Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Performance of Physically Constrained Multi-Pair Two-Way Massive MIMO Relaying with Zero Forcing

In this paper, we consider a physically constrained multi-pair two-way massive multiple-input multiple-output (MIMO) decode-and-forward (DF) half-duplex relay system, where multiple single-antenna user pairs exchange information through a massive MIMO relay, and we employ zero-forcing reception/zero-forcing transmission (ZFR/ZFT) at the relay. When the number of relay antennas M becomes very large and tends to be infinite, we study the large-scale approximation of the sum spectral efficiency (SE) with the effect of spatial correlation generated by the constrained space. Furthermore, we investigate the energy efficiency (EE) with a practical power consumption model, and demonstrate the impact of the relay antenna number and the size of constrained space on the EE performance.
On Capacity Sensitivity in Additive Vector Symmetric α-Stable Noise Channels

Due to massive numbers of uncoordinated devices present in wireless networks for the Internet of Things (IoT), interference is a key challenge. There is evidence both from experiments and analysis of statistical models that the uncoordinated nature of channel access leads to non-Gaussian statistics for the interference. A particularly attractive model in this scenario is the additive vector α-stable noise channel. In this paper, we study the capacity of this channel with fractional moment constraints. In particular, we establish well-posedness of the optimization problem for the capacity. We also study convergence of the capacity loss due to an additional constraint where input probability measures are concentrated on spherical shells, in addition to the fractional moment constraints.
Multicore Power Estimation using Independent Component Analysis Based Modeling

State-of-the-art power estimation research for multicore processors combine performance counters that collect run-time activity information with an offline-generated power model. To generate these power models, the package power is measured and the activity information is traced while synthetic workloads are executed. These workloads stress distinct core components in order to expose power responses so that the activity information has low collinearity. The measurements are then combined into a power model describing the general power behavior. However, one of the main drawbacks of these synthetic workloads is that they are most of the time custom-designed for a given multi-core architecture and are hardly available. In this paper, we present a methodology to generate power models using freely available benchmarks, e.g. PARSEC/Splash-2. To minimize the collinearity of the activity information due to the uncontrolled/unspecified behavior of these more general benchmarks, we propose to use independent component analysis. This allows to avoid the use of synthetic workloads and a reduction of the relative error by 24% in the average case, when compared to prior state-of-the-art work. Although, we also observe an increase of 22% relative error in the worst case for our approach, this can easily be improved by using either different or more training benchmarks. These promising results give a strong indication that independent component analysis could directly be used with real application workload, leading to the possibility to build/improve power models during runtime.
Immunomonitorization of cytokines: renal transplant patient into tolerance or rejection?

Results: Cyclosporine (CyA) based triple immunosuppressive regimen was associated with acute rejection (p<0.001) and also with IL-10 (p<0.001), TGF-β (p<0.001), IL-1α (p<0.005), IFN-γ (p<0.001) positivity. In the healthy control group all of the cytokines were measured negative indicating an immunological balance. Hemodialysis for over 5 years significantly leads to TGF-β (p=0.021) positivity but it does not correlate with acute rejection after renal transplantation. Conclusion: We found that immunomonitorization by cytokine secretion profile is a promising tool to determine immunological risk at renal transplantation patients and is necessary to optimize individual therapy decisions related to immunosuppression regi- mens, thereby improving transplant outcomes.
A Matching-Theoretic Approach to Distributed SWIPT in Ad-Hoc Wireless Networks

This paper studies the problem of stable node matching for distributed simultaneous wireless information and power transfer in multi-user amplify-and-forward (AF) ad-hoc wireless networks. Particularly, each source node aims to be paired with another node that acts an AF relay to forward its signal to the destination, such that the achievable rate is improved, in return for some payment. In turn, a matching-theoretic solution based on the one-to-one Stable Marriage Matching game is considered, and a distributed polynomial-time complexity algorithm is proposed to pair each source node with its best potential relaying node based on the power-splitting ratios. Simulation results are presented to validate the proposed matching algorithm, and show that it yields sum-utility and sum-payment that are comparable to those of centralized schemes, with the added merits of low-complexity, and network stability.
Distributed Model Predictive Control of Wind Farms for Short-Term Grid Support

The increasing share of power generation from inverter-coupled sources leads to faster transients in the power grid and makes it necessary to provide new methods for wind farms also to participate in grid support for short-term disturbances. This paper proposes to use distributed model predictive control of wind farms allowing to compensate such disturbances better than established control schemes, while considering relevant constraints on states and inputs.
Bluetooth Beacon Based Accurate Indoor Positioning Using Machine Learning

The objective of this work is to develop an indoor location system with high precision and continuous position monitoring in real time with the use of a mobile phone without any special hardware using only commercially available low-cost sensors. Finding the location is done using the measured Received Signal Strength Indicator (RSSI) value of Bluetooth beacons received from mobile phones combined with measurements from other phone sensors. For the development of our model, we collected measurements for the RSSI values from Beacons which we placed in a space of 30.75 sqm and the values from the mobile accelerometer in motion. We divided the space into 16 subareas of 1.45m x 1.35m and used our measurements to develop a machine learning model using the open source TensorFlow framework to predict the correct subarea of the user. Through experiments, we show that our model can reach an accuracy of 0.7209 which means that our system can predict the correct user location in 72% of the cases with accuracy less than 1 meter.
Analog Behavioral Model of Underdamped Free Oscillation of Cantilever Beams

A physical model representing an underdamped free oscillation of a cantilever beam is constructed. The model is incorporated into a pSPICE Analog Behavioral Model (ABM) in order to construct a circuit schematic that generates a model-based waveform representing that of the experiment. A strain gage is employed to sense the change in strain in the oscillating cantilever beam. The frequency and damping ratio of oscillation of the cantilever are determined by means of curve fitting. Experiment and pSPICE ABM results show an excellent agreement.
Sina Weibo Bursty Event Detection Method

In response to the recent frequent outbreaks of bursty events, if the supervision is not strengthened, the grievances caused by it will have an extremely negative impact on society. Therefore, how to effectively detect bursty events in social networks has become the focus of research. In order to eliminate the interference of local daily events generated by noisy data, this paper proposes a bursty event detection method for quantifying the influence of microblog text. Through the analysis of high-impact microblogs, the burst words are mined, the potential bursty event data sets are constructed, and the k-means cluster analysis method is applied to detect the event. In order to verify the method, this paper compares with the commonly used detection methods on the real dataset. The experimental results show that the method has certain improvement in accuracy and efficiency.
How to Apply Random Projections to Nonnegative Matrix Factorization with Missing Entries?

Random projections belong to the major techniques to process big data and have been successfully applied to Nonnegative Matrix Factorization (NMF). However, they cannot be applied in the case of missing entries in the matrix to factorize, which occurs in many actual problems with large data matrices. In this paper, we thus aim to solve this issue and we propose a novel framework to apply random projections in weighted NMF, where the weight models the confidence in the data (or the absence of confidence in the case of missing data). We experimentally show the proposed framework to significantly speed-up state-of-the-art NMF methods under some mild conditions. In particular, the proposed strategy is particularly efficient when combined with Nesterov gradient or alternating least squares.
Characterization of On-Chip Interconnects: Case Study in 28 nm CMOS Technology

Performance of a high-speed network on a chip is mainly affected by the performance of the global on-chip interconnects. This paper investigates a point-to-point transmission line interconnect to address the performance limitations due to the scaling problems of on-chip global interconnects in new CMOS technologies. A brief explanation is given about the basic properties of the transmission lines that should be considered. Moreover, the design methodology is given for designing the on-chip interconnect and understanding its behavior. Based on the different transmission line geometry, 3D EM simulations are done and S-parameters are carried out to analyze the effects of the different metal layers, width of the signal line, and space to shield layers of the interconnect. This results in a systematic understanding of the design of the on-chip interconnect and its performance dependence on various parameters. Finally, a 5 mm length on-chip lossy transmission line with serpentine shape and shielding layers is implemented in 8-metal-layer 28 nm CMOS standard technology.
Derivation of Respiratory Effort from Photoplethysmography

In this paper, a new and non-invasive method has been proposed to retrieve respiratory effort component from photoplethysmography (PPG) sensor. The PPG signals are recorded using a commercial wrist worn device. Inverse synchrosqueezed wavelet (ISSW) transform has been applied to reconstruct the respiratory component and then to derive respiratory effort component. The reconstructed respiratory component and the respiratory effort signal from PPG are shown to be highly correlated with airflow signal and extracted respiratory effort obtained using a thermocouple secured under nose. The findings in this paper can make significant changes in various applications such as for sleep analysis, where continuous monitoring of respiratory effort from a wrist worn sensor unobtrusively is crucial for identification of various sleep abnormalities such as sleep apnea.
Coded Aperture Design for Super-Resolution Phase Retrieval

Phase retrieval is an inverse problem which consists on estimating a complex signal from intensity-only measurements. Recent works have studied the problem of retrieving the phase of a high-resolution image from low-resolution phaseless measurements, under a setup that records coded diffraction patterns. However, the attainable resolution of the image depends on the sensor characteristics, whose cost increases in proportion to the resolution. Also, this methodology lacks theoretical analysis. Hence, this work derives a super-resolution model from low-resolution coded phaseless measurements, that in contrast with prior contributions, the attainable resolution of the image directly depends on the resolution of the coded aperture. For this model we establish that an image can be recovered (up to a global unimodular constant) with high probability. Also, the theoretical result states that the image reconstruction quality directly depends on the design of the coded aperture. Therefore, a strategy that designs the spatial distribution of the coded aperture is developed. Simulation results show that reconstruction quality using designed coded aperture is higher than the non-designed ensembles.
Framework For UAV Mobile Object Tracking based on UE4SIM

Nowadays, Unmanned Aerial Vehicles (UAVs) gain popularity and become more and more deployed for many applications. Tracking moving object is among the widespread UAVs related applications that attract researchers. Accordingly, in this paper, we propose an autonomous object tracking framework for (UAVs) called MOSSEcA-UAV. Our proposed framework integrates the MOSSECA tracker and adapted it for UAV target tracking. Simulation results demonstrate the robustness of our framework.
Method for Fused Phase and PCA Direction Based on a SIFT Framework for Multi-Modal Image Matching

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching. To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper. This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image. Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious. Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points. Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate. The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.
Measurements and Stabilization of the Radio Signals Time Delay When Their Transmitting Over Long Wideband Analog Fiber Optics Links

The results of measurements of the time delay in analog wideband fiber-optic links for microwave signal transmission are presented. Fiber-optic links operates in frequency range 0.1. 18 GHz. The delay is determined by the results of measurements of the fiber-optic link phase-frequency response performed by using a vector network analyzer with a large number of points in the measuring trace. The length of the fiber-optic link in the experiments extends up to 1000 meters. The results of experiments on stabilizing the microwave signal delay at the fiber-optic link output using a fiber stretcher with piezoelectric control inserted in the link are also considered.
Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists. More recent work has focused on more than one views. Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information. However, combining the information of four views of mammograms increases the performance of classification. In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram. The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed. Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction. Our proposed system is trained on four views of mammograms, after data augmentation. We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms. In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification. We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems. The value of AUC for normal and abnormal classification is 0.93.
A Novel Hybrid-Excitation Switched-Flux Linear Machine With Partitioned-Excitations

This article proposes a novel hybrid-excitation switched-flux linear machine with partitioned-excitations (PE-HESFLM), which places the armature windings and field winding into different primaries. It can be geometrically taken as the combination of a conventional flux-switching linear machine and an additional primary for accommodating the field winding. Therefore, the proposed machine can exhibit increased armature and field slot areas so that the competition for space between these two winding types is effectively mitigated, which in return translates to improved thrust density and flux-adjusting capability. First, the configurations of the proposed PE-HESFLM are introduced, following which different slot/pole combinations are globally optimized based on genetic algorithm (GA). Finally, the electromagnetic performances are comparatively investigated. It is shown that the proposed machine can effectively improve the thrust density and flux-adjusting capability, so that the proposed machine configuration is a particularly good candidate for applications which require wide-speed capability.
Ray Theory-Based Transcranial Phase Correction for Intracranial Imaging: A Phantom Study

Ultrasonic imaging provides a non-invasive way to diagnose brain disease. However, due to imaging degradation effects from the phase-aberration and reverberation, it is still challenging to achieve an accurate transcranial imaging. The objective of this work is to improve the quality of transcranial imaging. To this end, a ray theory based transcranial phase correction method was proposed to correct the phase abberation induced by cranial bones. With the pre-knowledge of the shape and longitudinal velocity of the cranial bones, the corrected phases are derived by solving eikonal equation (ray-theory). The Ideal Synthetic Aperture Focusing Technique (I-SAFT) is applied for signal acquisitions in simulations and in-vitro phantom experiment with one-element transmitting and all-element receiving method. Dynamic focusing is achieved at each imaging position with I-SAFT and the transcranial imaging distortion is modified with the proposed phase correction method. Simulations and experiments show that the imaging distortion of target circular phantoms was corrected, and the imaging quality is improved after the phase correction. With the proposed method, the average error of the central position of target phantoms decreases from 1.98 mm to 0.21 mm, the eccentricity of fitted ellipse averagely decreases from 0.63 to 0.19, and the average maximum luminance contrast of phantoms improves from 37.36dB to 42.41dB. It is illustrated that the proposed ray-theory based phase correction method might be useful for intracranial imaging.
Spectral Coexistence of 5G Networks and Satellite Communication Systems Enabled by Coordinated Caching and QoS-Aware Resource Allocation

The combination of underlay spectrum sharing and coordinated multi-point technologies promises substantial spectral efficiency (SE) gains for 5G cellular networks. In this work, we present a family of simple cooperative mobile edge caching strategies that create joint transmission (JT) opportunities and make use of the non-computational-demanding score-gated least recently used (SG-LRU) caching scheme to achieve high cache hit rate, thus increasing the sum-SE and reducing both the backhaul traffic and the content access latency. In addition, we derive a low-complexity coordinated quality-of-service (QoS) aware resource allocation scheme that maximizes the sum-SE of coordinated beamforming (CBF) under given transmission power per base station, inter-system interference power, and per-user QoS constraints as well as simple alternatives for both CBF and JT. We consider a use case where a cellular network coexists with a fixed satellite service earth station in the 3.7-4.2 GHz C-band. Numerical simulations illustrate the performance gains of the proposed coordinated caching and resource allocation strategies and shed light on the impact of various parameters on their efficiency.
An Approach to Granulation and Ontology Similarity Measurement for Intelligent Decision Support in Semi-Structured Subject Areas

The semi-structured subject areas are one of the main study objects in the theory of artificial intelligent systems. As the largest intelligent management system in a semi-structured area, an intelligent railway transport management system is considered. The task of the constructing granular ontologies for a semi-structured domain that is different from the known device and data synchronization between granules is examined. An approach to measuring the similarity degree is proposed by the performed measuring analysis of the semantic proximity of the ontology contexts. The proposed approach is based on the graph representation of some ontologies; it is universal and applicable for intelligent decision support in semi-structured subject areas.
A Survey on Deep Learning Based Intrusion Detection System

Although new technologies bring a lot of convenience to our lives, there are new problems in the field of cyber security. People use many critical applications such as e-commerce, e-banking, e-government and share critical data including personal data, account and credit card information. With the large amount of data produced and the increase in the zero-day attacks, the existing security applications are insufficient. In this context, deep learning algorithms have been used in cyber security applications. In the literature, deep learning application studies have been done in many areas like PC-based malware, android-based malware, intrusion detection system, phishing attack, cyber intelligence and spam detection. When the results of the studies were examined, it was observed that these studies' outputs give more successful results than the traditional security applications. A lot of studies have been done for many different network infrastructures such as traditional networks, IoT, SDN, Ad Hoc. In this paper, recent deep learning based intrusion detection systems are investigated.