Miracle-dz/UniFAR-FTUs huggingface dataset:
Music Source Separation with Generative Adversarial Network and Waveform Averaging

The task of music source separation is to extract a target sound from mixed sound. A popular approach for this task uses a DNN which learns the relationship of the spectrum of mixed sound and one of separated sound. However, many DNN algorithms does not consider the clearness of the output sound, this tends to produce artifact in the output spectrum. We adopt a generative adversarial network (GAN) to improve the clearness of the separated sound. In addition, we propose data augmentation by pitch-shift. The performance of DNN strongly depends on the quantity of the dataset for train. In other words, the limited kinds of the training datasets gives poor knowledge for the unknown sound sources. Learning the pitch-shifted signal can compensate the kinds of training set and makes the network robust to estimate the sound spectrum with various pitches. Furthermore, we process the pitch-shifted signals and average them to reduce artifacts. This proposal is based on the idea that network once learned can also separate pitch-shifted sound sources not only original one. Compared with the conventional method, our method achieves to obtain well-separated signal with smaller artifacts.
A Fast BP Algorithm for Bistatic Spotlight SAR Based on Spectrum Compression

For bistatic spotlight synthetic aperture radar (SAR) with parallel trajectory, this paper investigates a fast back-projection (BP) imaging algorithm based on spectrum compression. The long synthetic aperture is split into several short subapertures (SAs), and each of them constructs a SA image with coarse azimuth resolution in a unified rectangular coordinate system by BP integral. Then, by applying the spectrum compression technique introduced in the paper, the azimuth spectrum of the SA image can be compressed greatly and, as such, the images can be coherently accumulated after azimuth up-sampling and spectrum recovery. Since there is no need for data interpolation in the image fusion, the proposed algorithm can achieve high precision as the original BP algorithm does but with higher computational efficiency. Simulation experiments are presented to validate the effectiveness of the method.
Variational Bayesian Inference based Soft-Symbol Decoding for Uplink Massive MIMO Systems with Low Resolution ADCs

In this paper, we present an algorithm to obtain the posterior beliefs of the transmitted bits in an uplink coded massive multiple-input-multiple-output (MIMO) wireless communication system with low resolution analog-to-digital-converters (ADC) at the base station (BS). The nonlinearities introduced by low resolution ADCs necessitates a new multiuser detection approach, for which propose a variational Bayes’ inference framework. Further, in coded communications, it is more important to obtain soft symbol estimates for the data bits, rather than hard decisions on the data symbols. We approximate the exact posterior distribution with a fully factorized distribution used in mean field theory in statistical physics, and find its parameters such that the evidence lower bound (ELBO) is maximized. These parameters are used to obtain the posterior beliefs of the transmitted bits in an iterative manner. The resulting algorithm, named the quantized variational Bayesian soft symbol decoding (QVBSSD) is computationally inexpensive and easy to implement. The output posterior beliefs are input to a soft-input-hard-output channel decoder, which gives the decoded bits. We empirically illustrate the bit error rate (BER) of the QVBSSD algorithm, and show its superiority compared to the unquantized MMSE based detection.
Low Multilinear Rank Updating

A low multilinear rank approximation (LMLRA) of a tensor is often used to compress a large tensor into a much more compact form, while still maintaining most of the information in the tensor. This can be achieved by only storing the principal subspaces in every mode and dropping the other singular vectors. These subspaces are then combined using a core tensor of (much) smaller dimensions than the original tensor. When the tensor is non-static, for example when new tensor slices are regularly added in one mode, one can expect that the LMLRA of the tensor changes only slightly and an approximation of the new tensor can be derived from the previous one. In this paper, a method is derived to track the different subspaces of a tensor by generalizing the rank-adaptive SURV method of Zhou et al. for tracking matrix subspaces to higher orders. Using a sequential truncation approach, this leads to efficient and accurate updates of the LMLRA.
A Novel Multi-Vehicle-Born Bistatic SAR System

Multi-vehicle cooperation detection and strike is playing more and more important roles in complex battlefield environment nowadays. In this paper, a novel multi-vehicle-born bistatic SAR system is introduced. This novel system has advantages of flexibility, concealment and high-resolution high-SNR forward-looking imaging and detection. Scheme of this novel system and principles to design system parameters are described. Simulation results of an example system are given to verify the feasibility of this novel system.
Gray Sequence Image Shadow Removal Algorithm Based on Computer Vision

Computer vision is a method that includes acquiring, processing, and analyzing digital images. This paper improves the mixed Gaussian model and proposes a shadow detection algorithm that fuses texture features and color features. In order to remove the interference of moving shadows, a shadow removal algorithm combining the gradient of the gray ratio and the normalized cross-correlation feature is proposed. The gray-scale sequence image is used to calculate the ratio of the foreground and its corresponding background gray, and the shadow is judged and removed based on the gradient average of the calculated gray ratio. This paper proposes an improved hybrid Gaussian model and uses different learning rates in different time periods during the background update process, which increases the rate of background model convergence and also improves the detection rate of interesting targets. Color and texture feature shadow detection algorithms have been applied in the process of projected shadow detection based on color features.
Miniaturized Ultra-Wideband Antenna Design for Human Implants

This paper presents the design of a miniaturized ultra-wideband (UWB) antenna for wireless body area network (WBAN) communication in human implant applications around 4 GHz. The CST Microwave Studio (MWS) bio model has been utilized in the design process to generate a realistic model of human tissue. The model has been validated by different measurements. The results are in good agreement with the simulations and provide a bandwidth of 1.5 GHz for the antenna with casing and loaded with porcine tissue.
A New Air Target Orientation Observations Based on Electromagnetic Vortex Radar

Being capable of providing weather-independent, day-and-night, forward-looking, without relative motion or beam scanning, and high-resolution images, electromagnetic vortex radar (EMVR) is a promising sensing technique in applications. However, a great quantity of orbital angular momentum (OAM) modes should be applied in the existing EMVR imaging methods to achieve high azimuthal resolution in air target orientation observations, which seriously limits the improvement of resolution in practice. Hence, in this paper, we propose a orthogonal matching pursuit (OMP) algorithm for EMVR air target orientation observations to improve the azimuthal resolution and reduce the number of measurements. Point target simulation is presented in this paper. The simulation results show that the proposed OMP algorithm significantly outperforms the conventional fast Fourier transform (FFT) estimation algorithm in terms of azimuthal resolution when applied to EMVR air target orientation observations.
Crystal centering using deep learning in X-ray crystallography

A key challenge in X-ray crystallography is to find a good point on the crystal on which to center the beam because the crystal takes radiation damage after a number of shots which significantly distort the measurements. Therefore, the beam needs to be aimed manually by an operator, which results in significant additional effort and time.This paper presents an approach toward automating the beam aiming using machine learning, training a neural network with labeled data, resulting in a more efficient system that does not rely on manual supervision to determine where to aim the beam. A range of different neural network architectures are evaluated based on the accuracy of their predictions.
A Combined Waveform-Beamforming Design for Millimeter-Wave Joint Communication-Radar

Millimeter-wave (mmWave) joint communication- radar (JCR) simultaneously realizes a high data rate communication and a high-resolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the state-of-the-art mmWave communications hardware, however, suffer from a limited angular field-of-view (FoV) and low detection rate for radars due to the employed directional beam. To address this limitation, we propose an adaptive and fast combined waveform-beamforming design for mmWave JCR with a phased-array architecture. We present a JCR beamformer design algorithm that permits a trade-off between communication data rate and radar successful recovery rate in the angular domain. We show that distinct radar measurements can be obtained with circulant shifts of the designed JCR beamformer for compressed radar sensing. Numerical results demonstrate that our JCR design enables the angle-of-arrival/departure estimation of short- range radar targets with a high successful recovery rate and a wide FoV at the expense of a slight loss in the communication rate.
Impact of Ferritin Load on Gonadal Reserve among Regular Transfused β-Thalassemia

Background: Iron overload in association with persistent anemia is responsible for endocrine dysfunction in β-thalassemia patients, blood transfusion combined with iron-chelation can modify life quality in these children, but they tend to suffer from delayed maturity and endocrine dysfunction. Aim: This study aims to correlate degree of hypogonadism to ferritin load in regular transfused β-thalassemia patients. Methods: It was carried out on 30 β-thalassemia major (TM) patients aged 12 to 18 years, puberty was assessed clinically, blood picture on Cell-Dyne 2700, ferritin level and pattern of FSH, LH, testosterone and estradiol before and after gonadotropin (GnRH) analogue stimulation test, they were determined on ARCHITECT ABBOTT system. Results: Twenty patients had not yet achieved puberty, FSH level was 1.45 ± 1.88 mIU/ml before (GnRH) analogue and 3.78 ± 4.19 mIU/ml after 4 hours of injection. LH level was 1.91 ± 4.79 mIU/ml before (GnRH) test, while after 4 hours it was 6.52 ± 7.50 mIU/ml, 88.24% of males had low serum testosterone level, 84.6% of girls had low serum estradiol level, FSH, LH, estradiol, testosterone before and after GNRH analogue were statistically insignificant, mean ferritin level was 3344.32 ± 1142.142 ng/ml, with insignificant correlation to hormonal pattern before and after GnRH therapy. Conclusion: Iron overload and hypogonadism are the presenting data in this study, insignificant correlation between ferritin level and hormonal reserve pattern, there may be another etiology in pathophysiology of low gonadal reserve such as severe anemia, chronic disease and may be genetic predisposition underlying susceptibility to iron toxicity, which need further investigations.
Linear Approximation based Q-Learning for Edge Caching in Massive MIMO Networks

To meet increasing demands in wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy to maximize the average success probability (ASP) in homogeneous Poisson point process (PPP) distributed caching network having massive MIMO base stations. To improve over Q-learning, a linear function approximation based Q-learning is proposed which shows that only a constant number of (three) parameters need updation irrespective of size of state and action sets, while Q-learning in this context requires the parameters update of size number of states times number of actions. Given a set of available placement strategies, simulations show that the approximate Q-learning converge, successfully learns and provides the same best content placement as Q-learning, which shows the successful applicability and scalability of the approximate Q-learning.
Decentralized Dynamic ADMM with Quantized and Censored Communications

In this paper, we develop a quantized and communication-censored alternating direction method of multipliers (ADMM) to solve a dynamic optimization problem defined over a decentralized network. At every time, the network nodes collaboratively minimize the summation of their local objective functions, which are time-varying. The proposed algorithm, termed as DQC-ADMM, tracks the time-varying optimal solution through local iterative computation and communication. Unlike traditional approaches that require the nodes to transmit the exact local iterates to their neighbors at every time, we quantize the transmitted local iterates and introduce a communication-censoring mechanism so as to reduce the communication cost spent in the optimization process. Although the quantized and censored communications cause inaccuracy, we rigorously prove that the proposed algorithm is able to track the time-varying optimal solution with a bounded error under mild conditions. We also demonstrate the tracking performance and communication savings of the proposed algorithm through numerical experiments.
Improving Detection of Malicious Office Documents Using One-Side Classifiers

The current threat landscape is diverse and has lately been shifting from the binary executable application to a more light-coded and data-oriented approach. Considering this, the use of Microsoft Office documents in attacks has increased. The number of malicious samples is high and the complexity of evasion techniques is also challenging. The VBA macros are highly used in enterprise environments with benign purposes, so, in terms of detection, the number of false alarms should be close to zero. In this paper we discuss and propose a solution which focuses on keeping the rate of false positives as low as possible and, at the same time, maximizes the detection rate.
Real-Time SAR Image Registration Based On Segmentation Of Distorted Image

Due to atmospheric interference, topographic fluctuation, unstable radar platform and other factors, SAR image geometric distortion, which is especially significant in the real-time SAR image of UAV, cannot be avoided. In order to solve the problem of SAR image geometric distortion, a registration method based on image segmentation is proposed. The algorithm classifies the image regions according to the degree of image distortion, calculates the mapping relation of different distortion regions, and finally completes the registration of the whole image by using image mosaic reconstruction.
Active Content Popularity Learning via Query-by-Committee for Edge Caching

Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage’s the trade-off between exploration and exploitation of the network, and learn the user’s preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results.
The Design of Real-Time Digital Clothing Projection System

The variety and richness of clothing is an important thing for the brilliance and fluency of the performance. However, if we need to change our clothes for every show, changing clothes during the performance and even carrying clothes are both problems. To improve these problems and increase performance variability. In this paper, we propose a method for users to project virtual clothing on themselves using a computer with a projector. Kinect will capture the user's body and bones as well as each location and direction. The implementation the three steps method of a fast and efficient space coordinate transforming from camera coordinates to a real-world three-dimensional space coordinate to project and control virtual clothing in real time. Users can choose different costumes in our system. Anyone can easily wear virtual costumes during the show.
System-level Simulation of Cooperative Sensor Data Fusion Strategies for Improved Vulnerable Road Users Safety

Cooperation between vehicles and/or with static elements of the road infrastructure enables a wide number of applications and services, such as traffic monitoring and prediction, localization and mapping, or novel safety approaches for vulnerable road users. For instance, relying on on-board sensors, on conventional navigation systems, and on V2X wireless connectivity, vehicles can represent geo-tagged measurements (e.g., LiDAR) in the form of local occupancy grid maps accounting for the presence of obstacles. The latter maps can be subsequently shared, either directly (e.g., with other fellow vehicles around) or via a centralized entity (e.g., edge cloud…). Through cooperative fusion, equipped vehicles thus contribute to elaborate a global view of the physical environment.In this paper, we first describe a flexible end-to-end system simulator that can evaluate such cooperative mapping strategies in complex road driving environments. The overall simulation flow spans from sensor and V2X connectivity abstractions up to fusion algorithms running at the application level. We then present a few illustrating simulation results in a smart intersection scenario, confirming the importance of V2X-aided cooperation to enhance the physical perception of standalone vehicles in terms of both coverage and detection performances.
An Ultra Broadband Multi-Tone Six-Port Radar for Distance Measurements in K-Band Waveguides

Continuous wave (CW) radars show an excellent performance in high precision distance measurements. However, when it comes to absolute distances, the ambiguity of these systems is a notable issue. To extend the unambigous range beyond λ/2, dual tone systems have been presented using two spaced CW measurements. In this paper, a broadband multi-tone radar operating in the K-band using six-port technology is presented. By using more than two frequencies, the unambigous range can be further enlarged. The radar system presented in this paper is able to perform 5-FSK measurements within a bandwidth of 5.5GHz delivering an update rate of 25Hz and a precision of 37. 96µm.
Slow-Time MIMO Radar Clutter Mitigation Based on Robust Pulse-to-Pulse Coding

Slow-time MIMO radar is investigated to deal with some cost sensitive applications like UAVs detection to avoid the use of arbitrary waveform generators and digital receivers. But this method suffers from the deterioration of Signal to Noise Ratio (SNR) caused by superposition of clutter. To solve this problem, a robust pulse-to-pulse coding method is proposed to mitigate clutter as well as suppress the sidelobe level in Doppler domain. In our proposed method, the problem of optimizing weighting vector is transformed into a typical MVDR problem and the design of new weighting vector is obtained by the combination of MVDR solution and a sequence of weight coefficients which are calculated by Fourier inversion. This proposed method can mitigate the clutter adaptively while the sidelobe is suppressed. Both simulation and experiments results validate the reliability and effectiveness of this method.
Road Importance Using Complex-Networks, Graph Reduction & Interpolation

Most people spend hours on the road on a daily basis making road networks a crucial part of our daily lives. Trips to work, grocery store, hospital or even casual jogs and road trips mainly occur on walkable or drivable roads. With the increase of online communities, professionals and enthusiasts, road networks are now abundantly available from various sources making them a great resource for a variety of analysis such as finding the road importance, road characteristics, city planning, and the association between neighborhoods’ walkability and the local obesity rate. However, as data increases, analyzing larger regions requires much more processing power and computational time. We aim to incorporate graph reduction and centrality interpolation while utilizing some already-efficient complex networks centrality algorithms, to produce ready-to-analyze road scores for the entire given data set while reducing the required computational time when compared to the conventional algorithms that do not use reduction. Furthermore, our produced road scores can be applied to non-network characteristics such as amenities, elevation, road type, road condition and road structure to produce more accurate walkability scores.
Multi-Objective Learning for Efficient Content Caching for Mobile Edge Networks

Previous works on learning-based caching problems often only concern prefetching popular contents during off-peak traffic hours, and service them to the edge at peak periods. In this work, we investigate the cache strategy design problem with two possibly conflicting objectives in mobile edge networks when consumer preferences are unknown. We focus on caching for paid contents, and model the decision making problem in a combinatorial multi-objective multi-armed bandit (CMO-MAB) perspective, by taking both the aggregated offloading traffic data in dominant objective and the cumulative revenue in non-dominant objective into account. Furthermore, we propose a multi-objective proactive caching (MOPC) algorithm to jointly optimize the total reward vectors in both objectives. Simulation results demonstrate that the proposed MOPC algorithm outperforms its competitors, which are not specifically designed to address the caching problem involving dominant and non-dominant objectives.
Verification of the Calculation Method on CLAMP-ON Resistance Testing and Measurement

The paper concerns the applicability of the precise calculation of the resistances in an earthing system using the clamp-on method for contactless measurement of resistance. The importance of the calculative method application is evident in the critical cases of a system with only few (near three) earthing points. The paper bases on previous results obtained for the method of independent calculation of each separate earth resistance based on the results from direct clamp-on measurements.
Autonomous Image Segmentation by Competitive Unsupervised GrowCut

In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide a detailed comparative analysis of the three algorithms' segmentation performance and properties. Our analysis identified application-specific regimes that govern the relative performance of the analyzed algorithms.