Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Large Scale Antenna System Based on Lee Channel

With the rapid development of mobile communication technology, in order to solve the problem that the number of antennas is increasing in a large scale, it is difficult to completely arrange and interfere with each other in the limited physical space. This paper is based on the Lee channel model to simulate large-scale MIMO technology. Through simulation, it is found that the channel capacity of the system is linearly related to the signal-to-noise ratio. As the signal-to-noise ratio increases, the channel capacity of the system increases monotonously. The correlation of the system becomes slower as the initial angle decreases, and the overall trend decreases with the increase of antenna number.
BAW Filters for 5G Bands

The number of frequency bands requiring BAW filters is expected to grow significantly with the launch of 5G mobile applications. BAW is in particular well suited to address the new radio bands below 6 GHz also referred to as nr-1. RF integration is the only path forward to achieve full connectivity in LTE + 5G wireless due to the complexity of the antenna systems and the resulting coexistence challenges. The article gives an overview and describes recent trends regarding BAW for high frequency bands, wide bandwidth filters, miniaturization and thermal management. Evolution of RF content and the challenges of developing complex RF modules are discussed.
Outage Probability Analysis of Mixed RF-FSO System Influenced by Fisher-Snedecor Fading and Gamma-Gamma Atmospheric Turbulence

In this paper, we investigate a dual-hop relaying system, composed of radio frequency (RF) and free-space optical (FSO) link. Decode-and-forward (DF) relay is employed to integrate the first RF link and the second line-of-sight FSO links. The RF channel is assumed to be subject to recently proposed Fisher-Snedecor fading model, which was shown to be convenient for modeling in realistic wireless communication scenarios. The FSO channel is affected by Gamma-Gamma distributed atmospheric turbulence. Expression for the outage probability is derived and utilized to present numerical results. Based on presented results, the effects of various RF and FSO channels parameters on the overall system performance are examined and discussed.
Graph Spectral Filtering for Network Simplification

Visualization is an important tool in the analysis and understanding of networks and their content. However, visualization tools face major challenges when dealing with large networks, mainly due to visual clutter. In this context, network simplification has been a main alternative to handle massive networks, reducing complexity while preserving relevant patterns of the network structure and content. In this paper we propose a methodology that rely on Graph Signal Processing theory to filter multivariate data associated to network nodes, assisting and enhancing network simplification and visualization tasks. The simplification process takes into account both topological and multivariate data associated to network nodes to create a hierarchical representation of the network. The effectiveness of the proposed methodology is assessed through a comprehensive set of quantitative evaluation and comparisons, which gauge the impact of the proposed filtering process in the simplification and visualization tasks.
Hybrid Multiobjective Differential Evolution Based on Positions of Individuals in Multiobjective Optimization

This paper proposes a hybrid multiobjective differential evolution (HMODE) framework based on positions of individuals in Pareto frontier (PF) to solve multiobjective optimization problem. Firstly, the hybrid multiobjective evolutionary algorithm (HMOEA) in HMODE is designed as the global search strategy to explore the entire solution space. HMOEA uses Pareto dominating and dominated relationship-based fitness function (PDDR-FF) to distinguish the nondominated and dominated individuals. The location information of individual can be determined by its PDDR-FF value. The elitist maintenance strategy based on PDDR-FF and simple selection strategy guarantee the capability of converging to the multiple directions of PF quickly and distributing along the PF uniformly. Secondly, differential evolution (DE) is combined with HMOEA as the local search to enhance the convergence and distribution performances on the elite population derived from HMOEA. Different PDDR-FF values of individuals clearly reflect their dominance relationships and position information in the PF. DE uses the position information of alternative individuals to finely tune the search directions to multiple areas of PF. Numerical comparisons indicate that the efficacy of HMODE outperforms HMOEA in convergence and distribution performances.
Nearest Centroid Neighbor Based Sparse Representation Classification for Finger Vein Recognition

In this paper, an efficient finger vein recognition algorithm based on the combination of the nearest centroid neighbor and sparse representation classification techniques (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NCN-SRC) is presented. The previously proposed recognition algorithms are mainly based on distance computation. In the proposed method, the distance, as well as the spatial distribution, are considered to achieve a better recognition rate. The proposed method consists of two stages: first, the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> nearest neighbors of the test sample are selected based on the nearest centroid neighbor, and then, in the second stage, based on the selected number of closest nearest centroid neighbors (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>), the test sample is classified by sparse representation. Findings from the proposed method <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NCN-SRC demonstrated an increased recognition rate. This improvement can be attributed to the selection of the train samples, where the train samples are selected by considering the spatial and distance distribution. In addition, the complexity of SRC is reduced by reducing the number of train samples for the classification of the test sample by sparse representation and the processing speed of the proposed algorithm is significantly improved in comparison with the conventional SRC which is due to the reduced number of training samples. It can be concluded that the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NCN-SRC classification method is efficient for finger vein recognition. An increase in the recognition rate of <italic>3.35%</italic>, <italic>9.07%</italic>, <italic>20.23%</italic>, and <italic>0.81%</italic> is obtained for the proposed <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NCN-SRC method in comparison with the conventional SRC for the four tested public finger vein databases.
Parametric Study of Different Shapes-Slotted Substrate Integrated Waveguide for Wideband Applications

On the way to design Substrate Integrated Waveguide Bandpass Filter (SIW-BPF) for wideband applications a parametric study of the slot geometry etched on a SIW structure is the preliminary step to design such filter. Three different structures, namely, H-Slotted, T- Slotted, and U-Slotted SIW is investigated in order to examine the effect of altering different geometrical parameter of the unit on its response. H-Slotted Electromagnetic Band Gap (EBG) shows the highest Fractional Band Width (FBW), which is about 90% compared to the others, consequently it could be used to design a wideband SIW-BPF.
An Optimal Approach for Minimizing Aperiodic Response Times in Real-Time Energy Harvesting Systems

This paper explores an energy harvesting-based approach for a jointly execution of hard periodic tasks mixed with occurring soft aperiodic tasks in battery-powered embedded devices. Providing an optimal solution to this scheduling issue is challenging. Accordingly, we propose a new aperiodic task scheduling algorithm in real-time energy harvesting (RTEH) systems that targets at minimizing the response times of aperiodic tasks without compromising the schedulability of periodic tasks. Based on the slack stealing mechanism, the proposed algorithm called Slack Stealing with energy Preserving (SSP) profits whenever possible from available extra processing time and available extra energy so as to service the aperiodic tasks as soon as possible. Comparing to other algorithms, SSP achieves better performance in terms of the aperiodic responsiveness under various energy profiles and settings.
An Experimental Assessment of Direct Torque Control and Model Predictive Control Methods for Induction Machine Drive

Finite-State Model Predictive Control methods (FSMPC) have been presented recently in the field of electrical drive and power electronics as an alternative to the conventional strategies. This paper presents a comparative evaluation between Direct Torque Control (DTC) and two finite-state model predictive control strategies applied to induction motor drive. Both DTC and MPC are nonlinear control techniques which dispense with the use of modulation unit (i.e. pulse width modulator (PWM) or space vector modulator (SVM)). DTC can provide good decoupled flux and torque control using pair of hysteresis comparators and lookup switching table for voltage vectors selection. In contrast with the model predictive control which includes the inverter model in control design. The optimal selection of inverter switching states minimizes the error between references and the predicted values of control variables by the optimization of a cost function. The effectiveness of applied algorithms is investigated by an experimental implementation using real-time interface (RTI) based on dSpace 1104.
Optimal Trajectory Planning of Hybrid Systems by Efficient MIQP Encoding

This paper proposes an efficient encoding for Mixed Integer Quadratic Programming (MIQP) problems in optimally controlling constrained hybrid systems from an initial state into a target region over a finite time horizon. The set of admissible trajectories given the system semantics is formulated by tailored constraints involving binary variables to encode the transition dynamics. A subset of these constraints establish a phase sequencing, which reduces the number of possible value combinations for the binary variables significantly, resulting in lower computation times for solving the MIQP problem. An illustrating example demonstrates the low computation times.
Enhancing the Effectiveness of Local Descriptor Based Image Matching

Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.
A Study of SSVEP Responses in Case of Overt and Covert Visual Attention with Different View Angles

Standard automated perimetry is a common visual field test in clinical practices. But the test effectiveness relies on responses from subject and technician experience in operating the test equipment. Therefore, it calls for a more objective way of measuring visual field; as such we consider SSVEP as potential suitable technique. SSVEP is extensively studied in the context of a brain-computer interface, where successful SSVEP detection relies on fovea vision. But peripheral vision is more critical in assessing the effective field of view in Glaucoma patients. So this study investigates how SSVEP responses exhibit with different view angles and subject's visual attention in peripheral vision. We designed an experiment with single flickering stimulus at three view angles horizontally. Subject performed overt, covert and no visual attention at each stimulus position, while EEG and eye tracking data are recorded simultaneously. We used spectral power amplitude ratio and maximum canonical correlation coefficients to evaluate SSVEP responses. By applying 1-way and 2-way ANOVA tests, there is no statistical significant difference among SSVEP responses when subject paid overt or covert visual attention with different view angles. This might suggest that SSVEP can be a usable approach to measure visual fields when subject oriented with covert visual attention. So reliable SSVEP responses highly depend on the choice of stimulus frequency but do not depend significantly on different visual attention and view angles.
Coefficient of Throughput Variation as Indication of Playback Freezes in Streamed Omnidirectional Videos

A large portion of today’s network traffic consists of streamed video of large variety, such as films, television shows, live-streamed games and recently omnidirectional videos. A common way of delivering video is by using Dynamic Adaptive Streaming over HTTP (DASH), or recently with encrypted HTTPS. Encrypted video streams disable the use of Quality of Service (QoS) systems that rely on knowledge of application-dependent data, such as video resolution and bit-rate. This could make it difficult for a party providing bandwidth to efficiently allocate resources and estimate customer satisfaction. An application-independent way of measuring video stream quality could be of interest for such a party.In this paper, we investigate encrypted streaming of omnidirectional video via YouTube to a smartphone in a Google Cardboard VR-headset. We monitored such sessions, delivered via both WiFi and mobile networks, at different times of day, implying different levels of congestion, and characterised the network traffic by using the Coefficient of Throughput Variation (CoTV) as statistic. We observe that this statistic shows to be able to indicate whether a stream is stable or unstable, in terms of potential video playback freezes, when the DASH delivery strategy is used.
Context-Aware Smartphone Applications Based on Generic Context Supervision Services

Mobile phones have the abilities to use contextual information from a variety of different sources. Primarily, the contextual information can be collected from the history of mobile device usage, personal user information and sensor readings. The generalization of such collected information and data, as well as their processing, can lead to numerous benefits, where the most notable is the software reusability and maintainability. The more the types of data are similar it means that generalization is more realistic. This paper describes principle of development of a context-aware applications based on a generic solution for contextual information control for Android operating system mobile based devices. In this paper the focus is on creating supervisor framework which provides higher level of information for clients based on basic information conditions and their combinations. It was developed, implemented and described one practical Android based smartphone application that provides to the user personalized view on his/her meetings and tasks within the organization.
24 GHz FMCW Radar System for Real-Time Hand Gesture Recognition Using LSTM

A 24 GHz frequency modulated continuous wave radar system to recognize human's hand gestures is implemented, which uses commercial off-the-shelf RF front-end IC with one transmitter and four receivers. Planar patch array antennas, signal conditioning circuits and interconnections to a PC are designed for the system. Range-Doppler maps for four receiver channels are obtained with saw-tooth chirping signals transmitted to detect hand gestures. The radar system shows real-time highly accurate gesture recognition. Long-short term memory recurrent neural network as a supervised machine learning technique is used. Seven kinds of hand gestures are recognized within 0.4 m and $\pm 30^{\text{o}}$ from the center of the transmitted antenna with above 91 % accuracy.
RISF: Recursive Iterative Spanning Forest for Superpixel Segmentation

Methods for superpixel segmentation have become very popular in computer vision. Recently, a graph-based framework named ISF (Iterative Spanning Forest) was proposed to obtain connected superpixels (supervoxels in 3D) based on multiple executions of the Image Foresting Transform (IFT) algorithm from a given choice of four components: a seed sampling strategy, an adjacency relation, a connectivity function, and a seed recomputation procedure. In this paper, we extend ISF to introduce a unique characteristic among superpixel segmentation methods. Using the new framework, termed as Recursive Iterative Spanning Forest (RISF), one can recursively generate multiple segmentation scales on region adjacency graphs (i.e., a hierarchy of superpixels) without sacrificing the efficiency and effectiveness of ISF. In addition to a hierarchical segmentation, RISF allows a more effective geodesic seed sampling strategy, with no negative impact in the efficiency of the method. For a fixed number of scales using 2D and 3D image datasets, we show that RISF can consistently outperform the most competitive ISF-based methods.
Design Features of High-Speed CMOS Differential Difference Operational Amplifiers at Low Static Current Consumption

In article interrelation of the linear operation range of a CMOS differential difference operational amplifier (DDOA) with the main parameter of the pass characteristics of its input differential stages (DS), the limiting voltage (Vlim), is considered. The results of mathematical and comparative computer modeling of the dependence of Vlimtypical CMOS DS on the channel width of transistors at different static currents for three CMOS technological foundry processes (0.25 um SiGe CMOS, 0.35um SiGe CMOS and 0.6 um Si CMOS) are presented. Recommendations are given for increasing the maximum slew rate of the output voltage (SR) of the DDOA, which are based on the introduction into each input DS of a differentiating circuit of transient correction.
A Novel Method for Detecting Telecom Fraud User

Telecom fraud has been a worldwide problem with substantial annual revenue losses for many subscribers and service provider companies. To achieve effective and inexpensive detection for telecom fraud users, we propose an effective and applicable fraud user detection method based on user's Call Detail Record (CDR). The proposed method consists of two modules, namely machine learning module and template detection module. In the machine learning module, a Support Vector Machine (SVM) algorithm based on supervised learning is used to classify users using summary characteristics. In the template detection module, a Finite State Machine (FSM) based on fraud user's behavior is used to filter suspicious users, which generated in the machine learning module. After the two modules, fraud users can be detected. We implement our approach and evaluate it on a real-world dataset. The experiments show that the method can achieve high detection accuracy of 93.56%, which demonstrate that the proposed method has more excellent performance in comparison with the state-of-the-art approaches.
Power-Efficient ReRAM-Aware CNN Model Generation

This is the first work to propose generation of the network model of a convolutional neural network (CNN) specifically for power-efficient implementation using the ReRAM technology. State-of-the-art in this area is based on implementation of an already fixed CNN model. It uses parallel crossbar structures to achieve a desired precision quantization of the edge weights using the limited precision provided by individual ReRAM devices. In contrast, in this work we keep the ReRAM crossbar structure in mind during model generation, and target altering a base CNN model such that the resulting implementation will be more power efficient. This is by means of eliminating the parallel crossbars as much as possible, thus reducing the number of required analog-to-digital and digital-to-analog converters which are dominant sources of power consumption. We propose four architectural techniques which are applicable to convolutional and fully-connected layers in a CNN and alter the network model to work with lower precision of the weights with negligible loss in accuracy. In our experiments, we achieve at least 50% power savings with almost no loss in accuracy for popular CNNs compared to ReRAM implementation of the base model.
Practical Implemetation of Energy Management Algorithm on a Microgrid System

—Research in Microgrid has taken recently more attentions in the context of solving the conventional centralized power network constraints. Combining information and power technologies, the efficiency and reliability of the power network have been improved dramatically. As an additional advantage, including renewable energy sources and storage in to form distributed energy systems has proven its aptitude to solved some economic and technical penalties appearing in classical centralized systems. However, appropriate energy management algorithms in hybrid microgrids are primordial to reach the objectives mentioned previously, for which, this paper has given a significant contribution. This work proposes a practical and efficient solution to implement a real-time energy management policy on a microgrid prototype based on commercial products. This method is implemented and tested successfully to manage the power flow between 6.3 KWp photovoltaic system; 4.5 KW lead-acid batteries and 3.6 KW programmable loads. The objective is to maximize the use of renewable energy while driving efficiently the storage system. Another target summarized in operating the microgrid in island mode as much as possible to improve its reliability.
On Exploiting Per-Pixel Motion Conflicts to Extract Secondary Motions

Ubiquitous Augmented Reality requires robust localization in complex daily environments. The combination of camera and Inertial Mersurement Unit (IMU) has shown promising results for robust localization due to the complementary characteristics of the visual and inertial modalities. However, there exists many cases where the measurements from visual and inertial modalities do not provide a single consistent motion estimate thus causing disagreement on the estimated motion. Limited literature has addressed this problem associated with sensor fusion for localization. Since the disagreement is not a result of measurement noises, existing outlier rejection techniques are not suitable to address this problem. In this paper, we propose a novel approach to handle the disagreement as motion conflict with two key components. The first one is a generalized Hidden Markov Model (HMM) that formulates the tracking and management of the primary motion and the secondary motion as a single estimation problem. The second component is an epipolar constrained Deep Neural Network that generates a per-pixel motion conflict probability map. Experimental evaluations demonstrate significant improvement to the tracking accuracy in cases of strong motion conflict compared to previous state-of-the-art algorithms for localization. Moreover, as a consequence of motion tracking on the secondary maps, our solution enables augmentation of virtual content attached to secondary motions, which brings us one step closer to Ubiquitous Augmented Reality.