Miracle-dz/UniFAR-FTUs huggingface dataset:
Degrees of freedom in underspread mimo fading channels

Recent information theoretic results suggest that in richly scattered wireless environments, systems with multiple transmit and multiple receive antennas (MIMO systems) can have very large capacities. In particular, Foschini and Gans [l] considered a channel with nt transmit and n, receive antennas, with Rayleigh flat faded channel gains i.i. d. across antenna pairs, and showed that at high SNR, the capacity of this channel grows like min(nt, n,) log SNR for large SNR. This yields a min{nt, n,}-fold increase in capacity over a channel with a single transmit and a single receive antenna. The parameter min{nt,n,} can be interpreted as the number of degrees of freedom (d.0.f.) of the channel: the dimension of the space over which communication can take place. The above result assumes that the receiver can perfectly track the fading gains of the channel (so-called perfect channel state information CSI at the receiver). In high mobility applications, this may not be a reasonable assumption. Moreover, in the high SNR regime where the amount of noise is small, it is conceivable that the impact of channel uncertainty on performance is more pronounced. This leads to the question: what is the high SNR capacity of time-varying fading channels without the prior assumption of CSI? Towards this end, Lapidoth and Moser [2], building on earlier work by Taricco and Elia [3], recently showed a contrasting result: that at high SNR, the fmt order term of the capacity is log log SNR, regardless of what the number of transmit and receive antennas is. Thus, not only does capacity grow much slower than in the case with perfect CSI, but this result also suggests that without CSI, the performance gain from having multiple antennas, if any, will only appear as a second-order effect. The increase in the number of the degrees of freedom has minimal impact. Does this then suggest that the perfect CSI results are very fragile? Even though [2] explicitly incorporates the channel variation (and the associated uncertainty) in its model, this result is still asymptotic in the SNR and thus one has to be care ful in interpreting its regime of validity. In particular, since the channel variation process is k e d while the SNR is taken to infinity, it is conceivable that the 1oglogSNR growth only occurs when the noise level is much smaller than the amount of channel variation from one sample to the next. However, typical wireless channels are underspread, which means that this variation is small. Thus, one has to look at the effect of the SNR and the amount of channel variation simultaneously to get a more complete picture. In this work, we would like to put forth such a picture. Suppose the fading process for each channel gain is Gauss-Markov and define SPR to be the inverse of the one-step MMSE prediction error. For underspread channels, SPR is large. We
Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks

Ultrasound (US) beam can be focused at multiple locations to increase the lateral resolution of the resulting images. However, this improvement in resolution comes at the expense of a loss in frame rate, which is essential in many applications such as imaging moving anatomy. Herein, we propose a novel method based on Generative Adversarial Network (GAN) for achieving multi-focus line-per-line US image without a reduction in the frame rate. Results on simulated phantoms as well as real phantom experiments show that the proposed deep learning framework is able to substantially improve the resolution without sacrificing the frame rate.
A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.
Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions

Deep learning architectures have been successfully applied in video-based health monitoring, to recognize distinctive variations in the facial appearance of subjects. To detect patterns of variation linked to depressive behavior, deep neural networks (NNs) typically exploit spatial and temporal information separately by, e.g., cascading a 2D convolutional NN (CNN) with a recurrent NN (RNN), although the intrinsic spatio-temporal relationships can deteriorate. With the recent advent of 3D CNNs like the convolutional 3D (C3D) network, these spatio-temporal relationships can be modeled to improve performance. However, the accuracy of C3D networks remain an issue when applied to depression detection. In this paper, the fusion of diverse C3D predictions are proposed to improve accuracy, where spatio-temporal features are extracted from global (full-face) and local (eyes) regions of subject. This allows to increasingly focus on a local facial region that is highly relevant for analyzing depression. Additionally, the proposed network integrates 3D Global Average Pooling in order to efficiently summarize spatio-temporal features without using fully-connected layers, and thereby reduce the number of model parameters and potential over-fitting. Experimental results on the Audio Visual Emotion Challenge (AVEC 2013 and AVEC 2014) depression datasets indicates that combining the responses of global and local C3D networks achieves a higher level of accuracy than state-of-the-art systems.
Cooperative Computation Offloading and Resource Allocation for Mobile Edge Computing

With the development of wireless technologies, each smart mobile terminal (SMT) can offload computation task to mobile edge computing (MEC) server via multiple wireless access points (APs). To drop the computation offloading cost of SMT with connected multiple wireless APs, this paper investigates computation offloading problem of SMTs in a multiple wireless APs system which aims to minimize the system cost by jointly optimizing the offloading strategy, bandwidth and computation resource allocation. We then propose a game-theoretic computation offloading and resource allocation (GCRA) scheme to solve this optimization problem, which decomposes the offloading problem into two the subproblems (resource allocation and offloading decisions). Our analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme can effectively decrease the system cost compared with the existing schemes.
SNR Statistics of Indoor Mobile VLC Users with Random Device Orientation

This paper studies the signal to noise ratio (SNR) statistics of mobile users with random orientations in an indoor visible light communication (VLC) system. The considered system model consists of an access point (AP) and a mobile user device (UD) with a random orientation, which makes the instantaneous received SNR random. The distance between the AP and the UD is modeled through the random waypoint (RWP) mobility model, whereas uniform incidence angle is used to model the random orientation of the UD. Novel closed- form expressions for the probability density function and the cumulative distribution function of the instantaneous received SNR are derived. Based on this, closed-form expressions of typical performance measures, such as the outage rate probability (ORP) and the average symbol error probability (SEP) are also derived. The accuracy of the derived expressions are validated through extensive simulations and the effect of mobility and random orientation of the UD on the performance of the system are then evaluated. Our results show that the UD's random orientation degrades the system performance.
D2M: Mobility-Aware Dynamic Data Multicasting in Software-Defined Data Center Networks

In this paper, we study the problem of mobility-aware dynamic data multicasting in software-defined Data Center Networks (DCNs) in the presence of Internet of Things (IoT) devices. In the existing literature, researchers studied that due to unbalanced traffic distribution in DCN, the network throughput and delay degrade significantly. Moreover, with the integration of heterogeneous IoT devices, the difficulty in achieving balanced traffic distribution increases. Hence, there is a need to design an efficient data multicasting scheme in DCN, while integrating the software-defined network architecture. In this work, we propose a Dynamic Data Multicasting scheme, named D2M, using single leader multiple follower Stackelberg game for ensuring high utilization of network capacity and efficient load balancing. In D2M, the controller acts as the leader, and the switches act as the followers. The leader decides the distribution of flow-rules among the switches for ensuring efficient load-balancing. We represent bandwidth distribution as a pseudo-Cournot competition, where each follower decides the optimal bandwidth to be allocated for each flow. The existence of at least one Nash equilibrium using D2M is ensured. Through simulation, we observed that using D2M, the network throughput increased by 6.13–95.32% than using existing schemes, while ensuring 21.32–99.29% reduction in delay.
Influence of Multi-Pulse Rectifier on Power Quality in an Industrial Environment

The power quality in industry is actually of a greater importance due to the presence of sensitive loads like computers, protection relays, measurement and other electronic devices operated near the high-power nonlinear loads. One of the most important aspects of this quality is related to the harmonic distortion of voltage and current waves with the well-known negative consequences on the industrial power system components and its operating efficiency. This paper analyses the effects on the quality of electrical energy of the multipulse rectifiers used to supply some electrical high-power drives as main components of the production process, involved in the driving of the rolls of the scraper from a hot strip mill. The measurement results show clearly the advantage of the presence of the 6-pulse and 12-pulse rectifiers during the fast transient conditions of slab lamination.
Interactive home healthcare system with integrated voice assistant

Voice assistants are making their way in home healthcare, improving patient care by increasing efficiency and presenting a new experience by offering a wide range of tools and capabilities. Patients can access medical information, monitor and analyze health issues and communicate with ease with the help of useful applications and services. This is especially beneficial for the elderly patients. This paper showcases the patient-focused voice and web services developed for Amazons Alexa and Google Assistant and pinpoint the benefits of this technology in healthcare in the comfort of the patients home.
3D Structure Surface Modelling From Volumetric CT Images

Volume rendering is widely applied in 3D medical images for classifying and visualizing different organs. However, volume rendering can visualize organs for observation, it generally cannot provide 3D surface models for the organs, which are needed in quantitative analysis and 3D printing of organs. In this paper, we propose an effective and novel surface modelling method that can generate 3D surface models for the organs visualized by volume rendering, based on their incomplete classification information implicitly contained in volume rendering. Firstly, our method uses the organ classification information of volume rendering as the initial segmentation to extract topological structure of the organ; and then, a modified graph-cut algorithm is designed to segment complete region of the organ; finally, to obtain a smooth and accurate surface model, we introduce the geodesic active contour method. Our method demonstrates its effectiveness in rigid bones and elastic organs such as spleen and kidney which are of different shapes and sizes based on CT images.
Will Pumped-Hydro Energy Storage Business Models Work for Peak Shaving Use?

The desire for improved environmental sustainability continues to reduce reliance on fossil carbon based energy usage, while increasing installation of renewable energy sources in Australia and in the Pacific region. Pumped hydro energy storage systems (PHESS) are a technology that can be used to improve network stability where high penetration of renewable energy sources exist. However current participation in the Australian Electricity Market by PHESS does not match international participation in other electricity markets. This paper reviews international CAPEX costs and cos oft recharge energy for PHESS to better understand its potential use in the Australia national energy market. Deep sea PHESS as a conceptual emerging technology is also compared. PHESS and DSPHESS modelling in Australia and Fiji are developed to assess the final cost of energy from these sources in relation to peak shaving application. Part of each model is a sensitivity study of final cost of electricity produced from PHESS systems based on CAPEX costs and recharge energy costs.
Power Quality Improvements in Single-Phase Grid-Connected Photovoltaic Systems using a Nonlinear Adaptive Controller

This paper presents nonlinear adaptive control scheme for improving the power quality in single-phase grid-connected photovoltaic (PV) systems. For this purpose, an LCL filter is used at the output-side of the voltage source inverter (VSI) in the single grid-connected PV system. In this paper, the proposed adaptive controller is designed for the VSI by considering the average model of the VSI while considering only the dynamics of the LCL filter. The proposed controller enhances the power quality of the system by maintaining the values of total harmonic distortions (THDs) at lower levels as it estimates the parameters of the LCL filter using adaptation laws. These adaption laws are obtained in such a way that it is not essential to know exact parameters of the LCL filter. The parameter estimation capabilities of the proposed scheme are ensured through the tracking performances of different state variables associated with the dynamics of the LCL filter. The convergences of tracking errors are ensured during the different stages of the controller design process using the Lyapunov stability analysis technique and the stability of the overall system is also analyzed in this paper. The improvements in the power quality, using the proposed nonlinear adaptive control scheme, are demonstrated on a 3.5 kW single-phase grid-connected PV system by considering different operating conditions. The results are mainly discussed in terms of THDs in the current injected into the grid and also compared with a sliding mode controller (SMC).
A Secure and Low-overhead Active IC Metering Scheme

In the horizontal semiconductor business mode, the foundry can tamper or overbuild the integrated circuits (ICs) while the owner of the IC knows nothing about it. IC metering technique has been proposed to solve this problem. In this paper, we propose a new external active IC metering method. The physical unclonable function (PUF) is used to generate a unique key for each chip. We first propose to modify the finite state machine (FSM) so that the PUF-based key can be securely retrieved from the FSM. With regards to overhead and security, this retrieval scheme takes advantage over the existing scheme with encryption module to safeguard the PUF-based key. The experimental results show that the proposed retrieval scheme incurs negligibly low overhead to the original FSM. Also, the overhead due to the metering method accounts for small percentage to the overall design while the proposed scheme can resist typical attacks.
AutoRate: How attentive is the driver?

Driver inattention is one of the leading causes of vehicle crashes and incidents worldwide. Driver inattention includes driver fatigue leading to drowsiness and driver distraction, say due to use of cellphone or rubbernecking, all of which leads to a lack of situational awareness. Hitherto, techniques presented to monitor driver attention evaluated factors such as fatigue and distraction independently. However, in order to develop a robust driver attention monitoring system all the factors affecting driver’s attention needs to be analyzed holistically. In this paper, we present AutoRate, a system that leverages front camera of a windshield-mounted smartphone to monitor driver’s attention by combining several features. We derive a driver attention rating by fusing spatio-temporal features based on the driver state and behavior such as head pose, eye gaze, eye closure, yawns, use of cellphones, etc.We perform extensive evaluation of AutoRateon real-world driving data and also data from controlled, static vehicle settings with 30 drivers in a large city. We compare AutoRate’s automatically-generated rating with the scores given by 5 human annotators. Further, we compute the agreement between AutoRate’s rating and human annotator rating using kappa coefficient. AutoRate’s automatically-generated rating has an overall agreement of 0.87 with the ratings provided by 5 human annotators on the static dataset.
Valence and Arousal Estimation In-The-Wild with Tensor Methods

While it is relatively easy and natural for humans to detect and interpret non-verbal cues, it is a hard task for computer systems. Automatic recognition of emotions has been the subject of extensive studies in the past decade, but despite the various methods that have been implemented, the problem remains challenging. In particular, most existing works focus on predicting a set discrete stereotypical emotion categories. We are instead interested in predicting continuous values of valence and arousal, which are able to model accurately a broader range of spontaneous emotions. Moreover, as opposed to much of the prior work that focused on controlled (laboratory) conditions, we are interested in analysis in naturalist (in-the-wild) conditions. To do so, we propose to leverage the structure in the data using tensor methods. In addition to preserving the structure, these have the potential to also reduce the total number of parameters in the models, thus improving the computational performance. We first consider a model with analytic solution in the form of a Tucker Tensor Regression. We then investigate a deep, gradient based method, namely Tensor Regression Networks. We perform thorough experiments on two publicly available databases, AFEW-VA and SEWA, for facial affect estimation in-the-wild, in terms of valence and arousal levels. Experimental results demonstrate that tensor-based methods successfully leverage the structure in the data and on average outperform baseline methods.
Radio Resource Scheduling for 5G NR via Deep Deterministic Policy Gradient

The fifth generation (5G) wireless system plays a crucial role to realize future network applications with diverse services requirements. The 3rd Generation Partnership Project (3GPP) proposed 5G New Radio (NR) specifications with significantly greater flexibility on configurations and procedures to facilitate a more efficient and agile radio access network (RAN). At the same time, the complexity of resource management increases, and the advantage of machine learning techniques are worth studying. In this article, we investigate the radio resource scheduling issue in the 5G RAN. Through a modularized deep deterministic policy gradient (DDPG) architecture and specifically defined action as a combination of scheduling algorithms Through specifically defined action as a combination of scheduling algorithms, the proposed method is efficient to train and performing well. Favorable results are observed compared with conventional scheduling algorithms. The proposed architecture applies to other radio resource management problems with similar characteristic.
An Effective Suppression Method for 802.11 Transmission with Experimental Verification

Extensive, unmanaged IEEE 802.11 deployments may become sources of interference in emergency communications scenarios. In this paper, we consider the goal of protecting public safety by suppressing the transmission of 802.11. In order to develop a suppression method that can be effective in practice, we propose a new spoofing method which utilizes the channel detection mechanism in physical layer of 802.11 protocol. Besides, numerous experiments are conducted for commercial off-the-shelf (COTS) 802.11 devices (i.e. WiFi). Based on the experiment results, we finally present a reasonable and efficient method for preventing WiFi transmission. Further experiments and analysis show that the proposed method is superior to the state of the art suppression methods in terms of energy consumption and applicability to all 802.11 devices.
Solution of Large Engineering Problems on Parallel Computers

This contribution reviews utilization of parallel computers in various engineering problems. First, parallel computers are classified and a very short review of the most powerful computers is presented. Then, engineering problems are sorted with respect to possible parallelization.Finally, two examples of different utilization of parallel computers are described. Domaindecomposition method based on the Schur complement method is the first example and a multi-scale analysis of masonry structure is the second one.
Bloom Filter Based Efficient Caching Scheme for Content Distribution in Vehicular Networks

With the progression of Internet of Things (IoT), the number of connected vehicles in smart cities have shown an exponential growth. In order to enable fast data transfer, these vehicles communicate with each other and the roadside units. However, the data generated by these connected vehicles has overwhelming increased in recent times, which in turn overloads the traditional host-centric approach. For this reason, vehicular content-centric networking (VCCN) has emerged as a revolution, which provides efficient content distribution among vehicles. However, cache management is one of the most important aspects, which needs to be handled effectively. Therefore, in this paper, a bloom filter-based cache management scheme is proposed to enhance the performance of data transfer between smart connected vehicles. This algorithm uses the functionality of cache in vehicles to enable cooperative content distribution. The evaluation of bloom filter-based cache management scheme shows that the bloom filter reduces the search time in contrast to other data structures, which in turn increases the efficiency of retrieval from cache. The simulation results show that the proposed scheme achieves faster retrieval time and an efficient allotment system for helper vehicles in a smart city environment.