Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ultimately Reasonable, Liable Bandwidth Conserved Coherent Detection of OSSB RoF Signal, Which Stamp Out All Faults of Current Schemes

Optical Single Side Band signal signal plays a vital role in RF over fiber technology which combines abundant optical signals onto a single fiber through regulating wavelength's of optical signals. WDM PON(Wavelength Division Multiplexing Passive Optical Networks) are prominent with modern communication systems because they grant them to broaden the maximum amount of information propagated through the network. The most feasible and decisive coherent RoF system established on OSSB modulation is developed employing DP-MZM (Dual Parallel Mach-Zehnder Modulator). The microwave vector signal information is used to activate DPMZM via two ports having orthogonal phase shift and operating it in suitable biasing points. Highly efficient, information bearing SSB signal originated is broad casted over OF to the Target. Realising the distinguished influence of CD, target executes it. A LO is regenerated for CD, yet introducing Phase noise strongly due to the time delay of two signals. Thus a remarkably capable algorithm is introduced, in this paper, which profitably remove PN in signal recovery. The experiment is proved by evaluating with three different informations, QPSK, 16QAM and 2 QAM with bit rates 2. 5 Gb/s, 5 Gb/s and 3Gb/s respectively, verified by BER values of −12, −16, −10 for received power of 2 dBm, 6 dBm and 9 dBm respectively for a transmitted power of 16dBm.
Robust Beamforming for Downlink 3D-MIMO Systems with l11-norm Bounded CSI Uncertainty

In this paper, a novel robust beamforming scheme is proposed in three dimensional multi-input multi-output (3D-MIMO) systems. As one of the typical deployments of massive MIMO, a 3D-MIMO system owns sparse channels in angular domain. Thus, various of sparse channel estimation algorithms produce sparse channel estimation errors which can be utilized to narrow down the perturbation region of imperfect CSI. We investigate a l1-norm bounded channel uncertainty model for the robust beamforming problems, which captures the sparse nature of channel errors. Compared with the conventional spherical uncertainty, we prove that the scheme with l1-norm bounded uncertainty consumes less beamforming power with the same signal to interference and noise ratio (SINR) thresholds. The proposed scheme is reformulated as a second-order cone programming (SOCP) and simulation results verify the effectiveness of our algorithm.
Quantification of System-Level Mismatch Losses using PVMismatch

Differences in the current-voltage characteristics of photovoltaic (PV) modules connectedin series and parallel combinations lead to a loss in the system level power referred to as “electrical mismatch”. As a result of electrical mismatch, the power at the maximum operating point of the PV system is less than the sum of the power outputs of the modules if they were acting independently. In this study, the PVMismatch python package was used to model PV modulesfrom two representative flash test datasets. Randomly sampling from that dataset, a variety ofPV systems configurations were simulated using the Monte Carlo technique to arrive at distributions of potential configuration-specific mismatch losses. Further, this method was extended to simulate annual energy loss due to mismatch for a sample location. The method provides a framework to quantify electrical mismatch losses for accurate performance modeling of PV systems and enables sensitivity analyses to adopt effective binning strategies for reducing system level mismatch losses.
Application of UAV Precise Navigation in Environmental Safety Monitoring and Detection

Aiming at how to accurately position the UAV in the environmental safety monitoring, a new positioning and navigation method is proposed in this paper. This method can make good use of the regional landmark features to realize the precise positioning and navigation of the UAV. The method can comprehensively utilize the point feature of the image and the image contour feature to perform image matching, and overcomes the deficiencies of the unilateral only through the contour feature or the point feature matching, thereby forming a new landmark feature registration method. Experiments show that the method has certain robustness and stability, and it is improved in efficiency and accuracy when applied to environmental safety monitoring and detection.
Locally Consistent Parsing for Text Indexing in Small Space

We consider two closely related problems of text indexing in a sub-linear working space. The first problem is the Sparse Suffix Tree (SST) construction of a set of suffixes $B$ using only $O(|B|)$ words of space. The second problem is the Longest Common Extension (LCE) problem, where for some parameter $1\le\tau\le n$, the goal is to construct a data structure that uses $O(\frac {n}{\tau})$ words of space and can compute the longest common prefix length of any pair of suffixes. We show how to use ideas based on the Locally Consistent Parsing technique, that was introduced by Sahinalp and Vishkin [STOC '94], in some non-trivial ways in order to improve the known results for the above problems. We introduce new Las-Vegas and deterministic algorithms for both problems. We introduce the first Las-Vegas SST construction algorithm that takes $O(n)$ time. This is an improvement over the last result of Gawrychowski and Kociumaka [SODA '17] who obtained $O(n)$ time for Monte-Carlo algorithm, and $O(n\sqrt{\log |B|})$ time for Las-Vegas algorithm. In addition, we introduce a randomized Las-Vegas construction for an LCE data structure that can be constructed in linear time and answers queries in $O(\tau)$ time. For the deterministic algorithms, we introduce an SST construction algorithm that takes $O(n(\log \frac{n}{|B|}+\log^*n))$ time (for $|B|=\Omega(\log n\log^*n)$). This is the first almost linear time, $O(n\cdot poly\log{n})$, deterministic SST construction algorithm, where all previous algorithms take at least $\Omega\left(\min\{n|B|,\frac{n^2}{|B|}\}\right)$ time. For the LCE problem, we introduce a data structure that answers LCE queries in $O(\tau\sqrt{\log^*n})$ time, with $O(n(\log\tau+\log^*n))$ construction time (for $\tau=O(\frac{n}{\log n\log^*n})$). This data structure improves both query time and construction time upon the results of Tanimura et al. [CPM '16].
A New Antenna Array and Signal Processing Concept for an Automotive 4D Radar

On the way to Highly Automated Driving (HAD), new conditions for sensors used in vehicles arise. To achieve a highly accurate environmental perception the resolution of radar sensors has to be increased. Only with fine grained sensor information, it is possible to maneuver safely and highly automated at all road conditions in urban and rural surroundings. A prototypical implementation of such a sensor, which fulfills all these requirements, is presented in the following as automotive 4D radar. The relevant parts of the baseband signal processing are explained in combination with the used modulation waveform. A new antenna array arrangement is introduced which provides the ability to measure angles in both azimuth and elevation. To estimate the two angles of arrival a method which performs this in combination is exemplified. First measurements with a cyclist are recorded and show a radial speed enhanced 3D radar image. The first results are extremely promising. For validation of the measured data, also a simulation of the same scenario with identical system parameters is performed and shown.
Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided Space-Time Adaptive Processing

In space–time adaptive processing (STAP) technique, the estimation of the interference-plus-noise covariance matrix is one of the critical points. Incorporating a priori knowledge into STAP architectures can reduce the effect of the heterogeneous environment and substantially improve the estimation accuracy of the covariance matrix. Besides the prior information, the persymmetric structure in radar systems with symmetric spaced linear array and constant pulse repetition interval can also be exploited to improve the STAP performance. In this paper, we present a new computationally adaptive knowledge-aided STAP method that requires fewer samples by utilizing the persymmetric structure of the covariance matrix. In addition, based on the covariance matrix estimation technology of the newly proposed knowledge-aided STAP method, two knowledge-aided persymmetric adaptive detectors in the nonhomogeneous environment are proposed as well. First, a two-step design procedure-based detector is proposed for the partially homogeneous model, which is called knowledge-aided persymmetric adaptive coherence estimator. Second, we improve the stochastic heterogeneous model and propose a new knowledge-aided persymmetric generalized likelihood ratio test for this model. Finally, simulation results confirm the effectiveness of the proposed methods.
Deep Siamese Network for Multiple Object Tracking

Multiple object tracking is an important but challenging computer vision task. Thanks to the significant progress in object detection field, tracking-by-detection becomes a trending paradigm for tracking multiple objects at the same time. Appearance models are also widely used for associating detection results. In this paper, we combine cosine similarity metric learning with very deep convolutional neural network, yielding a robust appearance pairwise matching model: a deep Siamese network capable of re-identifying the same object after a long time and dealing with partial and complete occlusion. Embedded in existing tracking algorithms, our model is a lightweight but powerful module for decision-making among track hypotheses. Experiments on MOT Challenge 2016 benchmark [1] demonstrate the effectiveness of our model, which achieves state-of-the-art performance without delving into extensive hyper-parameter tuning.
Iterative Detection and Phase-Noise Compensation for Coded Multichannel Optical Transmission

The problem of phase-noise compensation for correlated phase noise in coded multichannel optical transmission is investigated. To that end, a simple multichannel phase-noise model is considered and the maximum a posteriori detector for this model is approximated using two frameworks, namely factor graphs (FGs) combined with the sum–product algorithm (SPA) and a variational Bayesian (VB) inference method. The resulting pilot-aided algorithms perform iterative phase-noise compensation in cooperation with a decoder, using extended Kalman smoothing to estimate the a posteriori phase-noise distribution jointly for all channels. The system model and the proposed algorithms are verified using experimental data obtained from space-division multiplexed multicore-fiber transmission. Through Monte Carlo simulations, the algorithms are further evaluated in terms of phase-noise tolerance for coded transmission. It is observed that they significantly outperform the conventional approach to phase-noise compensation in the optical literature. Moreover, the FG/SPA framework performs similarly or better than the VB framework in terms of phase-noise tolerance of the resulting algorithms, for a slightly higher computational complexity.
Traffic Police Gesture Recognition using RGB-D and Faster R-CNN

How to make self-driving cars understand the traffic gestures of traffic police is crucial for driverless, especially in China there are many police to help the traffic move smoothly and quickly at different intersection in rush hours. Faster R-CNN in deep learning is a mainstream method, however, has a low recognition rate in the case of complex backgrounds. In order to improve the recognition accuracy under complex environment, a two-stream Faster R-CNN based on color and depth data is proposed in this paper. Depth channel information is used to combine with RGB channel information at the feature level. RGB channel information is integrated with Depth channel information based on Faster R-CNN and RGB-D. Experimental results show that this method is more advantageous than the Faster R-CNN using only RGB data.
A Self-Packaged Circularly Polarized Dielectric Resonator Antenna With Wide Bandwidth and High Gain

A novel self-packaged circularly polarized (CP) dielectric resonator antenna with wide bandwidth and high gain is proposed. To obtain a low-profile antenna structure, one strip dielectric resonator (DR) is integrated entirely inside the hollow region of the other two-layer DR. Two coupled CP modes from the composite DR structure are simultaneously excited, constituting a wide operating bandwidth. The proposed antenna also has a high-radiation gain by taking advantages of the substrate integrated waveguide (SIW) based air cavity and the high-gain nature of the adopted two-layer DR. Measured results of the antenna prototype indicate that the design can achieve an overlap bandwidth of 28.6%, with a peak gain of 11.3 dBi.
Investigation of Sputtered Oxides and p+ Back-contact for Polycrystalline CdTe and CdSeTe Photovoltaics

Adding a thin layer of Al2O3 to the back of CdSeTe/CdTe devices has previously been shown to passivate the back interface and drastically improve surface recombination lifetimes. Using such a structure, lifetimes of over 400 ns have been recorded. Despite this, such devices do not currently show an improvement in open-circuit voltage (VOC) that is commensurate with this effect. Devices were fabricated using a range of Al2O3 thicknesses at the back interface. High efficiency devices exceeding 16.4% were fabricated with 0.5 nm Al2O3.
Classification of EEG Records for the Cursor Movement with the Convolutional Neural Network

Nowadays, very successful results are obtained with deep learning architectures which can be applied to many fields. Because of the high performances it provides in many areas, deep learning has come to a central position in machine learning and pattern recognition. In this study, electroencephalogram (EEG) signals related to up and down cursor movements were represented as image pattern by using obtained approximation coefficients after wavelet transform. The Obtained image patterns were classified by applying Convolutional Neural Network. In this study, EEG records related to cursor movements were classified and classification accuracy was obtained as 88.13%.
V Score —A Data Analytical Versatility Metric For Cricket

In this paper we introduce a novel metric to quantify the versatility of a cricketer across multiple formats of the game. The proposed Versatility Score (V Score) is derived from a Performance Matrix developed by applying exploratory data analysis and k-means clustering to player statistics data between 2008 and 2016. The metric enables us to re-engineer the current player evaluation system rating which does not account for a cricketer's ability to adapt to multiple formats of the game. The newly developed formula shows promising results by performing better than the rating in the current system in terms of the correlation coefficient obtained between the win/loss margin of a team and its rating taken as the average player rating. We further show an interesting insight in the effect of high intensity matches, specifically those between India and Pakistan, on the correlation coefficient obtained between team rating and win/loss margin. The proposed Versatility Score metric can be applied to take the field of cricket analytics further, especially as a tool for decision making in team selection.
Intelligent Malware Detection using Oblique Random Forest Paradigm

With the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.
On the Use of Artificial Noise for Secure State Estimation in the Presence of Eavesdroppers

The problem of remote state estimation in the presence of eavesdroppers has recently been investigated in the literature. For unstable systems it has been shown that one can keep the expected estimation error covariance bounded, while the expected eavesdropper error covariance becomes unbounded in the infinite horizon, using schemes based on transmission scheduling. In this paper we consider an alternative approach to achieve security, namely injecting noise into sensor transmissions, similar to the artificial noise technique used in physical layer security for wireless communications. Numerical results demonstrate significant performance improvements using this approach, with respect to the trade-off between the expected estimation error covariance and expected eavesdropper covariance.
Stroke Risk Assessment Using Ridge Regression Model

Stroke is a cerebrovascular ailment that is one of the significant causes of adult impairment in most of the developing countries. Prediction of stroke during initial stages is a major requirement in the treatment of this disease. A ridge regression analysis has been implemented in this research work to develop a predictive model for stroke to address the overfitting problem by a adaptable shrinkage approach. Bootstrap validation has been effectively implemented for the validation of the devised regression model.
A Predictive Control Strategy for Mitigation of Commutation Failure in LCC-Based HVDC Systems

High-voltage direct-current (HVDC) systems are being widely employed in various applications because of their numerous advantages such as bulk power transmission, efficient long-distance transmission, and flexible power-flow control. However, line-commutated-converter-based HVDC systems suffer from commutation failure, which is a major drawback, leading to increased device stress and interruptions in transmitted power. This paper proposes a predictive control strategy, deploying a commutation failure prevention module to mitigate the commutation failures during ac system faults. The salient feature of the proposed strategy is that it has the ability to temporarily decrease the firing angle of thyristor valves depending on the fault intensity to ensure a sufficient commutation margin. In order to validate the performance of the proposed strategy, several simulations have been conducted on the CIGRE Benchmark HVDC model using PSCAD/EMTDC software. Additionally, practical performance and feasibility of the proposed strategy are evaluated through laboratory testing, using the real-time Opal-RT hardware prototyping platform. Simulation and experimental results demonstrate that the proposed strategy can effectively inhibit the commutation failure or repetitive commutation failures under different fault types, fault impedances, and fault initiation times.
Government's Digital Surveillance and Citizens' Self-Censorship of Technology Use

How does a government’s covert digital surveillance of its citizens’ private communications affect the citizens’ digital technology usage behaviors? Building on the “chilling effects theory,” we expect covert digital surveillance to increase citizens’ risk appraisals about sharing sensitive information in the digital medium and lead to self-restrictions and self-censorship on the use of digital technologies. We also expect this relationship to be moderated by three personal characteristics of a citizen: (i) trust in government, (ii) privateness versus publicness, and (iii) awareness about government’s counterterrorism surveillance activities. Edward Snowden’s June 2013 revelations about the U.S. government’s covert digital surveillance program serve as an exogenous shock that could potentially change digital technology usage behaviors of U.S. citizens. To test our hypotheses, we use a dataset that was collected in the aftermath of the Snowden’s 2013 revelations, in four waves of surveys of a nationally representative panel of 607 U.S citizens. We find that U.S. citizens significantly changed their use of digital technologies in the aftermath of the Snowden revelations. They self-censored the sensitive topics and keywords in online searches, and self-imposed various restrictions on the use of a variety of digital technologies. A citizen’s trust in government and awareness about the counterterrorism focus of the government’s surveillance activities reduced the self-censorship behaviors. Privateness of the citizen increased self-censorship further. We discuss the implications of these findings for IS research and practice.
Hardware Design and Implementation of Pseudorandom Number Generator Using Piecewise Linear Chaotic Map

There are many applications, such as Monte-Carlo simulations, testing, banking, require very high run-times and secured random generator. Due to bitwise operations, hardware based random number generators are more efficient than software based. An FPGA based cryptographically secured pseudorandom number generator is proposed in this paper. This method uses a piecewise linear chaotic map along with linear congruential generator to enhance the performance. It is used to shuffle the bits of the input initial vector. The chaotic map shows good bifurcation and has no periodic window. Piecewise linear chaotic maps is defined in analogue space but FPGA is a digital device, hence in order to implement the chaotic map on FPGA it is digitalized. The FPGA implementation not only increases the speed but also the security. Implementing the algorithm on FPGA was a challenge due to the finite precision problem. In this paper the hardware architecture is implemented using VHDL language and synthesized on Xilinx Virtex-2 FPGA. Simulation result of the proposed algorithm show that the maximum clock frequency is of 373.218 MHz. The method yields a pseudorandom bit sequence which successfully passed the rigorous National Institute of Standards and Technology (NIST) test.
Deep level transient spectroscopy characterization of BaSi2 light absorbers

In this study, we investigated defect levels and their densities by deep-level transient spectroscopy (DLTS). In BaSi<inf>2</inf> epitaxial films, there is a hole trap with a density of N<inf>T</inf> = 1013 cm<sup>−3</sup> at 0.27 eV from the valence band edge. This is interpreted to originate from Si vacancies. We also discuss the minimum lifetime of BaSi2 from the viewpoint of the Shockley-Read-Hall recombination model.
An Overview of Recent Advances in Assessing and Mitigating the Face Morphing Attack

The face morphing attack enables the illegitimate sharing of photo-ID documents intended for identity verification. Multiple users may use the same passport, driver license or health insurance card without being condemned. This paper summarizes recent advances in protecting the photo-ID-based verification from the morphing attack. We explain the attack along with the standard approach of creating morphed face images. We identify research gaps and open challenges by summarizing studies assessing the potential of the morphing attack as well as studies concerned with generating databases of morphed face images and examining the performance of morphing detectors. We discuss new performance metrics looking for conformity with the standard on presentation attack detection. Based on the current advances, we recommend technical and organizational security mechanisms to mitigate or even prevent the morphing attack.