Miracle-dz/UniFAR-FTUs huggingface dataset:
OSNR Based Quality Estimation in Optical Network

In the last few decades, optical technology has become much advance to transmit higher traffic over a long distance. In spite of this, physical layer impairments (PLIs) poses a great concern over the optical communication to meet the demand of next generation technology. Hence, PLI aware algorithm is a major factor to improve the quality of an optical network. This paper discuss the quality estimation based on optical signal-to-noise ratio (OSNR). This approach includes noise due to amplifier spontaneous emission (ASE) in optical fiber, multiplexer, demultiplexer, and coherent crosstalk in optical switches to present OSNR in a wavelength division multiplexing/dense wavelength division multiplexing (WDM/DWDM) networks. In this work, estimation of ASE noise power for different values of wavelength and bandwidth has been discussed. This paper also presents a routing and wavelength assignment (RWA) algorithm based on OSNR to improve the quality of transmission.
A Computer-Aided System for Prostate Cancer Diagnosis

In this paper, a computer-aided diagnosis (CAD) system for early diagnosis of prostate cancer from diffusion weighted magnetic resonance imaging (DWI) is proposed. The proposed system begins with defining a region of interest that contains the prostate across the various slices of the input volume. Then, the apparent diffusion coefficient (ADC) of the defined region is calculated, normalized and refined. Finally, the classification of prostate into either benign or malignant is performed through two stages. In the first stage, seven convolutional neural networks (CNNs) are utilized to get initial probabilities for each case. Then, a random forest (RF) classifier uses these probabilities as input to decide the final diagnosis. The proposed system is a novel system in the sense that it has the ability to detect prostate cancer without any prior processing (e.g., the segmentation of the prostate region). Evaluation of the developed system is done using DWI datasets collected at seven different b-values from 32 patients (16 benign and 16 malignant). The acquisition of these DWI datasets is performed using two different scanners with different magnetic field strengths (1.5 Tesla and 3 Tesla). The resulting accuracy of the proposed system after the second stage of classification shows a good performance close to the performance of up-to-date systems.
Visual Tracking Algorithm Based on Improved Kernelized Correlation Filters

During the tracking, kernelized correlation filters cannot handle this situation well when the target occluded seriously, changes scale and goes out of view. To solve the above problem, we propose an improved visual tracking approach based on kernel correlation filters. First and foremost, to locate the target and estimate the target scale, two correlation filters are employed to tackle the corresponding problems. Meanwhile, to memory the target appearance, we use an independent target appearance correlation filter conservatively updated. Second, we combine the SVM detector to re-detection when tracking failure occurs caused by the target undergoes heavy occlusion or missing in video stream. Third, to solve model drifts due to heavily surface variations of the target, we apply a novel adaptive model updating strategy to update the correlation filters and classifier. Extensive experimental results demonstrates that the proposed tracker achieves an excellent performance on the OTB2013 benchmark dataset, comparable to the state-of-the-art methods.
Unsupervised electric motor fault detection by using deep autoencoders

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors ʼ knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron ( MLP ) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory ( LSTM ) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine ( OC-SVM ) algorithm. The performance has been evaluated in terms area under curve ( AUC ) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %.
Design and Analysis of Modified Leaky Integrate and Fire Model

Various neural models have been proposed to account for the spiking behaviour of neurons when stimulated by sources, such as external current and synaptic stimulations. In the presented work, the time dependent synaptic vesicle release probability based on calcium current has been computed. This vesicle release probability has been used to design the modified leaky integrate and fire model, which was compared with traditional leaky integrate and fire model on the basis of goodness of fit with respect to the spike patterns of the Guinea pig cortical pyramidal neurons. It has been shown that modified leaky integrate and fire model is less error prone than the traditional leaky integrate and fire model during the fitting process.
Temperature And Supply Voltage Insensitive Relaxation Oscillator Using Voltage Reference

These days smart devices are developing rapidly and becoming smaller. This recent trend requires the on-chip oscillator to be smaller and more accurate. A relaxation oscillator, which occupies a small area and consumes low power, is the potential candidate. However, its operation is sensitive to temperature and the supply voltage. Therefore, solution for these issues is very important. In this paper, we design a small area on-chip current-mode relaxation oscillator, that is insensitive to temperature and supply voltage variation, using voltage reference. The operation frequency of the proposed oscillator is 3.01MHz. And the frequency variation is 2.39% when supply voltage change from 1.4V to 2.0V and 1.09% when temperature change from -20°C to 100°C with the supply voltage of 1.8V. This oscillator is designed and fabricated in 0. 18µm CMOS process, and the oscillator consumes 40µW and occupies 0.0196mm2.
Cramér Rao Lower Bound Derivation of Parameter Estimation for Hypersonic Platform SAR-GMTI

The accuracy of parameter estimation of the moving target is an important index to evaluate the performance of ground moving target indication (GMTI) system. On this point, for synthetic aperture radar (SAR) equipped on the hypersonic platform, this paper mainly derives the Cramér Rao lower bound (CRLB) of parameter estimation of the moving target. Firstly, an approximate curved geometric model matched with the platform trajectory is established. Then, the multichannel steering vector of along-track interferometric phase is obtained based on the derivation of the complex response of SAR image. Further, the joint probability density function (pdf) of target parameters is designed, based on which the CRLB of parameter estimation is derived. Finally, the CRLB for the radial velocity estimation is simulated under different signal to clutter ratio (SCR) values and baselines. Meanwhile, its influence on the GMTI performance is further analyzed.
Voltage Stability Assessment Using Synchronized Phasor Measurements Applied to the Argentine Power System

This paper presents tests of different methods for long term voltage stability analysis based on PMU measurements. In particular, Thevenin Impedance Matching and sensitivity calculation are studied. Simulation are performed in a Argentine power system model, modified to include the main components for studying long term voltage stability. In particular, Disconnection of a large Thermal Generating Station causing long-term voltage instability at Gran Buenos Aires Area is studied. It is shown that computing the apparent impedance of Thevenin equivalent at a load bus from measurements taken at a single time but at various locations of the transmission corridor produce a significant reduction of the estimation error respect from the case when only local measurements are used. Voltage Stability analysis is performed using a reduced power system model with changing parameters that are computed only with PMU data at each time. Voltage stability index and sensibility calculations are performed using this reduced power system model.
Simulation And Electrical Characterization Of A Novel 2D-Printed Incontinence Sensor With Conductive Polymer PEDOT:PSS For Medical Applications

Due to the growing numbers of elderly people in the world, who suffer from incontinence and are in the need of care, technologies are necessary to increase the effectiveness of nursing staff and enhance the hygiene for humans to improve life quality. For this reason a low cost humidity sensor system printed onto the substrate of a diaper with the novel organic conductive ink PEDOT:PSS (Poly (3,4-ethylenedioxythio-phene):Poly(styrene sulfonate)) was developed in previous work [1]. The novel material PEDOT:PSS is still expensive because of rare use in research and market demand. Therefore a way for optimization of the sensor is aimed to print the sensor with less material, but at the same time with no loss of sensitivity. With this purpose, two theoretical models are developed. An analytic model with geometrical based calculations and a Finite Element Analysis (FEA) simulation model, for deeper understanding of electric field effects with focus on the total capacitance of the sensor. To verify these theoretical models a characterization measurement of manufactured samples of previous work [1] is made, to obtain a comparison between every experimental method. For the theoretical models the necessary material parameters are characterized.
A 0.25-V Rail-to-Rail Three-Stage OTA With an Enhanced DC Gain

This brief proposes a 0.25V rail-to-rail three stage OTA. The proposed OTA improves a DC gain by inserting an NMOS gate-driven amplifier into the conventional bulk-driven OTA. In addition, it uses an asymmetric self-cascode transistor and an indirect feedback compensation to enhance a DC gain and an unit-gain frequency. At the first stage, the bulk-driven amplifier has a rail-to-rail input, but it has a low DC gain due to a small transconductance. At the second stage, the NMOS gate-driven amplifier enhances the DC gain. At the last stage, the common-source amplifier drives an output load capacitor. The proposed OTA was fabricated using a 65nm CMOS process. Its area is 0.002mm2. It consumes $0.026~\mu \text{W}$ at the supply-voltage of 0.25V. The DC gain and unit-gain frequency are 70dB and 9.5kHz, respectively, with the phase margin of 88° at the load capacitance of 15pF.
Learning Typed Entailment Graphs with Global Soft Constraints

This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.
${\mathsf \Gamma}$-Source Magnetic Integrated Filter for Single-Phase Grid Tied Voltage Source Converters

This article proposes two magnetic integrated filters, called <italic><inline-formula><tex-math notation="LaTeX">${\mathsf \Gamma}$</tex-math></inline-formula>-source</italic> and <italic>Flipped-<inline-formula><tex-math notation="LaTeX">${\mathsf \Gamma}$</tex-math></inline-formula>-source</italic> with the advantage of better magnetic circuit utilization. Therefore, the number of required magnetic cores and the total size of inductive elements are reduced compared to the other high-order filters, such as the <italic>LCL</italic> and the <italic>LLCL</italic>. Also, the total windings copper weight as well as the windings currents and losses are lower. It will be shown that the required air gap for the magnetic core is very short, so the adverse effects of the electro-magnetic interference and the fringing flux are also prevented. Thus, the proposed filters require a small and simple magnetic element, which also works at a lower temperature. Moreover, the proposed filters offer a robust stability and a high filtering performance against a wide range of parameter mismatches. Based on the theoretical analysis and the comparative experimental tests on a grid tied converter, the advantages of the proposed filters are confirmed.
Leakage Control System Using Data Estimation of Resistive Memory

In this work, we present a data-aware read sneak current control technique for improving sensing margins in cross-point (X-point) resistive memories. We introduce a novel method of estimating resistive memory data distribution. The result from this estimation is utilized for determining adequate bias condition for reliable read operation. The adjusted bias condition will realize constant leakage current, which facilitates reliable sensing regardless of the data distribution in the resistive memory array.
Mechanics of Copper Wire Bond Failure due to Thermal Fatigue

Thermal stresses occur on wire Bonds due to mismatch of coefficient of thermal expansion (CTE) between wire and mold compound in addition to the global deformation of the package. This leads to different failure modes (different location of crack in wire bond) based on material and geometrical characteristics of package, ultimately leading to an open circuit. Such failures have been observed and reported widely in literature in the ball bond interface, ball neck region, mid span of wire and at stitch bond; however, little is known about the kind of damage that occurs at these critical regions due to material and geometrical differences in package and wire. Furthermore, different phases of m inter metallic compounds (IMCs) and its thickness that form at the wire bond-bond pad interface alters stress conditions on the wire bond. First part of this study presents a review of existing models to obtain stress in wire Bonds and results of finite element stress analyses from literature. Second part of the work focuses on studying three factors, namely, package type, mold compound material and interfacial changes in wire bond, for understanding types of deformations and stresses that develop on the wire bond. This analysis is aimed at providing vital information about package and materials to aid in optimizing design for extended reliability of wire Bonds.
A Reduced Overhead Low Complexity Sum Rate Optimization Algorithm for Massive MIMO System

In this paper, we study a joint spatial division multiplexing (JSDM) beamforming scheme based on the weighted minimum mean square error (WMMSE), aiming to optimize the sum rate of FDD massive MIMO system with low complexity and reduced-dimensional channel state information at the transmitter (CSIT). The proposed algorithm concatenates a prebeamforming matrix, which depends on the channel second-order statistics and is achieved by block diagonalization (BD), with a multi-user precoder to maximize the sum rate of the system by the low complexity WMMSE algorithm. The simulation results show that the WMMSE-based algorithm effectively increases the sum rate of the system comparing to the conventional JSDM which designs the two-stage beamformer with BD and zero forcing method. At the same time, the effect of effective rank in BD on the performance of the algorithm is analyzed.
Double-Parity-Check CA-SCL Encoding and Decoding for Polar Codes

Due to the unsatisfactory block error rate (BLER) performance of short or moderate block , the further development of polar codes is limited. In this paper, the parity-check codes concatenated cyclic redundancy check aided successive cancellation list (CA-SCL) decoding algorithms are studied. Then, an efficient concatenated method, double-parity-check CA-SCL (DP-CA-SCL) polar codes, is proposed according to exiting parity-check decoding algorithms. Not only can this new method improve the BLER performance in short or moderate block, but also it can guarantee the reduction of complexity relative to original CA-SCL method. The simulations in additive white Gaussian noise channel with binary phase shift keying modulation reveal that the DP-CA-SCL polar codes can provide significant performance gains. Comparing to the original CA-SCL decoding, there are 0.5 dB performance gains using proposed method at BLER of 10-3 with (128, 64) polar codes. For another moderate block (256, 128) polar codes, it can also guarantee performance gains at least 0.18 dB.
A Privacy Inference Model Based on Attribute Graph

Big data includes users' profiles, relations and personal life details. Users' data are collected and analysed by third parties. In order to protect their own privacy, users may decide to select the types of data that third parties are entitled to collect. Privacy inference models aim to obtain additional information beyond what is allowed by users. These models are composed of two main parts: users' relationship and profiles, also referred to as attributes. In this paper, we aim to study the privacy inference based on attributes and ignore users' relationship. The goal of this paper is to predict unknown sensitive information according to other basic information. In this work, we define an attribute graph, and propose a model based on attribute graph to infer privacy information. And we propose an algorithm to achieve attribute graph. What's more we demonstrate the effectiveness of this privacy inference model by means of an experiment.
Cognitive Spectrum Sharing Algorithm Based on Secondary Users’ Services

In cognitive radios, considering the fact that the varied services of secondary user will have different satisfaction with the spectrum provided by the primary operators, a satisfaction enhancement factor related to the transmission efficiency of the secondary user is introduced into spectrum sharing in this paper, which enables the secondary user to select the appropriate amount of shared spectrum according to the services requirements. Betrand game model is built to simulate the actual spectrum sharing scenarios with multiple primary operators and a secondary user. With the goal of optimizing the utility of each spectrum operator, a distributed price strategy is adopted in the Betrand game model to update the spectrum trading price of primary operators at different time. The simulation results show that the revenue of the primary operators can quickly converge to the Nash equilibrium. Also, the proposed spectrum sharing algorithm improve the convergence speed.
On Improving the Performance of Software-Defined Networking through Middlebox Policies

In today‘s networks, middleboxes, typically deployed as standalone devices with no standardized access, play an important role in providing services such as firewalls and NATs as well as load balancing. These networks resort to Software-Defined Networking (SDN) for management that can provide a centralized, programmable environment to direct traffic through a desired service chain. However, even if the SDN Controller is able to determine the best path to a certain middlebox, not having prior knowledge of its policies may lead to traffic bottlenecks, degrading overall network performance. By jointly considering the characteristics of middleboxes and SDN, we design a Lightweight, cost-effective, and middlebox policy-aware routing method to address this challenge. That is, a middlebox-translator module is added to the SDN architecture to help middleboxes give the controller routing“hints” in the REST (Representational State Transfer) API format so that better routing decisions can be made. A performance study using emulations of Mininet, shows that, compared to existing methods, our middlebox-aware design results in better performance in SDN-enabled systems, e.g., greater network bandwidth savings in firewall systems, or reduced response time as well as higher throughput in load-balancing systems.
An Approach to Marker Detection in IR- and RGB-images for an Augmented Reality Marker

We introduce an augmented reality marker based on ArUco markers (Garrido-Jurado et al., 2014) that can be detected in RGBand IR-images by using retroreflective material. Due to active perception by IR-capable camera systems the negative impact of external disturbances like change of light conditions on the marker detection is minimized. By the parallel processing architecture of RGBand IR-images redundancy stabilizes the detection. As different retroreflective materials influence the image quality depending on the camera system, we also examined different retroreflective materials and compared the performance of the Kinect V2 and the Intel RealSense D435 regarding the detection probability depending on the geometrical distance of the augmented reality marker to the camera.
Robust Interpretation of Genomic Data in Chronic Obstructive Pulmonary Disease (COPD)

Within genomic studies, a considerable amount of publications have reported SNP variants associated with COPD with little to no reproducibility. In this paper, we present a robust methodology which analyses a COPD cohort dataset using a genome-wide association study, additionally an investigation of the associated results using a variety of machine learning (ML) methods is performed. We use a logistic regression model to provide preliminary results and for further analysis we use machine learning models, RF, MLP, GLM and SVM. Within this study, indications of well established SNPs in previous publications occur in the preliminary results but fail to provide further indication of associative relationship when using ML methods for classification purposes. Results within this study show little to no predictive power after performing a robust methodology. These results indicate that a standardization of practice should be implemented to ensure the publication of false positive results is reduced and deterred. Further investigation of associative features should be considered a standard practice given the resulting information that can be provided with its' use.