Miracle-dz/UniFAR-FTUs huggingface dataset:
Convex Optimization Based Sparse Learning Over Networks

In this paper, we consider the problem of estimating a sparse signal over a network. The main interest is to save communication resource for information exchange over the network and hence reduce processing time. With this aim, we develop a distributed learning algorithm where each node of the network uses a locally optimized convex optimization based algorithm. The nodes iteratively exchange their signal estimates over the network to refine the local estimates. The convex cost is constructed to promote sparsity as well as to include influence of estimates from the neighboring nodes. We provide a restricted isometry property (RIP)-based theoretical guarantee on the estimation quality of the proposed algorithm. Using simulations, we show that the algorithm provides competitive performance vis-a-vis a globally optimum distributed LASSO algorithm, both in convergence speed and estimation error.
Medical Images Denoising Method Based on Total Variation Regularization and Anscombe Transform

Image denoising is a vital task in medical image processing, that is helpful to improve image quality. Hence, other postprocessing tasks, such as segmentation, detection etc. can work more effectively. In this paper, we consider the denoising problem for medical images produced by X-Ray/CT imaging techniques. In other words, the images are corrupted by Poisson noise. One of well-known model for solving the denoising problem is ROF model that based on Total Variation (TV) regularization. However, the ROF model is ineffective for medical noise removal. Hence, we proposed to combine with the Anscombe Transform. The combination of TV regularization and the Anscombe transform will utilize advantages of TV regularization to treat Poisson noise more effectively. In the experiments, we test on the CT images dataset of Radiopaedia. To assess restoration quality, we compute the Peak signal-to-noise ratio and Structure similarity scores. We also compare the restoration results to the ones of the ROF model and of the modified ROF model.
Long-Term Sequelae of Bisphosph onate-Induced Osteopetrosis: Metaphyseal Osteopenia, Osteosclerosis Fragility, and No vel Bone Modeling Defects After Drug Administration Ceases

FIGURE 6: A) Computed tomography of the patient’s knees in 2007, here showing the distal femur (Top), revealed widened metaphyses with thin cortices and a paucity of trabecular bone (arrow), compared to healthy boys of similar age (Bottom). In both distal femoral metaphyses there is expanded bone with a remarkable, large, central area (arrow) featuring a paucity of trabecular bone. B) Coronal sectioning of the CT shows the modeling defects and metaphyseal osteopenia and a vertical pattern of trabeculae. The subarticular epiphyseal bone is inexplicably dense (arrow). ABSTRACT
Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement

Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.
A 16-nm FinFET Power- and Phase Noise-Scalable DCO using On-Chip Tapped Inductor

In this paper a Digitally-Controlled Oscillator (DCO) with configurable power consumption and phase-noise is presented. The DCO provides two different power/phase-noise modes while maintaining an almost constant figure-of-merit (FoM) by using a tapped inductor in the LC tank. For each mode (low-power and low-noise mode) a different DCO core is selected, which either connects to the outer taps of the tank inductor or to the inner ones. The presented design achieves a tuning range of 25.9 % with a center frequency of 4.88 GHz at a FoM of approximately 185 dBc/Hz. The DCO concept is simulated in a 16 nm FinFET CMOS process.
On the Design of Solfeggio Audio Machine Assessment System

This paper presents an approach of solfeggio audio machine assessment based on multiple audio spectrum features. In this machine assessment system, we analyze 13 spectrum audio features of solfeggio audios, and build a machine assessment model based on the scoring results of music experts, aiming to score the solfeggio audio system at the professional level based on machine learning techniques. An effective way is designed to implement the system with high generalization ability in this paper, with deploying 150 practice audio files marked with expert scores for training and testing the model. Various features are extracted to train predictors, which access the distances between practice audio files and their target audio files. In this way, these predictors can mark unknown audio files reliably. The training and testing process are described in detail, and the test results testify that our system can provide expert level scoring.
Challenges for Managing Smart Cities Initiatives: An Empirical Study

Extensive urbanisation is increasing on a global scale. The urban population is estimated to rise to 66 per cent by 2050. This significant urbanisation growth drives the consumption of resources causing resource shortages and posing significant environmental and social concerns. To manage these concerns municipal decision makers' attempt to leverage the smart city concept with collaboration between external actors as a means to maintain the prepossessed living standard in the city. The philosophy of smart cities is to see challenges as opportunities and take advantage of other trends such as digitalization. Smart cities may offer a major market opportunity that can be easily exploited by information and communication technologies providers and their telecom equipment partners. However, although the opportunity exits, capitalizing on it is not as straightforward as it seems. This paper investigates the predominant challenges in smart city initiatives from the municipal decision makers' perspective. The study is based on the findings of a survey conducted via 65 participants from various small and medium enterprises and large organisations. The results show that smart cities decision makers mainly perceive challenges with various technical and non-technical issues such as collaboration, economical, governance, social acceptance, and awareness of technology, as well as high technological risk, security and privacy of users and cyber-crimes issues.
A 3D Ellipsoid Model for Isotropic and Non-Isotropic Scatterers Co-existing Massive MIMO Channels

In this paper, we propose a three-dimensional (3D) multi-confocal ellipsoid channel model for massive multiple-input multiple-output (MIMO) communication systems, which considers the coexistence of isotropic scatterers and non-isotropic scatterers. Specifically, the inner ellipsoids are used to model the isotropic scatterers, and the outer ellipsoids are used to model the non-isotropic scatterers. First, under the spherical wavefront assumption, we develop the mathematical relationships between the angle of arrival (AOA) and the angle of departure (AOD), which is more precise than conventional approximation, and can be used to capture the channel non-stationary properties. Second, we study the correlations of the proposed model in detail, and study the impacts of important parameters such as scatterer type and scatterer distribution on it. Finally, our numerical and the simulation results demonstrate that the proposed model can capture features of realistic massive MIMO channels and can be matched well to existing literatures.
An Enhanced Direction based Multi-objective Evolutionary Algorithms using Rank Sum

This paper suggests to use rank sum technique to enhance multiobjective evolutionary algorithms (MOEAs), specifically for MOEAs using directional information. We introduce Rank based Converge Direction (RCD) and Rank based Spread Direction (RSD) for DMEA-II (DMEA-II is a popular directional based MOEA), it called DRS. In which, we use rank sum technique in convergence direction and spread direction in place of dominated-non-dominated direction and non-dominated-non-dominated direction. The performance of the proposal is validated and compared with the original algorithm NSGA-II, DMEA-II on well-known benchmark sets. With respect to result, DRS is compared favorably with its origin and competitive with others.
A design methodology of bidirectional LLC resonant converter for energy storage systems

Abstract-A design methodology of bidirectional LLC resonant converter for energy storage application is proposed. Along with the design methodology, a study on LLC resonant converter with unsymmetrical characteristics in forward and backward mode is presented. Further, a comparative study is also carried out between symmetrical and unsymmetrical bidirectional LLC resonant converter.
A Practical Proposal for State Estimation at Balanced, Radial Distribution Systems

The ever-increasing deployment of distributed resources and the opportunities offered to loads for more active roles has changed the previously unidirectional and relatively straight-forward operating profile of distribution systems (DS). DS will be required to be monitored closely for robustness and sufficient power quality. State estimation of transmission systems has consistently served as a monitoring tool, which drives system-wide control actions and, thus, ensures the operational integrity of the electric grid. An update to the classic state estimation for the case of DS is offered in this work, based on a power flow formulation for radial networks that does not require measurements or estimate of the voltage angles.
Automatic Network Configuration for Real-Time, Distributed Industrial Automation Systems

The current trend towards the Industrial Internet of Things raises new requirements regarding the connectivity of distributed automation systems. Time-sensitive Networking is a new IEEE standard fulfilling these requirements by providing determinism and real-time for convergent networks. The networks in this domain are currently configured manually, but the rising number of network nodes, more frequent reconfigurations and the combination of different scheduling mechanisms will make a manual network configuration infeasible. My proposed Ph.D. thesis strives to automate the network configuration for real-time, distributed industrial automation systems. This can be accomplished by deducing the relevant network requirements from extended domain-specific models (e.g., IEC 61499) and synthesizing network schedules according to the deduced information that can be transformed into constraints and solved by constraint-based solvers like Satisfiability Modulo Theorem solvers.
Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

This paper describes a deep neural network – hidden Markov model (DNN-HMM) human activity recognition system based on instrumented objects and studies compensation strategies to deal with object variability. The sensors, comprising an accelerometer, gyroscope, magnetometer and force-sensitive resistors (FSRs), are packaged in a coaster attached to the base of an object, here a mug. Results are presented for recognition of actions involved in manipulating a mug. Evaluations are performed using over 24 hours of data recordings containing sequences of actions, labelled without time-stamp information. We demonstrate the importance of data alignments. While the DNN-HMM system achieved error rate below 0.1% for matched train-test conditions, this increased up to 26.5% for highly mismatched conditions. The error rate averaged over all conditions was 1.4% when using multi-condition training and decreased to 0.8% by employing feature augmentation. The use of FSR feature compensation, specific to weight variability, resulted in 0.24% error rate.
An Agent-Based Process Mining Architecture for Emergent Behavior Analysis

Information systems leave a traceable digital footprint whenever an action is executed. Business process modelers capture these digital traces to understand the behavior of a system, and to extract actual run-time models of those business processes. Despite the omnipresence of such traces, most organizations face substantial differences between the process specifications and the actual run-time behavior. Analyzing and implementing the results of systems that model business processes tend, however, to be difficult due to the inherent complexity of the models. Moreover, the observed reality in the form of lower-level real-time events, as recorded in event logs, is seldom solely explainable by higher-level process models. In this paper, we propose an architecture to model system-wide behavior by combining process mining with a multi-agent system. Digital traces, in the form of event logs, are used to iteratively mine process models from which agents can learn. The approach is initially applied to a case study of a simplified job-shop factory in which automated guided vehicles (AGVs) carry out transportation tasks. Numerical experiments show that the workflow of a process mining model can be used to enhance the agent-based system, particularly, in analyzing bottlenecks and improving decision-making.
Hyper-Parameter Selection on Convolutional Dictionary Learning Through Local ℓ0,∞ Norm

Convolutional dictionary learning (CDL) is a widely used technique in many applications on the signal/image processing and computer vision fields. While many algorithms have been proposed in order to improve the computational run-time performance during the training process, a thorough analysis regarding the direct relationship between the reconstruction performance and the dictionary features (hyper-parameters), such as the filter size and filter bank’s cardinality, has not yet been presented.As arbitrarily configured dictionaries do not necessarily guarantee the best possible results during the test process, a correct selection of the hyper-parameters would be very favorable in the training and testing stages. In this context, this works aims to provide an empirical support for the choice of hyper-parameters when learning convolutional dictionaries. We perform a careful analysis of the effect of varying the dictionary’s hyper-parameters through a denoising task. Furthermore, we employ a recently proposed local $\ell_{0, \infty}$ norm as a sparsity measure in order to explore possible correlations between the sparsity induced by the learned filter bank and the reconstruction quality at test stage.
Channel Capacity of Adaptive Transmission With MRC Receiver in Correlated η – μ Fading Channels

Capacity analysis of Maximal Ratio Combining (MRC) diversity systems operating in correlated η −μ fading channels has been presented. A study on the effects of−correlation on the channel capacity has been performed. Different optimal and suboptimal adaptive systems are considered for the analysis. It has been obtained that with correlation the capacity of the system increases at higher SNR. This due to channels become good at higher SNR, and the high correlation signifies a similar kind of channel in all branches. The numerical and result sections provide a detailed study of the system.
Clustering based Cloud Radio Access Networks for 5G Smart Systems

In 5G networks, mitigating blockage effects in millimeter-wave (mmWave) signals is a major challenge to overcome. Coordinated multipoint (CoMP) transmission is one of the inference mitigation schemes that can be used in a densified cellular network. CoMP transmission is usually implemented within a cloud radio access network (C-RAN) via fronthaul links which is connected to a control unit (CU). In this paper, a radio unit clustering scheme (RCS) was proposed based on CoMP transmission to mitigate blockage effect of mmWave links. Applying a clustering scheme in C-RAN can increase the network capacity by avoiding data transmission via non-line-of-sight (NLOS) links. Based on stochastic geometry, the coverage probability of the proposed RCS is derived into a closed-form expression by modeling the position of base stations as a Poisson point process. In addition, an enhanced cooperative handover scheme is proposed to increase mobility robustness of the proposed RCS. Simulation results show that the coverage probability is enhanced as the cluster size increases in mmWave C-RANs and a radio link failure (RLF) probability during handover procedure is reduced.
On the electron mobility of strained InGaAs channel MOSFETs

A discussion over the microscopic electron mobility of strained InGaAs channels of metal-oxide-semiconductor field-effect transistors (MOSFETs) is presented. The theoretical model is validated against experimental results of strained InGaAs-on-Insulator (InGaAs-OI) MOSFETs. It accurately reproduces effective and Hall mobilities vs. charge density curves, once the calibration of interface trap density has been performed. Our findings are that 1) the interface trap distribution is decreased by strain, and this explains 2) the increase of Hall mobility with strain for low tensile strain values and 3) the insensitivity of Hall mobility to strain for higher tensile strain values. Finally, for the same reason 4) in ideal trap-free devices no strain-induced mobility enhancement is foreseen.
Electro-tactile Cues for a Haptic Multimedia Finger Motoric Learning System

In this paper, we present a new haptic multimedia finger motoric learning system. This system is using electro-tactile sensation as haptic cues for enhancing information to the user, in addition to visual and audio information. This system is used to train the users to “type” above a hand tracking device. As each character or each note showed in a screen and sounded from the sound system, the electrical sensation is delivered to the finger, indicating which finger to use to do the action. Subjects who use this system feel that electro-tactile sensation make them response more reflexive and automatic to do the task. The result shows that this system can reduce the time to learn a new finger skill about 20% faster.
On the segmentation of plantar foot thermal images with Deep Learning

Foot ulceration can be prevented by using thermal information of the plantar foot surface. Indeed, important indicators can be provided with a thermal infrared image. As part of a non-constraining acquisition protocol, these images are freehandedly taken with a smartphone equipped by a dedicated thermal camera. A total of 248 images have been obtained from an acquisition campaign composed of control and pathological subjects. Our aim is the segmentation of these plantar foot thermal images. To that end, we compare three different deep learning methods namely, the Fully Convolutional Networks (FCN), SegNet, U-Net, and the previously proposed prior shape active contour-based method. 80% of our database serves to train the 3 deep learning networks and 20% are used for the test. When applied to our data, results show that the SegNet method outperforms the three other methods with a Dice Similarity Coefficient (DSC) equal to 97.26%. This method also shows efficiency in segmenting both feet simultaneously with a DSC equal to 96.8% for a smartphone based plantar foot thermal analysis for diabetic patients.
Lemma-based Dynamic Time Warping Search for Keyword Spotting Applications in Romanian

Keyword spotting is the task of identifying words of interest in audio streams. The task has been approached in the past using HMM-based speech decoders and more recently with deep neural networks. This paper presents a two-fold approach in which the audio stream is first transcribed into words and then the words of interest are searched for in the transcription. We focus on the search part which is specifically tailored for the Romanian language. We propose two search methods for identifying words of interest in the transcriptions, namely, direct string search and dynamic time warping-based search. Both of them are applied on raw and, for better performance, on lemmatized transcriptions. The methods are evaluated in terms of F-measure. We report F-measures of up to 92% for the search algorithm solely and up to 89% for the full system: speech transcription followed by keyword spotting.
Change Prediction for Low Complexity Combined Beamforming and Acoustic Echo Cancellation

Time-variant beamforming (BF) and acoustic echo cancellation (AEC) are two techniques that are frequently employed for improving the quality of hands-free speech communication. However, the combined application of both is quite challenging as it either introduces high computational complexity or insufficient tracking. We propose a new method to improve the performance of the low-complexity beamformer first (BF-first) structure, which we call change prediction(ChaP). ChaP gathers information on several BF changes to predict the effective impulse response seen by the AEC after the next BF change. To account for uncertain data and convergence states in the predictions, reliability measures are introduced to improve ChaP in realistic scenarios.
Kinematic and Dynamic Modelling of "T-FLoW" Humanoid Robot for Ascending and Descending Stair

Walking on uneven surface with balanced manner is the key of the humanoid robot. Many essential parameters of the human walking can be captured with seven links planar biped robot. Hence, in this work kinematics, dynamic modeling and trajectory planning of a seven links planar biped robot with six joints, walking on stair with different level ground in the sagital plane. In this paper, the discussion is concerned to control kinematics for dynamic up and down stairs of the humanoid robot with Full-body kinematics based on Zero Moment Point (ZMP) to analyze stability of the bipedal robot. We define that stair configuration is already known. Dynamic of stair climbing is more unstable than dynamic walking on the ground because it needs an additional vertical motion and has different step length of walking. On this kinematics, center of the robot position of mass is adjusted by the upper body of the robot.Trajectory planning based on the behaviour human walking. The validity of the proposed method is confirmed by simulation, using geometry analysis equation and a uniques strategy for ascending and descending stairs. Furthermore Full-body kinematics for analysis has a good result on the simulation experiment with accuracy 100 percent. Position vector from Full-body kinematics will be used to complete the dynamic system model of T-Flow humanoid robot.