Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind DCT-based prediction of image denoising efficiency using neural networks

Visual quality of digital images acquired by modern mobile cameras is crucial for consumers. Noise is one of the factors that can significantly reduce visual quality of acquired data. There are many image denoising methods able to efficiently suppress noise. However, often in practice denoising does not provide sufficient enhancement of images or even demonstrates visual quality reduction compared to observed noisy data. This paper considers the problem of prediction of denoising efficiency of images in a blind manner under additive white Gaussian noise condition. The proposed technique does not require a priori knowledge of a noise variance and uses a moderate amount of image data for analysis. The denoising efficiency prediction employs neural networks (all-to-all connected multi-layer perceptron)to create a regression model. Image statistics obtained in the spectral domain are used as input data and the state-of-the-art visual quality metrics are considered as outputs of the network. As a target denoising method, block matching and 3D filtering (BM3D)technique is used. It is demonstrated that the obtained neural networks are compact and overall prediction procedure is fast and has an appropriate accuracy to confidently answer to the question: “Do we need to denoise an image? ” The full dataset, executable code and demo Android application is available at https://github.com/asrubel/EUVIP2018.
On the Development of Adaptive, Tendon-Driven, Wearable Exo-Gloves for Grasping Capabilities Enhancement

Soft, underactuated, and compliant robotic exo-gloves have received an increased interest over the last decade. Possible applications of these systems range from augmenting the capabilities of healthy individuals to restoring the mobility of people that suffer from paralysis or stroke. Despite the significant progress in the field, most existing solutions are still heavy and expensive, they require an external power source to operate, and they are not wearable. In this letter, we focus on the development of adaptive (underactuated and compliant), tendon-driven, wearable exo-gloves and we propose two compact, affordable, and lightweight assistive devices that provide grasping capabilities enhancement to the user. The devices are experimentally tested and their efficiency is validated using three different types of tests: First, grasping tests that involve different everyday objects, second, force exertion capability tests that assess the fingertip forces that can be exerted while using the exo-gloves, and third, motion tracking experiments focusing on the finger bending profile. The devices are able to significantly enhance the grasping capabilities of their user with a weight of 335 g and a cost of $92 for the body powered version and a weight of 562 g and a cost of $369 for the motorized exo-glove version.
Cloud Applications Consolidation through Context Information and Heuristic optimization

Resource consolidation has been proposed as an effective mechanism to save energy in data centers. Several algorithms have been developed for this purpose, which use the minimal number of servers and network switches, and power off unused equipment. Consolidation algorithms usually take into account static service constraints (Central Processing Unit - CPU, Random Access Memory – RAM, disk, bandwidth), but do not consider dynamic context information, as CPU utilization and the specific role of each Virtual Machines (VMs) in the running application (e.g., core component, backup replica, member of a pool of workers for load balancing). In this paper, we describe and evaluate a novel heuristic-based consolidation strategy that explicitly considers context information. Our approach avoids running idle VMs that are pre-provisioned for availability and redundancy purposes, hence pursuing a better linear relationship between power consumption and actual computation than other existing algorithms. We demonstrate through simulations, by comparing different heuristics, the optimal trade-off between service level and energy efficiency achieved by our approach.
Theoretical Comparison of Methods for Modeling Thin Dielectric or Conducting Sheets in the FDTD Grid

This communication compares the methods used to account for sheets thinner than the cell size of the finite-difference time-domain method. The comparison relies on the reflection and transmission of plane waves. It is shown that the Maloney and Smith method is the best. In contrast to the other methods, it provides correct reflection and transmission coefficients for any incidence angle.
An Approach to Compression of Genomic Data Based on Image File Format

The reduction in cost, sequencing time and assembly of thousands of complete genomes imposed an increasing demand for genomic data storage. Data compression approaches are viable solutions to circumvent such a demand and can reduce genetic data by more than 75% of the needed space amount of uncompressed information. Current specialized strategies implemented as compression tools for DNA sequences take advantage of the inherent characteristics of the DNA sequences, such as the small size of the required alphabet (ATCG) for data representation as well as the frequency of internal repeats in a sequence. In this work, we explore a novel perspective for lossless genomic sequence compression using standard lossless image formats. We show that DNA sequences stored in a FASTA file are converted into a matrix of bits. The matrix was then encoded in the lossless image file formats WEBP and FLIF for performance evaluation. It was performed using a dataset containing 1,547 DNA sequences from 1,163 different organisms from the kingdoms Animalia, Archaea, Bacteria, Fungi, Plant, and Protist. Our results demonstrated that the WEBP format presented the best space savings (76.15%, 0.84) when compared to the other image formats. Furthermore, when compared to specialized genomic data compression tools, WEBP space savings presented similar results with DELIMINATE (76.03%, 2.54) and MFCOMPRESS (76.97%, 1.36).
Image-Based Visualization of Classifier Decision Boundaries

Understanding how a classifier partitions a high-dimensional input space and assigns labels to the parts is an important task in machine learning. Current methods for this task mainly use color-coded sample scatterplots, which do not explicitly show the actual decision boundaries or confusion zones. We propose an image-based technique to improve such visualizations. The method samples the 2D space of a dimensionality-reduction projection and color-code relevant classifier outputs, such as the majority class label, the confusion, and the sample density, to render a dense depiction of the high-dimensional decision boundaries. Our technique is simple to implement, handles any classifier, and has only two simple-to-control free parameters. We demonstrate our proposal on several real-world high-dimensional datasets, classifiers, and two different dimensionality reduction methods.
Machine Cognition of Violence in Videos Using Novel Outlier-Resistant VLAD

Understanding highly accurate and real-time violent actions from surveillance videos is a demanding challenge. Our primary contribution of this work is divided into two parts. Firstly, we propose a computationally efficient Bag-of-Words (BoW) pipeline along with improved accuracy of violent videos classification. The novel pipeline's feature extraction stage is implemented with densely sampled Histogram of Oriented Gradients (HOG) and Histogram of Optical Flow (HOF) descriptors rather than Space-Time Interest Point (STIP) based extraction. Secondly, in encoding stage, we propose Outlier-Resistant VLAD (OR-VLAD), a novel higher order statistics-based feature encoding, to improve the original VLAD performance. In classification, efficient Linear Support Vector Machine (LSVM) is employed. The performance of the proposed pipeline is evaluated with three popular violent action datasets. On comparison, our pipeline achieved near perfect classification accuracies over three standard video datasets, outperforming most state-of-the-art approaches and having very low number of vocabulary size compared to previous BoW Models.
5G Millimeter Wave Channel Sounders, Measurements, and Models: Recent Developments and Future Challenges

Millimeter wave (mmWave) communication is a key technology for fifth generation (5G) wireless communication systems due to its tremendous bandwidth available to support high data rate transmission. This article investigates the recent developments and future challenges in 5G mmWave channel sounders, measurements, and models. Various channel sounders are comprehensively classified and compared. Channel measurements in diverse indoor and outdoor scenarios for different mmWave bands are surveyed. Meanwhile, a comparison of multiple mmWave bands, validation of mmWave massive multiple-input multiple-output (MIMO) channel properties, and measurement and modeling of human blockage effects are shown. Different channel modeling approaches including deterministic, semi-deterministic, and stochastic modeling methods are summarized. Some future research directions are also given.
Enlarging a Smartphone with AR to Create a Handheld VESAD (Virtually Extended Screen-Aligned Display)

We investigate using augmented reality to extend the screen of a smartphone beyond its physical limits with a virtual surface that is co-planar with the phone and that follows as the phone is moved. We call this extension a VESAD, or Virtually Extended Screen-Aligned Display. We illustrate and describe several ways that a VESAD could be used to complement the physical screen of a phone, and describe two novel interaction techniques: one where the user performs a quick rotation of the phone to switch the information shown in the VESAD, and another called "slide-and-hang" whereby the user can detach a VESAD and leave it hanging in mid-air, using the phone to establish the initial position and orientation of the virtual window. We also report an experiment that compared three interfaces used for an abstract classification task: the first using only a smartphone, the second using the phone for input but with a VESAD for output, and the third where the user performed input in mid-air on the VESAD (as detected by a Leap Motion). The second user interface was found to be superior in time and selection count (a metric of mistakes committed by users) and was also subjectively preferred over the other two interfaces. This demonstrates the added value of a VESAD for output over a phone's physical screen, and also demonstrates that input on the phone's screen was better than input in mid-air in our experiment.
Time Dependent Early breakdown of AIGaN/GaN Epi Stacks and Shift in SOA Boundary of HEMTs Under Fast Cyclic Transient Stress

This experimental study reports first observations of (i) SOA boundary shift in GaN HEMTs and (ii) early time to fail of vertical AIGaN/GaN Epi stack under fast changing (sub-10ns risetime) cyclic transient stress conditions for a 600V qualified commercial grade HEMT stack. It is shown that a stack qualified for 10 years lifetime under DC stress, fails faster under cyclic transient stress. Integrated electrical and mechanical stress characterization routine involving Raman/PL mapping and CL spectroscopy reveals material limited unique failure physics under transient stress condition. Failure analysis using cross-sectional TEM investigations reveal signature of different degradation and failure mechanism under transient and DC stress conditions. A failure model is proposed for failure under cyclic transient stress.
Effect of Bending on the Characteristics of a Coplanar Textile Antenna

The effect of a coplanar textile antenna bending is presented in this paper. The coplanar antenna was simulated on commercial software CST in two configurations: flattened and rolled up. The effect of bending on the characteristics of the portable antenna has been simulated. A parametric study considering the radius of curvature of the antenna as a variable parameter was performed.
Decoding Subjective Emotional Arousal during a Naturalistic VR Experience from EEG Using LSTMs

Emotional arousal (EA) denotes a heightened state of activation that has both subjective and physiological aspects. The neurophysiology of subjective EA, among other mind-brain-body phenomena, can best be tested when subjects are stimulated in a natural fashion. Immersive virtual reality (VR) enables naturalistic experimental stimulation and thus promises to increase the ecological validity of research findings i.e., how well they generalize to real-life settings. In this study, 45 participants experienced virtual rollercoaster rides while their brain activity was recorded using electroencephalography (EEG). A Long Short-Term Memory (LSTM) recurrent neural network (RNN) was then trained on the alpha-frequency (8-12 Hz) component of the EEG signal (input) and the retrospectively acquired continuous reports of subjective EA (target). With the LSTM-based model, subjective EA could be predicted significantly above chance level. This demonstrates a novel EEG-based decoding approach for subjective states of experience in naturalistic research designs using VR.
End-to-End Bone Age Assessment with Residual Learning

Bone age is a reliable metric for determining the level of biological maturity of children and adolescents. Its assessment is a crucial part of the diagnosis of a variety of pediatric syndromes that affect growth, such as endocrine disorders. The most commonly used method for bone age assessment (BAA) is still based on the comparison of the patient's hand and wrist radiograph to a bone age atlas. Such a method, however, takes considerable time, requires an expert rater, and suffers from high inter-rater variability. We present a deep-learning-based approach to estimate bone age from radiographs. It provides a fast, deterministic solution for bone-age assessment. We demonstrate the effectiveness of our method by using it to rate a set of 200 radiographs as part of a contest organized by the Radiological Society of North America. The results of this experiment have shown that our method's performance is similar to the one of a trained physician. Our system is available on-line, providing a free global service for doctors working in remote areas or in institutions with no BAA experts.
Identification and Modeling of Driver Multiloop Feedback and Preview Steering Control

Novel (semi-)automated systems are rapidly being introduced into modern road vehicles, but anticipating possibly critical human-machine interaction issues is difficult, because the human driver's behavior is as of yet still poorly understood. This paper aims to improve our understanding and models of driver steering behavior on winding roads, using Frequency-Response Function (FRF) measurements of drivers' feedforward, heading feedback, and lateral position feedback dynamics. The steering behavior data were collected in a human-in-the-loop simulator experiment, in which drivers followed the road centerline at constant forward velocity, while being perturbed laterally by wind-gust disturbances. All three measured FRFs can be captured with a multiloop, single preview-point driver model, which has only five parameters. These parameters provide unmatched understanding of – otherwise lumped – driver internal steering processes, quantifying how and what portion of the previewed centerline trajectory is used for control, and how lateral position and heading feedback are weighed. The gained insights may help to reduce driver-automation interaction issues in modern road vehicles, to quantify between-driver steering variations, adaptation and learning, and to design human-like and individualized automatic and shared steering controllers.
EPD Similarity Measure and Demons Algorithm for Object-Based Motion Estimation

Reduction of the temporal redundancies among frames, which can be achieved by the proper motion-compensated prediction, is the key to efficient video compression. Image registration is a technique, which can be exploited to find the motion between the frames. As the motion of an individual scene in a frame is varying across time, it is important to find the motion of the individual object for efficient motion-compensated prediction instead of finding the global motion in a video frame as has been used in the video coding literature. In this paper, we propose a motion estimation technique for video coding that estimates the correct motion of the individual object rather than estimating the motion of the combination of objects in the frame. This method adopts a registration technique using a new edge position difference (EPD) similarity measure to separate the region of individual objects in the frame. Then we apply either EPD-based registration or the Demons registration algorithm to estimate the true motion of each object in the frame. Experimental results show that the proposed EPD-Demons registration algorithm achieves superior motion-compensated prediction of a frame when compared to the global motion estimation-based approach.
On the Control Strategies that Humans use to Interact with Linear Systems with Output Nonlinearities

This paper examines the feedforward control strategies that humans use to interact with an unknown nonlinear system. We present results from two different experiments. In the first experiment, 11 human subjects interact with an unknown linear system. In the second experiment, 11 different human subjects interact with the same linear system but with the addition of a static output nonlinearity. We use subsystem identification to identify the feedback and feedforward controllers that model the subjects' control strategies. The identified controllers suggest the subjects use approximate feedforward plant inversion in both experiments. However, plant inversion is less precise for the nonlinear experiment.
Barrett's Esophagus Identification Using Color Co-Occurrence Matrices

In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett's Esophagus (BE) and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basis Function (SVM-RBF) and Bayesian classifier supports the context of automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73% for positive adenocarcinoma identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to 76%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.
STT-MRAM Design Technology Co-optimization for Hardware Neural Networks

The potential of embedded STT-MRAM technology for designing large-scale multiply-and-accumulation (MAC) array circuits are evaluated by comprehensive and holistic design-technology co-optimizations. After careful calibrations with experimental data, post-layout circuit simulations together with GPU-enabled massively parallel Monte Carlo evaluations are conducted to guarantee the designs at rare failure rates. With all critical device and design non-idealities included, architectural emulations are performed to examine the hardware neural network (HNN)'s accuracies and estimate system-level power, performance and area specs. Results indicate the amount of process variation, parasites and error levels to control in order to achieve a feasible solution for STT-MRAM based HNNs.
Using Grey Wolf Hunting Mechanism to Improve Brain Storm Optimization

Brain storm optimization algorithm (BSO) is recently proposed population based optimization algorithm, which is inspired by the human brainstorming process. It is designed to solve global optimization problems and has a good performance in solving large-scale multidimensional multimodal problems. However, as it largely relies on the cluster center to update the population which makes it hard to exchange information within population, it can be frequently fall into the local optimal and can't get rid of this situation easily. Grey wolf optimization (GWO) algorithm has good abilities of global search and local area avoidance, thus, GWO is studied and combined with BSO to improve its ability of global search and avoid local optimal. The experiment results on CEC'17 benchmark function indicate the feasibility of this combination.
Optimal Signaling Schemes and Capacities of Non-Coherent Correlated MISO Channels Under Per-Antenna Power Constraints

This paper investigates the optimal signaling schemes and capacities of non-coherent correlated multiple-input single-output (MISO) channels in fast Rayleigh fading. We consider both channels under per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. For per-antenna power constraint channels, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of optimal input distribution and the uniqueness of optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn–Tucker condition on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution, with a mass point located at the origin. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine the capacity gain of MISO over SISO via the phase solutions of a constrained quadratic optimization problem on a sphere, which can be obtained using a proposed penalized optimization algorithm. We also extend the results to MISO channels subject to the joint per-antenna and sum power constraints. Under this consideration, it is shown that not all per-antenna constraints are active. While the finiteness and discreteness of the optimal effective magnitude and the optimal input vector distributions still hold, the optimal phases and the optimal power allocation among the transmit antennas need to be determined simultaneously via a quadratic optimization problem under inequality constraints. These solutions can finally be used to obtain the MISO capacity gain.
Power-Efficient and Memory-Aware Approximate Hardware Design for HEVC FME Interpolator

Challenges related to mobile video coding systems are of utmost importance for researchers, especially regarding issues posed by the limited battery capacity and computational resources. The current state-of-the-art video coding standard is the High Efficiency Video Coding (HEVC), which implements computationally intensive coding tools, such as the Fractional Motion Estimation (FME). As a way to deal with real-time constraints, hardware acceleration is necessary. Furthermore, the usage of hardware accelerators allied to approximate computing is also an alternative to improve performance and save power/energy. In this work, we present a power-efficient and memory-aware approximate configurable hardware design for the HEVC FME interpolator. The proposed design is capable of realtime interpolation of UHD 4K and 8K (Ultra High Definition - 2160p@60fps and 4320@60fps) videos when synthesized using a 45nm standard-cell library, with a power dissipation ranging from 9.78mW to 50.4mW.