-band and realize miniaturization. To achieve compact dimension and high isolation, symmetrical distribution layout is adopted to decouple the low frequency band, the U-shaped neutralization line (NL) contacting with two microstrip lines, and it can to improve the isolation in high frequency band, and the inverted U-shaped NL contacting with two radiation patch can decoupling in the middle frequency band. And then study the envelope correlation coefficient is lower, and the radiation pattern in operation bands is quasi-omnidirectional. It indicates that the antenna has obtained a satisfactory diversity performance within the whole operation bands which can be a good candidate for some portable MIMO applications.
Applying Modified Code Entity-Based Regression Test Selection for Manual End-to-End Testing of Commercial Web Applications

Regression testing plays an important role in maintaining the quality of web applications as they are modified. To reduce the cost of this testing, researchers have proposed a variety of regression test selection (RTS) techniques. The challenges in applying RTS include the lack of tool implementations and the lack of many application requirements such as automated testing and sufficient documentation. This paper presents the challenges and solutions of applying a simple and safe RTS approach to manual E2E testing of legacy web applications, and presents a case study where it is applied to two development projects. Case study results show that this approach can reduce testing more precisely than a test reduction approach based on developer experience with sufficiently low deployment and labor costs.
A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)

PM2.5 is one of the most important pollutants related to air quality, and the increase of its concentration will aggravate the threat to people’s health. Therefore, the prediction of surface PM2.5 concentration is of great significance to human health protection. In this study, A hybrid CNN-LSTM model is developed by combining the convolutional neural network (CNN) with the long short-term memory (LSTM) neural network for forecasting the next 24h PM2.5 concentration in Beijing, which makes full use of their advantages that CNN can effectively extract the features related to air quality and the LSTM can reflect the long term historical process of input time series data. The air quality data of the last 7days and the PM2.5 concentration of the next day are first set as the input and output of the model due to the periodicity, respectively. Subsequently four models namely univariate LSTM model, multivariate LSTM model, univariate CNN-LSTM model and multivariate CNN-LSTM model, are established for PM2.5 concentration prediction. Finally, mean absolute error (MAE) and root mean square error (RMSE) are employed to evaluate the performance of these models and results show that the proposed multivariate CNN-LSTM model performs the best results due to low error and short training time.
Analytical model for PMSM Analysis Including Finite Soft-Magnetic Material Permeability

This paper presents a simplified analytical method to predict the influence of magnetic finite permeability of iron core on machine’s parameters of a surface mounted permanent magnet synchronous machine. On the open circuit, two-dimensional field distributions produced by permanent magnets synchronous machine (PMSM) are obtained in analytical approach forms by taking into account the relative permeability value of a ferromagnetic rotor and stator core. The analytical solution is given by applying boundary and interface conditions in each region of the proposed machine. Analytical method is verified by the finite-element analysis (FEA).
Automatic segmentation of polycystic kidneys from magnetic resonance images using decision tree classification and snake algorithm

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive bilateral renal cyst formation, leading to severe increases in kidney volume and loss of function. Total kidney volume (TKV) is the only established biomarker for tracking ADPKD. This is measured multiple times per year from each patient to examine the extent of renal enlargement and overall cyst load. Currently this is conducted by planimetry tracing, which involves manually delineating kidneys from surrounding tissues in the abdominal cavity using a digital drawing tool. By performing this on every image in a magnetic resonance scan, TKV is estimated. This is a time-consuming and laborious process for radiologists. Our aim is to develop an automated method for ADPKD patient kidney segmentation and quantifying TKV. Thirteen MRI scans of kidneys ranging across the spectrum from normal to severe cyst load were analyzed. Images were separated into two halves, each made up of 200 square regions. Features were extracted from grayscale values of each region, and these data were combined in a supervised decision tree algorithm to classify between kidney and non-kidney regions. Filtering and dilation were applied to the classified 400x400 matrix in order to roughly segment the kidneys. Contrast enhancement and k-means clustering was performed before applying an active contour function to determine kidney edges. Eccentricity analysis confirmed appropriate relative sphericity for segmented kidney shapes, before combining their areas with linear extrapolation to estimate TKV. This protocol is evaluated against clinical reference standard TKV measurements.
New Proposal to Compare Student Data in Institutional Research

This article proposes new criteria for using student data in universities. First criteria are called primary data and secondary criteria are called secondary data. We define primary data as those that are not linear combination data, and secondary data as a linear combination of primary data. For example, at the macro-level, primary data are correct and incorrect answers to a question in an examination or students' attendance and absence from a lecture. At the macro-level, secondary data are the total points in an examination or students' total attendance in and absence from a lecture. At the meso-level, secondary data are student records of lectures as well as grade point average, or rank, in the annual record of the university. Primary data are mainly constructed by faculty while secondary data are constructed by administrative staff. To compare primary and secondary data, collaboration between faculty and administrative staff is important.
Formation of vitamin A lipid droplets in pancreatic stellate cells requires albumin

Objective: Quiescent pancreatic stellate cells (PSCs) store vitamin A as cytoplasmic lipid droplets, and, when activated by profibrogenic stimuli, they transform into myofibroblast-like cells characterised by the loss of vitamin A droplets. Activation of stellate cells is central to fibrogenesis, but the mechanism for the formation of vitamin A droplets and its relationship to stellate cell activation remain unclear. Methods: With use of cultured PSCs, an attempt was made to characterise the function of albumin endogenously expressed in stellate cells. Results: Albumin is endogenously expressed in quiescent PSCs, localised in cytoplasmic lipid droplets, and its levels are markedly reduced after stellate cell activation. Continuous albumin expression in stellate cells is sufficient to maintain their fat-storing phenotype even after cell passages and renders cells resistant to the activating effects of transforming growth factor β (TGFβ). Forced expression of albumin in PSCs after passage 2 (activated PSCs) induced the re-appearance of lipid droplets and phenotypic changes, which were previously reported with retinol treatment. Retinol increases albumin synthesis in activated PSCs and the suppression of albumin expression using small interfering RNA (siRNA) abolishes retinol-induced effects. Conclusions: The data demonstrate a novel role for albumin in the formation of cytoplasmic vitamin A lipid droplets in stellate cells, and suggest that albumin may have a direct influence on stellate cell activation.
A Robust Segmentation of Blood Vessels in Retinal Images

Retinal blood vessels generally appear as wire mesh structures that have different widths. Their morphology plays a crucial role in indication and timely handling of fatal diseases, for instance diabetic retinopathy or hypertension. To address the aforementioned issues, this paper presents a robust vessel segmentation technique. First, the input retinal image is splitted into the Red, Green, and Blue Channels. On the Green channel pre-processing is done to eradicate noise or other disease. This step also extracts key feature, such as bright or dark contrast of the input retinal image through top-hat morphological opening operation. Meanwhile, the Gaussian matched filters are applied along with different scales to achieve segmentation of thick or thin vessels. Finally, the pixel-by-pixel OR operation is applied to obtain final segmented image. Simulations on DRIVE and STARE datasets indicate that proposed technique surpasses recent methods in terms of Accuracy (Acc). The proposed technique is also computationally efficient than most of the recent methods compared therein.
An Efficient Method to Reduce Crosstalk for Multi-layered GNR Interconnects at 32 nm Technology

In recent, the coupling capacitance becomes prominent in deep submicron and nanotechnology. It can significantly affect the performance of a digital circuit that causes digital logic fault and degrades the performance of Integrated Circuits (ICs). To address this problem, the research paper introduces two shielding methodologies (active and passive shielding) of a multi-layered graphene nanoribbon (MLGNR). An RLC based multi-conductor transmission (MTL) line model is simplified to an equivalent single conductor (ESC) to address the impact of the crosstalk-induced delay. The model parameters are used with reference to the ITRS 32 nm technology in order to demonstrate the performance for different interconnect spacing and lengths. For better understanding, shielded and unshielded methodologies are demonstrated to provide a comparative analysis and improvement with respect to unshielded interconnect lines. Irrespective of the shielding methodology, it is evident that an active shielding provides an improved crosstalk-induced delay compared to passive shielding for different interconnect spacing. At 32nm, the simulation results demonstrate that an active shielded interconnect outperforms passive and without shielding techniques by on an average 6.31% and 44.51% for the crosstalk-induced delay, respectively for different spacing at global lengths. In addition to that, active shielding provides an improved power delay product (PDP) of approximately 6.75% and 16.83% than passive shielded interconnects for 5nm and 20nm spacing, respectively at 1000µm length. Therefore, in order to accomplish the constraint of next-generation technology, an MLGNR can be adopted as a desirable shielding nanomaterial.
Silicon nitride waveguide coupled 67+ GHz Ge photodiode for non-SOI PIC and ePIC platforms

A Ge photodiode, directly coupled to a silicon nitride waveguide, showing more than 67 GHz bandwidth is demonstrated for the first time, which paves the way for utterly new SiN waveguide platform based applications. By light feeding through SiN waveguides, the new photodiode can also be a key enabler for a bulk-Si based, monolithically integrated electronic-photonic integrated circuit platform. We show that the new devices, fabricated on bulk-Si, provide the same bandwidths as Si waveguide coupled SOI based reference Ge photodiodes. However, their O-band responsivity is 0.3 A/W, which is about three times lower compared to the SOI waveguide coupled devices. We attribute this effect to substrate losses and few specific layout features but see some potential for improvement by design and technological optimizations. We demonstrate that the diodes can be fabricated with high yield and low metrics tolerances.
Endurance improvement of more than five orders in GexSe1-x OTS selectors by using a novel refreshing program scheme

Selector device is critical in high-density cross-point resistive switching memory arrays for suppressing the sneak leakage current path. GexSe1-x based ovonic threshold switch (OTS) selectors have recently demonstrated strong performance with high on-state current, nonlinearity and endurance. Detailed study of its reliability is still lacking and the understanding on the responsible mechanisms is limited. In this work, for the first time, the endurance degradation mechanism of Ge-rich GexSe1-x OTS is identified. Accumulation of slow defects that remain delocalized at off-state and GeSe segregation/crystallization during cycling lead to the recoverable and non-recoverable leakage current, respectively. Most importantly, a refreshing program scheme is developed to recover and prevent the OTS degradation and the endurance can be therefore improved by more than five orders without adding additional material elements or process steps.
A Review of Deep Learning for Single Image Super-Resolution

Super-Resolution (SR) refers to the reconstruction of corresponding high-resolution images from observed low-resolution images, which has important application value in monitoring equipment, satellite images and medical images. According to the number of input images, super-resolution can be divided into single image Super-Resolution (SISR) and multi-frame image super-resolution (MISR), in which single image super-resolution is better and more respected in efficiency and practical application. So far, mainstream algorithms of SISR are mainly divided into three categories: interpolation-based methods, reconstruction-based methods and learning-based methods. Because of the high performance of in-depth learning, Deep Learning for Single Image Super-Resolution has attracted much attention in the past five years. In view of the current SISR hotspot, i.e. the single image super-resolution method based on depth learning, this paper summarizes the development history of SISR, studies the advantages and disadvantages of each excellent algorithm, and discusses the development trend and challenges of the algorithm.
Cost-Effective Prediction in Medicine and Marketing: Only the Difference Between Bayesian Model Averaging and the Single Best-Fit Model

There is a need for cost-effective tools to identify a small fraction of the population with a high probability of having a disease (in medicine) or buying the product (in marketing). Instead of the most probable model, Bayesian model averaging attempts to find the most probable prediction, given the data. None of these methods give satisfactory results in predicting from small-data, low-signal data sets. Using an example problem of predicting who has a rare lung cancer, this paper demonstrates that improved results can be found from considering only the difference between the predictions of the single best-fit model and Bayesian model averaging, and shows the correlation between precision for cancer and the size of that difference in predictions.
Maximizing Joint Data Rate and Resource Efficiency in D2D-IoT Enabled Multi-Tier Networks

The next-generation wireless network is expected to be highly dense with a large number of Device-to-Device (D2D) communication enabled IoT devices in fog computing based cellular networks. The dense deployment of the heterogeneous network architecture is expected to fulfill the smart devices’ growing data demand, lower power consumption, and lower latency constraint. The 5G technology is expected to have such multi-tier architecture with various computational capability and radio enabled IoT devices. However, the coexistence of such heterogeneous network model spawns research challenges such as interference management, non-uniform computational capacity with non-uniform devices connectivity and service deadline. Thus, in this paper, we propose a coexistence of D2D-IoT (D-IoT) and fog computing model in cellular networks and formulate the resource allocation problem in such a multi-tier architecture considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching based solution. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieve more than 94% of optimum network performance.
Bi-directional Sub-60mV/dec, Hysteresis-Free, Reducing Onset Voltage and High Speed Response of Ferroelectric-AntiFerroelectric Hf0.25Zr0.75O2 Negative Capacitance FETs

Being the first demonstration of quasi-antiferroelertic Hf1-xZrxO2 (QAFE-HZO, Zr=75%) negative-capacitance (NC) FET with non-hysteretic bi-directional sub-60mV/dec (SSfor=51mV/dec, SSrev=53mV/dec, ΔVT<1mV) for low onset voltage and high speed response, both forward & backward (reverse) sweep exhibit obvious N-DIBL and NDR of QAFE-HZO to confirm NC effect. There is an operation window for steep SS without hysteresis for QAFE-HZO as compared with accompanying a non-negligible hysteresis FE-HZO (Zr=50%). The QAFE-HZO is also serviceable for both n-type and p-type channel FETs. The voltage transient response presents NC time 48%-77% improvement and VFE overlap of negative dQ/dVFE for forward and reverse, indicating non-hysteretic QAFE-HZO.
Impact of Intra-Cluster Angular Spread on the Performance of NLoS Millimeter Wave Links With Imperfect Beam Alignment

Beam misalignment is an unavoidable occurrence in millimeter wave (mmWave) wireless communication systems which causes a drop in achievable spectral efficiency (SE) and adversely affects the link-level performance. Spatial parameters of the mmWave propagation channel, especially angle distributions of multi-path components are vital in assessing the loss in achievable capacity due to imperfect beam alignment in such systems. In this paper, we present a tractable approach to quantify the achievable SE of beamformed Non-Line-of-Sight (NLoS) links in mmWave fading channels with intra-cluster angular spread. We derive the expression for achievable SE of a point-to-point mmWave NLoS link after accounting for random beam misalignment errors at the transmitter as well as receiver end. Using analytic approximations, we consider the impact of antenna beam pattern parameters, and use a comprehensive mmWave channel model for performance evaluation. The analysis provides realistic bounds on achievable SE since the intra-cluster angular spread of rays at both ends is taken into account. We provide insights into the interplay between antenna half power beam width, the degree of misalignment, channel fading coefficient, channel coherence parameter and the angular spread of rays to demonstrate the decisive impact of intra-cluster spread on the performance of mmWave point-to-point NLoS links.