Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Local Backscattering Estimation for Underwater Image Enhancement

Due to absorption and scattering effects the under-water scenes are characterized by poor contrast, color shifting, additional noise and hazy appearance. In this paper we introduce a novel solution that estimates locally the backscattered light. While in general the existing solutions estimate a global backscattered light value over the entire scene, our local strategy is able to deal effectively to the more challenging non-uniform illumination generated by multiple light-sources. Our solution computes two complementary estimates of the local backscattered light, covering a large and a small patch size. The optimal local backscattered light is computed as the mean of the outputs processed with the small and the large patches while the transmission map, is estimated based on the dark-channel prior (DCP) [1]. Finally, our restored results are computed by simply inverting the optical model using the transmission and the local backscattered light estimates. The qualitative evaluation demonstrates the effectiveness of our approach compared with the recent underwater enhancing techniques.
Developments and applications of underwater LiDAR systems in support of marine science

Light Detection and Ranging (LiDAR) has been used extensively to accumulate high-resolution topographical data in air. Over the last few decades, the technology has been extended to capture bathymetric data in coastal waters. With a large portion of the ocean unmapped, there is opportunity for technology advancement to deliver improved quality and efficiency in the mapping of shallow water regions. This paper assesses existing technology and the history of underwater LiDAR profiling and bathymetric mapping to identify potential opportunities for future growth. Alternative uses for laser ranging systems, both subsea and in-air will drive expected system specifications. The findings assist with classifying important design choices that drive the functionality and suitability of a LiDAR for different marine science applications.
Novel Heterogeneous Attocell Network Based on the Enhanced ADO-OFDM for VLC

In this letter, an enhanced asymmetrically clipped dc-biased optical orthogonal frequency-division multiplexing (ADO-OFDM) scheme is proposed with asymmetrically clipped optical OFDM for the local network and dc-biased optical OFDM for the global network to avoid co-channel interference. Based on that, a novel visible light communication attocell adopting heterogeneous networks with optical access point using two light-emitting diode sources with different transmitter semiangles can be adopted. The signal-to-noise ratio and the average capacity are then investigated and formulated. Simulation results show that the average achievable capacity region of the enhanced ADO-OFDM can be greatly increased compared with that of the conventional ADO-OFDM.
Second-Order N-path Notch Filter for Hum Noise Suppression

Hum noise activity such as power line noise is one of critical problems in biomedical signals processing. Various approaches utilizing both analog and digital techniques have been proposed. However, these approaches have some disadvantages. This paper proposes the use of N-path notch filter as a hum noise filter. The notch depth in a conventional N-path notch filter is limited by the number of path and off-resistance. A new N-path notch filter with second-order resistor equivalent switched capacitor is proposed. Simulation results show that with resistor equivalent switched capacitor circuit improves the notch depth by 63.67dB with an off-resistance of 200MΩ.
Permutated Symmetric Key for Perfect Lightweight Image Encryption

Internet advancements and the arrival of new embedded gadgets have radically raised the information sharing in the form of images. In turn, the encryption algorithms have been widely used to secure images. Most of these image encryption algorithms have to run on resource-constrained devices that demand less computational complexity. In this regard, the algorithm proposed in this paper satisfies the need for a computationally less complex image encryption process with its simpler and efficient key generation procedure. The analysis of the implementation results in terms of statistical parameters in addition to its ability to withstand differential attacks validates the security level of the proposed Iightweight image encryption algorithm.
Sub-System SVD Hybrid Beamforming Design for Millimeter Wave Multi-Carrier Systems

In this paper, the hybrid beamforming design for multiple-input multiple-output orthogonal frequency-division multiplexing systems is studied over the indoor millimeter wave (mm-wave) channels. Under practical hybrid beamforming constraints for multi-carrier systems and the study of sub-systems, we propose the sub-system SVD (SS) hybrid beamforming design. To alleviate the complexity problem of channel estimation, reduction of the amount of the required channel state information (CSI) is also studied based on the statistical properties of indoor mm-wave channels. The SS hybrid beamforming algorithm is then extended to the limited feedback SS (LSS) scheme in which the transmit beamformers are designed based on limited information fed back from the receiver. A fast codeword selection algorithm is also developed to reduce the search complexity of the LSS algorithm. The simulation results show that our SS algorithm achieves the performance level of traditional full-digital beamforming with low complexity and the LSS algorithm has better performance and lower complexity than the previous algorithm, even if the amount of CSI is greatly reduced.
Low-Complexity Coverage Analysis of Downlink Cellular Network for Combined LOS and NLOS Propagation

Recent works on coverage analysis distinguish between the line-of-sight (LOS) and non-LOS links, as they have a significant channel-gain difference. However, analyzing the effect of complex wireless channels while distinguishing between them requires highly complex computation. Herein, we propose an interference approximation method to reduce the computational complexity of such analysis comparing with previously proposed ones. Furthermore, this proposal enables coverage analysis with an only one-fold integral expression while ensuring accurate analysis for various blockage models and parameters, which is validated through simulation.
Non-Uniform Burst-Sparsity Learning for Massive MIMO Channel Estimation

We address the downlink channel estimation problem for massive multiple-input multiple-output (MIMO) systems in this paper, where the inherit burst-sparsity structure is exploited to improve the channel estimation performance. In the literature, the commonly used burst-sparsity model assumes a uniform burst-sparse structure in which all bursts have similar sizes. However, such assumption is oversimplified to hold in practice. Outliers deviated from such uniform burst structures can significantly degrade the accuracy of the existing burst-sparsity models, which may result in a reduced recovery performance. To capture a more general burst-sparsity structure in practice, we propose a novel non-uniform burst-sparsity model and introduce an improved pattern-coupled prior to account for more realistic non-uniform burst structures. A generic sparse Bayesian learning based framework to exploit the non-uniform burst-sparsity and to enhance massive MIMO channel estimation performance is then developed. We further prove that our solution converges to a stationary point of the associated optimization problem, and our framework includes the state-of-the-art pattern-coupled method as a special case. Simulation results verify the robust performance of the devised method.
[Journal First] What Makes a Great Manager of Software Engineers?

Having great managers is as critical to success as having a good team or organization. A great manager is seen as fuelling the team they manage, enabling it to use its full potential. Though software engineering research studies factors that may affect the performance and productivity of software engineers and teams (like tools and skill), it has overlooked the software engineering manager. On the one hand, experts are questioning how the abundant work in management applies to software engineering. On the other hand, practitioners are looking to researchers for evidence-based guidance on how to manage software teams. We conducted a mixed methods empirical study to investigate what manager attributes developers and engineering managers perceive important and why. We present a conceptual framework of manager attributes, and find that technical skills are not the sign of greatness for an engineering manager. Through statistical analysis we identify how engineers and managers relate in their views, and how software engineering differs from other knowledge work groups.
A Transient-Enhanced Digital Low-Dropout Regulator with Bisection Method Tuning

This paper presents a digital low-dropout regulator (DLDO) with bisection method (BM) tuning for fast transient response. Different from conventional DLDOs, the proposed DLDO employs adders instead of shift registers to tune binary distributed PMOS power switches. When a load-transient is detected, the BM tunes power switches within constant steps to stabilize the output voltage in a short setting time. To improve the voltage undershoot induced by fluctuating load, an additional analog assistant (AA) technology is adopted. The proposed DLDO is designed and simulated in 180nm CMOS process. A maximum setting time of 3.3us with only 9μA quiescent current and the voltage undershoot/overshoot of 187/43mV is obtained with an 18mA/1ns load step and a total capacitor of 100pF. The resulting figure of merit is 0.42ps.
Latency Analysis of Wireless Networks for Proximity Services in Smart Home and Building Automation: The Case of Thread

Proximity service (ProSe), using the geographic location and device information by considering the proximity of mobile devices, enriches the services we use to interact with people and things around us. ProSe has been used in mobile social networks in proximity and also in smart home and building automation (Google Home). To enable ProSe in smart home, reliable and stable network protocols and communication infrastructures are needed. Thread is a new wireless protocol aiming at smart home and building automation (BA), which supports mesh networks and native Internet protocol connectivity. The latency of Thread should be carefully studied when used in user-friendly and safety-critical ProSe in smart home and BA. In this paper, a system level model of latency in the Thread mesh network is presented. The accumulated latency consists of different kinds of delay from the application layer to the physical layer. A Markov chain model is used to derive the probability distribution of the medium access control service time. The system level model is experimentally validated in a multi-hop Thread mesh network. The outcomes show that the system model results match well with the experimental results. Finally, based on an analytical model, a software tool is developed to estimate the latency of the Thread mesh network, providing developers more network information to develop user-friendly and safety-critical ProSe in smart home and BA.
An effective implementation of Gaussian of Gaussian descriptor for person re-identification

Person re-identification (ReID), a critical task in surveillance systems, has obtained impressive advances in recent years. However, most current works focus on improving the person re-identification accuracy. In practical terms, the direct use of these works seems difficult, even infeasible. Among the features proposed for person representation in person ReID, Gaussian of Gaussian (GOG) has been proved to be robust. Towards applying this feature for practical usage, in this work, we simultaneously propose two improvements. First, we re-implement and perform intensive experiments to select the optimal parameters of GOG during feature extraction. Second, we propose and apply preprocessing techniques on person images. The experimental results show that the proposed approach allows to extract GOG 2 times faster than the available source code and achieve remarkably high accuracy for person ReID. The obtained accuracies at rank-1 on VIPeR dataset are 51.74% (with background) and 57.25% (without background). The implementations and evaluation datasets used in this paper are made publicly available.
Optimization of M-of-N detectors in heavy-tailed noise

When multiple observations are available to a detector, choosing the signal-present hypothesis when $M$ of the $N$ observations individually support the decision is a means for both simplifying decision fusion and countering intermittent interference. Design of the detectors requires choosing a single-observation decision threshold, the minimum required number of single-observation detections (${M}$), and in some scenarios the number of observations used in the final decision (${N}$). Given $N$, empirical formulae are presented to approximate the optimal value of $M,$ which minimizes the signal-to-noise power ratio required to achieve an operating point, for two scenarios involving heavy-tailed noise. The first case involves a K-distributed background and a minimax optimal value of $M$ chosen to work over a range of tail heaviness. The second case is for a Gaussian-distributed background where a cell- averaging normalizer is used, which results in $F$-distributed single-observation decision statistics. The empirical formulae depend on $N$, the detector operating point, and for the latter case the number of independent auxiliary data samples used by the normalizer to estimate the background noise power. Two false-alarm performance metrics are considered: the standard probability of false alarm and the average number of samples between false alarms when the M-of-N detector is applied to a time series sequentially to detect a signal with unknown starting time. The proposed approximations to the optimal $M$ had error magnitude less than or equal to one at least for $N \in [3$, $78]$ for a wide range of operating points and all scenarios evaluated.
Guaranteed Performance of Nonlinear Attitude Filters on the Special Orthogonal Group SO(3)

This paper proposes two novel nonlinear attitude filters evolved directly on the special orthogonal group $\mathbb {SO}\left ({3}\right)$ , able to ensure prescribed measures of transient and steady-state performance. The tracking performance of the normalized Euclidean distance of attitude error is trapped to initially start within a large set and converge systematically and asymptotically to the origin from almost any initial condition. The convergence rate is guaranteed to be less than the prescribed value, and the steady-state error does not exceed a predefined small value. The first filter uses a set of vectorial measurements with the need for attitude reconstruction. The second filter does not require attitude reconstruction and instead uses only a rate gyroscope measurement and two or more vectorial measurements. These filters provide good attitude estimates with superior convergence properties and can be applied to measurements obtained from low-cost inertial measurement units. The simulation results illustrate the robustness and effectiveness of the proposed attitude filters with guaranteed performance considering high level of uncertainty in angular velocity along with body-frame vector measurements.
Semi-Global Robust Stabilization for a Class of Nonlinear Systems With Uncertain Measurement Functions

This paper considers the problem of semi-global stabilization via linear feedback for a class of uncertain nonlinear systems. Different from the existing results, the systems considered here have uncertain measurement functions due to the sensors’ property. By generalizing the notion of homogeneity with monotone degrees to the uncertain case, a measurement feedback controller is recursively constructed to semi-globally asymptotically stabilize the system by appropriately choosing a series of Lyapunov functions as well as the corresponding level sets. The proposed control scheme not only performs in a linear form for better implementation but also leads to robustness to different sensors. Finally, a numerical example is presented to demonstrate the effectiveness of the control law.
An Improved Algorithm for Ship Target Detection in SAR Images Based on Faster R-CNN

Ship detection is of great value for fishing activity control, military defense, maritime transport, etc. Satellite-based synthetic aperture radar (SAR) can provide high-resolution images, allowing surveillance over massive water bodies to be possible. However, traditional ship detection algorithms like CFAR (Constant False-Alarm Rate), cannot produce convincing results. In recent years, detectors based on convolutional neural networks have made great progress, and among them, Faster R-CNN is one of the best in performance. In this paper, we propose an improved algorithm based on Faster R-CNN for ship target detection and adopt four strategies to improve the performance. The four strategies are replacing the VGG16 backbone network with ResNetl01, implementing online hard example mining, replacing the default non-max suppression algorithm with soft-nms, and changing the aspect ratio of the anchors. Experimental results show that our improved algorithm can boost the performance of the traditional Faster R-CNN detector (72.7% mAP) by about 5% in mAP (mean average precision) on the HRSC2016 (High Resolution Ship Collection) dataset, showing the effectiveness of the proposed approach.
Geomagnetically Induced Currents (GICs) in Equatorial Region

GICs is a ground end manifestation associated with the space weather perturbations that should be greatly taken into account by the society. Even though the GICs implication to the power system is not regularly happened, it can cause large scale of system failure. In equatorial, the power system is considered safe since the most intense of geomagnetic storm happened in high latitude. However, the internal damage due to GICs which finally led to the South African power system failure has totally changed the normal perception. Therefore, a preliminary investigation on the GICs activity in equatorial region is performed to understand the space weather impact to the power system. Time derivative of the horizontal magnetic field component (dB/dt) is done to indicate the GICs activity value based on Faraday’s law. All the reported power failures are compiled to produce the threshold value of dB/dt, which possibly cause the harmful effect to the system. Then, dB/dt analysis is extended to show the pattern of GICs activity in function of magnetic latitude and local time. The results reveal that power network in equatorial region has possibly suffered by GIC. Plus, high number of intense GIC activity in this region occurred during dayside.
Subjective Evaluation of a Noise-Reduced Training Target for Deep Neural Network-Based Speech Enhancement

Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of both systems were evaluated subjectively by a speech recognition test based on Hagerman sentences and a translation of the ITU-T P.835 recommendation, respectively. Results were compared with the objective measures STOI and POLQA. Neither STOI nor POLQA reliably predicted subjective results. While STOI anticipated improvement, subjective results for both models showed degradation of speech intelligibility. POLQA results were overall hardly affected, while the subjective results showed significant changes in overall quality, both positive and negative, in many of the tests. One of the systems was trained to remove all noise; a strategy that is common in speech enhancement systems found in the literature. The other system was trained to only reduce the noise such that the signal-to-noise ratio increased with 10 dB. The latter system subjectively outperformed the system that attempted to remove noise completely. From this, we conclude that objective evaluation cannot replace subjective evaluation until a measure that reliably predicts intelligibility and quality for deep-neural-network-based systems has been identified. Results further indicate that it may be beneficial to move away from more aggressive noise removal strategies toward noise reduction strategies that cause less speech distortion.
Secure Communication in Non-Geostationary Orbit Satellite Systems: A Physical Layer Security Perspective

Satellite communication systems serve as an indispensable component of wireless heterogeneous networks in 5G era for providing various critical civil and military applications. However, due to the broadcast nature and full accessibility of wireless medium, serious security threats exist from such systems. As an effort to address this issue, this paper, for the first time, investigates the secure communication in a non-geostationary orbit (NGSO) satellite system from a physical layer security perspective. Specifically, we focus on the downlink of an NGSO satellite which provides services to a fixed earth station and is wiretapped by a fixed eavesdropper. We first apply three types of orbiting models to characterize the movement state of the satellite. Based on the orbiting models, we then provide theoretical analysis for the secure communication performance of such a system. The expressions of two fundamental performance metrics, secrecy capacity and secrecy outage probability, are derived in a closed form for any system time. Finally, we conduct extensive simulations to validate our theoretical performance analysis and illustrate the security performance in a practical NGSO satellite communication system.
Growth, Microstructure, and Properties of GdBCO–Ag Superconductor

We prepared GdBa<sub>2</sub>Cu<sub>3</sub>O<sub>7−δ</sub> (Gd123, GdBCO) single-grain bulk with Ag addition via the top-seeded melt growth method. Peritectic temperatures of precursor powder mixture and pure Gd123 powder were tested by differential thermal analysis (DTA). The dependence of the volume fraction and the size of Gd<sub>2</sub>BaCuO<sub>5</sub> (Gd211) particles on the distance from the seed was determined with the help of image processing of the micrographs taken by a scanning electron microscope (SEM) from the polished a/c-section of the sample. The morphology of Ag particles was evaluated by the SEM image analysis. The substitution of Ag in Gd123 was characterized by the wavelength-dispersive spectrometry. The magnetic measurements of superconducting properties (critical current density <italic>J<sub>c</sub></italic> at 77 K and critical temperature <italic>T<sub>c</sub></italic>) were carried out using a vibrating sample magnetometer. Magnetization measurements were performed on the specimens taken from several different locations of the same crystal plane and the value of the maximum middle critical temperature (<italic>T<sub>c,m</sub></italic>) was determined as 92.6 K.