Miracle-dz/UniFAR-FTUs huggingface dataset:
Perfusion Monitoring by Contactless Photoplethy Smography Imaging

The flow of blood, or perfusion, of the skin can be indicative of the local but also systemic health of an individual. The noncontact Photoplethysmography (PPG) imaging is a recently emerging technology able to monitor skin perfusion. Using an off-the-shelf camera and a light source, it is possible to remotely detect the dynamic changes in blood volume beneath the skin and derive a map correlated to the blood perfusion. In this paper we empirically investigate perfusion monitoring by camera-based PPG imaging. Laser Speckle Contrast Analysis (LASCA), a well-known technique for perfusion monitoring, is used as reference. We design an experimental setup that allows simultaneous PPG imaging and laser speckle measurements. We conduct experiments with different local and temporary perfusion perturbation of the skin and show that camera-based PPG imaging can detect the perfusion changes in the tissue. Results correlate well with the laser speckle measurements, suggesting that non-contact PPG imaging is capable of providing a realistic map of skin perfusion.
Nano-structured GaAs Solar Cell Design, Simulation and Analysis for Conversion Efficiency Improvement

This paper discusses nano-structured GaAs solar cell design and analysis for conversion efficiency improvement by increasing the light transmission and absorption, reducing the light reflection. The focus of this research is to construct different type of nano-grating shaped GaAs solar cells with various nano-grating heights and pitches, to compare the simulation results and find a suitable nano-structure that can provide higher conversion efficiency. Finite difference time domain (FDTD) simulation tool is used to simulate and calculate the light reflection, transmission and absorption for different nano-grating shapes, such as, parabolic, triangular, trapezoidal and rectangular. Based on the simulation results, it has been confirmed that the light reflection of parabolic shaped nano-grating structures is higher than triangular nano-grating structures, but it is lower than rectangular and trapezoidal shaped nano-grating structures. Moreover, the simulation results confirmed that the light transmission of parabolic shaped nano-grating structures is about 62.3% having a 200-nm grating height and an 810-nm grating pitch, which is about 22% higher than the rectangular (i.e., flat) type substrates.
Synthesis of Optical Nerve Head Region of Fundus Image

The Optic Disc (OD) and Optic Cup (OC) boundaries play a critical role in the detection of glaucoma. However, very few annotated datasets are available for both OD and OC that are required for segmentation. Recently, Convolutional Neural Networks have shown significant improvements in segmentation performance. However, the full potential of CNNs is hindered by the lack of a large amount of annotated training images. To address this issue, we explore a method to generate synthetic images which can be used to augment the training data. Given the segmentation masks of OD, OC and vessels from arbitrarily different fundus images, the proposed method employs a combination of B-spline registration and GAN to generate high quality images that ensure that the vessels bend at the edge of the OC in a realistic manner. In contrast, the existing GAN based methods for fundus image synthesis fail to capture the local details and vasculature in the Optic Nerve Head (ONH) region. The utility of the proposed method in training deep networks for the challenging problem of OC segmentation is explored and an improvement in the dice score from 0.85 to 0.902 is seen with the inclusion of the synthetic images in the training set.
Optimizing Player and Viewer Amusement in Suspense Video Games

Broadcast video games need to provide amusement to both players and audience. To achieve this, one of the most consumed genres is suspense, due to the psychological effects it has on both roles. Suspense is typically achieved in video games by controlling the amount of delivered information about the location of the threat. However, previous research suggests that players need more frequent information to reach similar amusement than viewers, even at the cost of jeopardizing viewers’ engagement. In order to obtain models that maximize amusement for both interactive and passive audiences, we conducted an experiment in which a group of subjects played a suspenseful video game while another group watched it remotely. The subjects were asked to report their perceived suspense and amusement, and the data were used to obtain regression models for two common strategies to evoke suspense in video games: by alerting when the threat is approaching and by random circumstantial indications about the location of the threat. The results suggest that the optimal level is reached through randomly providing the minimal amount of information that still allows players to counteract the threat. We reckon that these results can be applied to a broad narrative media, beyond interactive games.
An Optimistic Approach for Intrusion Security in Cloud

computing is internet based technology that provides online resources on pay per use basis. This computing technology is dependent on virtualization. By registering with cloud providers customers can use services (soft and hard) on pay per use basis. Customers are getting services in the form of virtual machines, they interact with these virtual machines to do their jobs. In recent survey it is found that security is the top most concern associated with cloud services. Customers are always afraid of adopting clouds services as their data are stored out of their network boundary and administration. Researchers in cloud are trying to develop security models to protect cloud services from intrusions including internal and external. In this paper we have proposed an intrusion security model that uses the optimum cloud resources and increases the resource and service availability. KeywordsCloud, Virtual Machine, Availability, Classifier, ANN
A Synthetic User Behavior Dataset Design for Data-Driven AI-Based Personalized Wireless Networks

It is envisioned that wireless networks of the future will support personalized, fine-grained services and decisions by predicting user satisfaction in real-time using machine learning and big data analytics. Data-driven personalization will empower wireless networks to further optimize resources while maintaining user expectations of networks. In order to design, test, and validate research ideas related to wireless network personalization, acquiring data is essential. However, datasets that comprise user behavior and corresponding user satisfaction information are generally not published due to privacy and confidentiality concerns. To account for this, in this paper, we propose a synthetic dataset design methodology to generate labeled user behavior data with ground truth satisfaction values which mimic the real characteristics of real datasets. Finally, we conduct sample user satisfaction prediction experiments using several machine learning algorithms.
Facial Expression Editing with Continuous Emotion Labels

Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension represents an emotion’s valence, the other represents its degree of arousal. We demonstrate the functionality of our model with a quantitative analysis using classifier networks as well as with a qualitative analysis.
What And How Other Datasets Can Be Leveraged For Medical Imaging Classification

Applying deep learning for medical imaging classification suffers from limited labeled data, especially for those relying on biopsy proven labels. In this work we approach this challenge by leveraging other available datasets with focus on answering what dataset can be used, and how to use them optimally. An application was considered, classifying benign or malignant colonic polyps given colonoscopy narrow-band imaging (NBI). Experiments were conducted with 5 auxiliary datasets with various characteristics, and multiple training schemes involving transfer learning, multitask learning (MTL) with a DenseNet backbone. Results consistently suggest an optimal scheme of MTL initialized from ImageNet pretrain model and followed by fine tuning on the main task. An auxiliary dataset helps better if it’s not only similar in terms of imaging mechanism or fine-grained variability, but also easy to train itself. A NBI classifier trained with such scheme and auxiliary dataset improved accuracy from 62.6% (baseline without using other dataset) to 70.7% with performance on par with 5 human experts.
A Fully Convolutional Encoder–Decoder Spatial–Temporal Network for Real-Time Background Subtraction

Background subtraction is described as the task of distinguishing pixels into moving objects and the background in a frame. In this paper, we propose a fully convolutional encoder–decoder spatial–temporal network (FCESNet) to achieve real-time background subtraction. In the proposed many-to-many architecture method encoded features of consecutive frames are fed into a spatial–temporal information transmission (STIT) module to capture the spatial–temporal correlation in the frame sequence, and then a decoder is designed to output the subtraction results of all frames. A “patch-based” training method is designed to increase the practicability and flexibility of the proposed method. The experiments over CDNet2014 have shown that the proposed method could achieve state-of-the-art performance. The proposed method is proved to be able to achieve real-time background subtraction.
Secrecy Analysis and Learning-Based Optimization of Cooperative NOMA SWIPT Systems

Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source node to the far user in the presence of an eavesdropper. Moreover, we consider that all the devices use power-splitting architecture for energy harvesting and information decoding. We derive the analytical expression of intercept probability. Next, we employ deep learning based optimization to find the optimal power allocation factor. The results show the robustness and superiority of deep learning optimization over conventional iterative search algorithm.
Performance Analysis of Linear Precoding in Massive MIMO Systems With Finite-Alphabet Inputs

This paper investigates the performance of linear precoders in massive multiple-input multiple-output (MIMO) systems. Different from the existing research, in this paper, we consider a more realistic scenario, where the input signals are taken from finite-alphabet constellation sets, such as phase shift keying (PSK) or quadrature amplitude modulation (QAM), instead of Gaussian signals. The expressions are derived for the achievable mutual information with two commonly known linear precoders, i.e., zero forcing (ZF) and matched filter (MF), in the scenarios were perfect and imperfect channel state information (CSI) is known at the base station (BS). Also, the performance upper bound of mutual information with precoding techniques is analyzed. Both the theoretical analysis and simulation results show that ZF and MF precoders are near optimal when the number of antennas equipped at the BS is much larger than the number of users, which is similar to the case of Gaussian inputs. However, different from the Gaussian inputs, for the case of finite-alphabet inputs, the increase in the number of antennas does not always mean the improvement of performance; specifically, after the number of antennas at the BS, reaches a certain value, more antennas actually almost have no help for the performance improvement of mutual information, which is true whether the CSI is perfect or imperfect.
Graph-Regularized Laplace Approximation for Detecting Small Infrared Target Against Complex Backgrounds

Against complex background containing the tiny target, high-performance infrared small target detection is always treated as a difficult task. Many low-rank recovery-based methods have shown great potential, but they may suffer from high false or missing alarm when encountering the background with intricate interferences. In this paper, a novel graph-regularized Laplace low-rank approximation detecting model (GRLA) is developed for infrared dim target scenes. Initially, a non-convex Laplace low-rank regularizer instead of the nuclear norm is employed to boost the accuracy of heterogeneous background estimation. Then, to maintain the intrinsic structure between background patch-image, the graph regularization is incorporated in the detecting model. Besides, aiming at reducing the nontarget outliers, a reweighted $l_{1}$ norm with nonnegative constraint is used. Finally, the proposed model is extended to a generalized framework (G-GRLA) by replacing different non-convex rank functions. With the help of the alternating direction method of multiplier (ADMM), the solution of the proposed model is obtained by an iterative optimization scheme. The experimental results on extensive actual infrared images present the superior performance of our proposed method to compare with the state-of-the-art methods.
Simulating Depth Perception in Virtual Microscopy

3D datasets production capacity in bioimaging has widely evolved in recent years. This trend also results in a growing demand of more suitable display procedures. In this paper, we propose a new virtual microscopy approach aiming at recovering the third dimension, in order to get closer to analogue microscopes. For this purpose, we rely on multi-view autosteroscopic display with off-centered parallel virtual cameras to ensure 3D perception for a more realistic user experience. Also, this approach handles very large volume data size thanks to an out-of-core data management structure which offers interactive navigation by using complete GPU algorithms.
A perceptually balanced loss function for short-time spectral amplitude estimation

Summary form only given. We present a novel approach to audio signal enhancement based on psychoacoustic principles. Specifically, we describe a short-time spectral amplitude estimator whose form comprises a weighted sum of the minimum mean-square error solution and the observed spectral value, where the weighting factor is given by the ratio of the masked threshold and this observed value. We then explore the connection between our approach and the idea of so-called balanced loss functions in statistics, showing the former to be an instance of the latter with a very special choice of weighting factor. Lastly, we present results indicating the relative merits of our approach in both objective and subjective terms, as compared to standard minimum mean-square error estimation under the assumed model. Software and sound examples are available at http://www-sigproc.eng.cam.ac. uk//spl sim/pjw47.
Scan Chain Based Attacks and Countermeasures: A Survey

Scan chains increase the testability but decrease security. Attackers may use scan chains to launch attacks to obtain sensitive information, which poses serious security threats. The scan chain-based attacks contain two steps: 1) scan data obtaining, including mode switching obtaining method and test mode only obtaining method, and 2) scan data analysis, including data mapping analysis method and signature analysis method. To prevent these attacks, various secure scan designs have been proposed. These designs are summarized into two categories: scan chain modification and scan input/output restriction. This paper gives a survey of the scan chain-based attacks and countermeasures. The secure scan designs are summarized and compared from the aspects of security, testability, test time, and hardware overhead.
Implementation of NETCONF Protocol

The first version of SNMP (Simple Network Management Protocol) was created in 1988 by IETF (Internet Engineering Task Force) as a standard for network management. However, SNMPv1 (and its newer versions 2, 2c and 3) since its beginning has been used mainly only for the network monitoring and very rarely for the configuration of network devices (routers and switches) with its SET commands. Since the beginning of computer networking, the configuration of network devices (routers and switches) has been usually performed by using the vendors’ operating systems and their non-standard user CLI (Command Line Interface). This has proven to be a very complex and ineffective solution, especially with fast development and more complexity in the computer networking area. To solve these issues, IETF standard organization created in 2006 the NETCONF protocol in order to define the standard solution for command-oriented CLI user interface for the configuration of network devices of different vendors. Today, more than ten years after the creation of the NETCONF standard there is the question about the actual acceptance of NETCONF protocol in current networks and about the actual maturity of NETCONF standard. The scope of this paper is to compare three ways of configuring the network devices: non standard CLI, standards SNMP and NETCONF.
Rendering artistic and believable trees for cartoon animation

We present a novel approach to design artistic and believable trees in a cartoon-like style, which can be rendered by an animated camera to produce a convincing 3D-like experience. While computer assisted traditional animation is able to generate the desired effects, this approach still depends too much on the creation of many hand drawn images. Existing approaches fully depending on 3D geometries, on the other hand, give little artistic freedom and have difficulties to avoid artifacts popping up in successive frames. In order to provide good solutions to these difficulties, we present a hybrid (2.5D) framework, combining the advantages of both 2D and 3D approaches. From an underlying 3D geometry we get the necessary information to obtain an acceptable level of 3D behavior and a good frame-to-frame coherence. In the same framework, 2D artistic input is employed to obtain any desired 'look', both of the rendering and of the animation.
Multilayer Gated Recurrent Unit for Spur Gear Fault Diagnosis

As an important transmission component, the spur gearbox may cause great losses if it fails, so fault diagnosis is the key to ensure the normal operation of the equipment. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed, which uses a three-layer gated recurrent unit (GRU) to deal with the fault diagnosis of the spur gear. Due to the complexity of the spur gearbox fault, vibration measurement is carried out separately at first. Then the vibration signals are extracted from the time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. By using this model, fault features can be deeply learned layer by layer, and feature types can be identified with higher accuracy. The proposed method was applied to two spur gears (number of teeth Gear1= 53, and Gear2= 80), which were installed on the input and the output shafts of the gearbox, and there are 10 state modes in total. To evaluate the method's performance, four methods were applied to compare, which are (GRU, multilayer long short-term memory (MLSTM), long short-term memory (LSTM) and support vector machine (SVM)) respectively. The classification result of MGRU model shows that it is effective for spur gear fault diagnosis.
An acoustic echo canceler

The authors describe an experimental 1000-tap, single-chip, programmable acoustic echo canceler designed in a 3- mu m, double-metal, p-well, CMOS technology. Hardware minimization is considered from both the system and the architecture perspective. To implement more simply an echo-canceler chip with 1000 taps, floating-point representations for both data and coefficients are used to accommodate the wide dynamic range of the speech signal (6 mantissa bits for the data, 14 mantissa bits for the coefficients, and 3 exponent bits for each). Required memory is reduced from 34 kb to 26 kb. The data paths for adaptation and convolution are designed with emphasis on regularity and modularity. The echo return loss and the convergence performance of a prototype operating at 1000 taps are shown. <<ETX>>
Modeling of integrated inductors with a coplanar ground plane using the PEEC method

Today, an exhaustive library of models describing the electrical behavior of integrated passive components in the radio-frequency range is essential for the simulation and optimization of complex circuits. In this work, a physical model for planar spiral inductors is introduced, based on a circular PEEC method. A mutual inductance formula is derived to include a coplanar ground plane and extend the model to inductors with an incomplete outer turn. The numerical solution is also discussed, including an efficient inversion procedure.
A Web-Based System for Personalized Learning Path Tracking of Doctoral Students

High-quality training of doctoral students for valuable and thorough scientific research imposes high academic standards. The new academic generation of young scientists needs to be trained in creativity, critical thinking and autonomous intellectual conclusions. Doctoral students need to find suitable conditions for work, so that they can become independent researchers who at an early stage take responsibility for the scope, direction and progress of their projects. This defines the main purpose of doctoral education, which needs to be led at a high academic and methodical level. The highly acclaimed and well-popularized doctoral information system, developed at the University of Ruse, provides a wide array of possibilities for personalized learning path tracking. This enables the status of each doctoral student to be determined accurately and timely. The software allows for keeping detailed portfolios for each participant, and makes generation of various progress visualization tools, such as reports and graphs, effortless. Generating reports quickly and efficiently is an important benefit, compared to the conventional manual methods widely used in educational institutions. The paper explores the developed software system, and provides some sample analysis based on personal learning paths of doctoral students enrolled in the recent few years.