Miracle-dz/UniFAR-FTUs huggingface dataset:
SINGLE IMAGE SUPER-RESOLUTION WITH LIMITED NUMBER OF FILTERS

In this paper, we propose a single image super-resolution with limited number of filters based on RAISR. RAISR is well known as rapid and accurate super-resolution method which utilizes 864 filters for upscaling. This super-resolution idea utilizes the filter learned with suﬃcient training set. To get low cost of calculation and comparable image quality with other highly accurate super-resolution methods, the patch of input image is classified into classes by simple hash calculation. Then, the high quality version of this patch is generated by applying the filter to low resolution patches. In our method, only 18 filters can make high resolution images by using simple geometric conversion and rotation conversion while keeping the accuracy and runtime of RAISR.
MutBug: A Lightweight and Compact Crawling Robot That Can Run on Both Sides

Double-sided crawling robots have potential to be quite useful for real-world applications. For example, double-sided crawling robots keep crawling even when they are turned over. In this letter, we present a lightweight and compact crawling robot that can run on both sides. To make this possible, two design strategies are proposed: the shared hip joints and the low profile differential drive based on orthogonally oriented four-bars. The shared hip joints allow the robot to have a thin body by reducing redundant structures. The orthogonally oriented four-bars, which can be installed in the limited thin body structure, enable the robot to equally transmit the motor's crank motion to the multiple legs. Thanks to these design strategies, the robot not only has compact size but also successful alternating gait. In result, the MutBug has body thickness of 15 mm and runs at the maximum speed of 0.53 m/s.
An Iterative Stacking Method for Pipeline Defect Inversion With Complex MFL Signals

Magnetic flux leakage (MFL) inspection in nondestructive testing (NDT) has been widely used in damaged pipeline defect inversion. The changeable environment and the complexity of MFL signal have brought severe challenges to the accurate estimation of defect sizes in inversion issue. This article proposes a novel pipeline defect inversion method (WT-STACK) based on stacking learning. This method consists of two parts. First, a multi-domain feature extraction with three-axis (axial, radial, and circumferential) signals is constructed. To avoid the feature information loss, signals are analyzed both in time and frequency domains. Second, to study the complex nonlinear relationship between the feature and defect size, an iterative stacking estimation network is developed with dynamic multi-domain features input. An adaptive learning is realized in the network, which enhances the generalization ability for different sample sets of defect inversion issue. Finally, the method is evaluated by experiments using MFL signals collected from experimental platform and simulation signals. Experimental results and comprehensive comparison analysis with other state-of-art methods validate the superiority of this method.
Resistive RAM Endurance: Array-Level Characterization and Correction Techniques Targeting Deep Learning Applications

Limited endurance of resistive RAM (RRAM) is a major challenge for future computing systems. Using thorough endurance tests that incorporate fine-grained read operations at the array level, we quantify for the first time temporary write failures (TWFs) caused by intrinsic RRAM cycle-to-cycle and cell-to-cell variations. We also quantify permanent write failures (PWFs) caused by irreversible breakdown/dissolution of the conductive filament. We show how technology-, RRAM programing-, and system resilience-level solutions can be effectively combined to design new generations of energy-efficient computing systems that can successfully run deep learning (and other machine learning) applications despite TWFs and PWFs. We analyze corresponding system lifetimes and TWF bit error ratio.
Rate-Less Codes Aided Cooperation Based on Antenna Selection in Multi-user Massive MIMO System

The design of coded cooperation scheme based on transmit antenna selection has been being a hot topic in wireless communications. The difficulty lies in the fact that plenty of channel codebooks have to be stored both at the transmitter and the receiver in advance for the fixed-rate coded cooperation, due to the dynamic cooperative transmit antennas in Multi-user massive multiple-input multiple-output (MIMO) system. To reduce the complexity, the rate-less codes aided cooperation scheme based on transmit antenna selection is described in this paper. And the bit error rate (BER) and the symbol error rate (SER) performance analyses are also derived. Simulation results show that the proposed scheme achieves much better performance gain compared to non-cooperative scheme.
Brain Connectivity Network Analysis and Classifications from Diffusion Tensor Imaging

In this paper an automated method was introduced for classifying Autism Spectrum Disorder (ASD) and Typically Developed (TD) brain based on graph theory-based features and classification using Diffusion Tensor Imaging (DTI). DTI is a promising method for characterizing microstructural changes in brain. This technique detects how water travels along the white matter tracks in brain. It is an MRI based neuroimaging technique. Using the created connectivity matrices developed from DTI, a graph theory-based analysis is performed and using that analysis some features have been extracted. Using these features, a classification work has been done between ASD and TD brains based on Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Principle Component Analysis (PCA) is an approach of reducing noisy features for better accuracy. Using SVM, classification accuracy of 75.00%, 62.50% & 56.25% are achieved from 2, 7 & 10 PCA features. Accuracy of 64.58%, 60.42% & 58.33% are achieved using LDA. Using less features is also showing better accuracy comparing same test samples.
Crack Detection Using Multiple Image Processing for Unmanned Aerial Monitoring of Concrete Structure

Concrete structures are subjected to crack formation over time. The causes of crack formation are shrinkage, thermal stress, weathering, etc. This paper is about the construction and application of a device that uses image processing to detect cracks. The system is carried by a drone and can be remotely accessed. The system has a graphical user interface for initializing the device, viewing real time video, taking pictures of a crack, measuring its width, and evaluating if safe or unsafe. The pictures and their results are stored for reference.
Artificial Intelligence in the Boardroom: Enabling ‘Machines’ to ‘Learn’ to Make Strategic Business Decisions

Artificial Intelligence (AI) has recently ventured from its academic ivory tower to the commercial world, and the consequences have raised interest in academia and industry alike. This paper explores the use of AI technologies in making strategic decisions in organizations by training AI systems to be a contributing member of the organizations' board of C-level members. The contribution of this paper is that it proposes a new AI-enabled strategic decision making model, based on a synthesis of the literature on AI and strategic decision making. The paper also presents an opportunity to empirically test this new model using diverse case studies.
An ITO Based High Gain Optically Transparent Wide Band Microstrip Antenna for K Band Satellite Communication

A high gain optically transparent microstrip patch antenna for K band (18 GHz–27 GHz) satellite communication has been proposed and investigated in this paper. In order to make sure the property of optical transparency of the antenna, optically transparent materials have been chosen for all the layers of the microstrip patch antenna. Indium Tin Oxide (ITO) film is opted for the radiating patch and ground plane where the substrate is made of pyrex glass (lossy) (Ɛr = 4.82 and tanδ = 0.0054). The proposed transparent antenna operates at center frequency of 24.8 GHz ranging from 17.625 GHz to 30.523 GHz that covers the entire K band of satellite communication. The transparency property of the patch antenna is useful to minimize the shadowing effect of the solar antenna as well as helpful to guarantee the maximum uses of satellite surface area. The proposed antenna has high IEEE gain of 12.36 dB and directivity of 13.38 dBi including efficiency of 92.38%. It also possesses very wide bandwidth (12.898 GHz), high front to back ratio (16.699 dB) and very good VSWR (1.0566) at center operating frequency.
Predicting Assignment Submissions in a Multi-class Classification Problem

Predicting student failure is an important task that can empower educators to counteract the factors that affect student performance. In this paper, a part of the bigger problem of predicting student failure is addressed: predicting the students that do not complete their assignment tasks. For solving this problem, real data collected by our university’s educational platform was used. Because the problem consisted of predicting one of three possible classes (multi-class classification), the appropriate algorithms and methods were selected. Several experiments were carried out to find the best approach for this prediction problem and the used data set. An approach of time segmentation is proposed in order to facilitate the prediction from early on. Methods that address the problems of high dimensionality and imbalanced data were also evaluated. The outcome of each approach is shown and compared in order to select the best performing classification algorithm for the problem at hand.
DATA-DRIVEN TIGHT FRAME FOR CRYO-EM IMAGE DENOISING AND CONFORMATIONAL CLASSIFICATION

The cryo-electron microscope (cryo-EM) is increasingly popular these years. It helps to uncover the biological structures and functions of macromolecules. In this paper, we address image denoising problem in cryo-EM. Denoising the cryo-EM images can help to distinguish different molecular conformations and improve three dimensional reconstruction resolution. We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF algorithm is closely related to the dictionary learning. The advantage of DDTF algorithm is that it is computationally efficient, and can well identify the texture and shape of images without using large data samples. Experimental results on cryo-EM image denoising and conformational classification demonstrate the power of DDTF algorithm for cryo-EM image denoising and classification.
Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals

In this paper we consider the generalized approximate message passing (GAMP) algorithm for recovering a sparse signal from modulo samples of randomized projections of the unknown signal. The modulo samples are obtained by a self-reset (SR) analog to digital converter (ADC). Additionally, in contrast to previous work on SR ADC, we consider a scenario where the compressed sensing (CS) measurements (i.e., randomized projections) are sent through a communication channel, namely an additive white Gaussian noise (AWGN) channel before being quantized by a SR ADC. To show the effectiveness of the proposed approach, we conduct Monte-Carlo (MC) simulations for both noiseless and noisy case. The results show strong ability of the proposed algorithm to fight the nonlinearity of the SR ADC, as well as the possible additional distortion introduced by the AWGN channel.
An Improved Channel Estimation Method for V2V OFDM Communication System

Vehicle-to-Vehicle (V2V) communication is crucial for intelligent transportation system (ITS). Accurate channel estimation is important for realizing reliable V2V communication. However, these existing channel estimation methods cannot provide an accurate estimation performance in low and high signal-to-noise ratio (SNR) simultaneously. To address this problem, an improved channel estimation method was proposed in this paper. The method we proposed is based on constructed data-pilots (CDP) channel estimation method, Spectral Temporal Averaging (STA), and SNR threshold determined by minimum square error (MSE) performance. Simulation results show our proposed method can get better Bit Error Rate (BER) performance than STA channel estimation method and CDP channel estimation method in a low SNR range, and get an almost same BER performance as the CDP channel estimation method in a high SNR range.
Target Re-detection and Tracking Based on Correlated Filter

Visual object tracking is a key research field in computer vision. Correlation filter tracker (CFT) based methods have shown the strong ability to track objects. However, they did not consider re-detecting the target when tracking failed, which leads to the bad performance. In order to solve this challenge, we propose a novel tracking algorithm that combines the correlation filter tracker and particle filter tracker to re-detect the target. Our algorithm consists three steps: First, we use the correlation filter tracker to track the target and calculate the average peak correlation energy (APCE). Then, we compare the APCE with the default threshold value to determine whether the tracking target misses. When the target missed, particle filter algorithm is used to re-detect the target and the location of the target is obtained in the current frame based on the location of the target in the previous frame. Compared with the staple tracking algorithm, experimental results show that the proposed algorithm is superior to the staple algorithm in distance precision and success rate. Besides, out algorithm operates fast and can meet the real-time requirement.
A 2-DOF Monolithic Compliant Rotation Platform Driven by Piezoelectric Actuators

To realize high-precision rotation angle adjustment in micro-/nanomanipulation, a novel monolithic two-degrees-of-freedom (2-DOF) pure rotation platform is designed, fabricated, and tested in this article. The rotation platform is driven by two piezoelectric actuators. Rotation decoupling is realized based on three Hooke's joints, and a large rotation range is obtained based on two bridge-type mechanisms. Compared with other rotation platforms, the developed 2-DOF pure rotation platform has the advantages of fewer actuators due to rotation decoupling design and larger working frequency range due to compact structure and monolithic fabrication. An analytical model is established to calculate transmission ratio and input stiffness. The dominant parameters are determined based on sensitivity analysis. Finite-element analysis is conducted to investigate the characteristics of the rotation platform. Experimental tests are carried out to investigate the performance of the rotation platform. Decoupling test results show that the maximum rotation angles in the X- and Y-axis are 2.04 and 2.12 mrad, respectively, and the X- and Y-axis relative coupling errors are 2.03% and 2.09%, respectively. Closed-loop control results show that the settling time is 40 ms and the resolutions in both X- and Y-axis are 5 μrad.
Soft robots based on dielectric elastomer actuators: a review

Conventional robots are mainly made of rigid materials, such as steel and aluminum. Recently there has been a surge in the popularity of soft robots owing to their inherent compliance, strong adaptability and capability to work effectively in unstructured environments. Of the multitude of soft actuation technologies, dielectric elastomer actuators (DEAs), also nicknamed ‘artificial muscles’, exhibit fast response, large deformation and high energy density, and can simply be actuated with electric voltage. In this paper, we will discuss applications of DEAs to soft robots, including robotic grippers, terrestrial robots, underwater robots, aerial robots and humanoid robots. We will survey the state of the art regarding these interesting applications and outline the challenges and perspectives. As we know, there have been extensive studies on dielectric elastomer technology in the aspects of materials, mechanics, design, fabrication and controls. To enable practical applications, efforts are underway to decrease operational voltages, improve reliability, and impart new functionalities. Key challenges include the development of freestanding actuators, untethered operation, smart/electronics free actuators, solid and stretchable electrodes, miniaturization, combination of synergistic actuation technologies to impart novel functionalities, development of effective control strategies, etc. We hope that this review can facilitate and enhance applications of dielectric elastomer technology to soft robots.
Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables

In this paper, a random forest regression model with multitype predictor variables (MTVRF) was utilized with four kinds of input variables, including surface reflectance, spectral indices, terrain factors, and land cover types, to establish the nonlinear relationship between land surface temperature (LSTs) and other land surface parameters. The main objective of this paper is to analyze the superiority of MTVRF model in multivariable regression wherever on the simple or complex underlying surface and further to demonstrate the robustness of random forest (RF) regression downscaling model trained in one study area while being applied to another area. The spatial resolution of the Moderate Resolution Imaging Spectroradiometer LST product was downscaled by MTVRF from 990 to 90 m. A comparison with two other downscaling methods, such as the basic RF model and the thermal sharpening algorithm, was also made. By computing the mean error, the determination coefficient ( $R^{2}$ ), and the root mean square error (RMSE) between the downscaled and referenced LSTs, the MTVRF model achieved a satisfied performance. Further satisfactory results were also obtained for the MTVRF to downscale LSTs for different land covers and evaluate the training model in various regions. The RMSE of the MTVRF model trained on study area B and evaluated on study area A was 3.13k, while the RMSE trained on study area A and evaluated on study area B was 2.11k; this shows the MTVRF model trained in a specific region is thought to be robust enough to downscale LSTs under other various surface conditions.
Operation of Autonomous AC Microgrid at Constant Frequency and with Reactive Power Generation from Grid-forming, Grid-supporting and Grid-feeding Generators

This paper presents a method for managing reactive power output from the grid-forming, grid-supporting and grid-feeding generators in an autonomous AC microgrid, which is operated at constant frequency. The grid-forming generator produces a reference voltage with a constant frequency of 50Hz while both the grid-supporting and grid-feeding generators take the voltage as reference at their respective coupling points with the microgrid. Both the grid-forming and grid-supporting generators produce output real power based on their respective terminal voltage deviation from the rated voltage. The grid-feeding generator outputs a planned real power. To manage the reactive power demand by the loads, reactive power output from each of the three types of generators needs be coordinated. In order to ensure accurate tracing of the reference of real power output from each of the grid-forming, grid-supporting and grid-feeding generators, the reactive power from each of them needs be kept within a fraction of its real power output. Such a fraction has been determined numerically by using a grid-tied inverter. The proposed method for operating the AC microgrid was validated using the real-time fixed time-step simulation in Matlab/Simulink platform.
Partial Matching Estimation Method of Walking Trajectories for Generating Indoor Pedestrian Networks

We propose a method that estimates the partial matching of a walking trajectory for integrate indoor walking trajectories and generate an indoor pedestrian network. Building structure information is necessary for the realization of indoor location information services (indoor LBSs). In indoor pedestrian networks and building structure information, since the shortest route calculation can be directly applied, the route from an arbitrary position to a destination can be obtained by a computer. Therefore, navigation and recommending a healthier route is possible by route recommendation. However, the generation of the indoor pedestrian network is costly, so it does not necessarily exist. Therefore, as a low-cost generation method, our final goal is to automatically generate an indoor pedestrian network structure by collecting the walking sensing data from many users and integrating the estimated 3-D walking trajectories. In the integration of such trajectories, such partial matching places as walking in the same passage are used as clues. To estimate a partially matched place, we use a stable zone (a stable walking zone) and linear walking. Since the stable walking zone detects the continuation of the state with only a slight change, the estimation accuracy becomes higher than the right and left turns. We estimate the correspondence among stable walking zones by machine learning using the walking time, the walking distance, the height, and Wi-Fi information. For machine learning, we use a support vector machine (SVM). From the results of an evaluation experiment with HASC-IPSC, an indoor walking sensing corpus, we obtained a result where the F value was 0.81.
Adaptive Balance of Quality-resources of Neural Networks with Principle Component Analysis

Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input classification. This computation overhead limits the applicability of DNNs to low-power, embedded platforms and incurs high cost in data centers. In this paper, by exploiting the gap between the level of accuracy required by the applications/users and that provided by the computing system, we propose a model to balance the resource and quality of neural network with principal components analysis (PCA), which simplifies network structure (such as the number of input nodes and the number of network layers) by reducing the dimension of dataset and achieve diverse optimizations. The continuous iteration and optimization of the balance model can acquire the best balance of quality and resources of the neural network. As a result, the consumption of network resources is reduced to the greatest extent under the condition of satisfying the quality requirements. In this paper, a pedestrian recognition network is taken as an example. The results illustrate that the total number of network nodes is reduced by 69% while the recognition rate achieved (95.99%) can reach required level of recognition rate (95%), which is only 2% lower than the highest recognition rate (97.01%) of the original neural network of pedestrian recognition.
Optimization of EVS Speech/Music Classifier based on Deep Learning

EVS (Enhanced Voice Services) is a multi-mode codec proposed by 3GPP (3rd Generation Partnership Project) for 4G mobile services with a good performance and codec quality. The key technology of EVS lies in the flexible switch between speech and audio coding mode which mostly depends on the speech/music classifier. In general, the music signal is more complex than speech signal, and it conform less to any known LP (Linear Prediction)-based model. Taking the EVS’s internal classifier as a baseline system, this study presents the optimization of the speech/music classifier from the perspective of neural network. The paper demonstrates the effectiveness of the optimized system on the MUSAN database. The experimental results show that the optimized system can improve the performance of the classifier, especially for music classification. Performed subjective experiments indicate that the proposed classification architecture improves perceived audio quality of the EVS codec.