Miracle-dz/UniFAR-FTUs huggingface dataset:
An Underwater Image Enhancement Method for Simultaneous Localization and Mapping of Autonomous Underwater Vehicle

Simultaneous localization and mapping (SLAM) has been treated as the key technology of the autonomous robots. This paper takes the application of visual SLAM in autonomous underwater vehicle (AUV) positioning as background. Unlike atmospheric imaging, underwater medium has issues with low contrast and color distortion. Here an underwater image enhancement method is brought forward to settle this problem. In this method, the image was converted from red-green-blue (RGB) color space into hue-saturation-value (HSV) color space, and the irradiation image was achieved after the guided filer was conducted on value component. The reflection image was obtained according to Retinex theory, and the image was converted back to RGB space. Finally, a gamma correction was applied to strength the image contrast. In the experiment, the images were exploited to perform SLAM. The results indicated that the enhanced images perform better than the original images in feature points extraction and visual SLAM.
Dose Dependent Profile Deviation of Implanted Aluminum in 4H-SiC During High Temperature Annealing

The influence of the high temperature annealing on differently implanted Al profiles was investigated by SIMS measurements. Depending on the implanted dose and also depending on the local concentration a significant diffusion of the implanted Al was observed. Based on this results at least two necessary conditions of Al diffusion during high temperature annealing could be determined.
Design of Centralized Control Architecture for Distribution Network Communication Network Based on SDN

The rapid development of power Internet of Things has promoted the vigorous development of new services in distribution networks, but at the same time it has proposed higher differentiated performance requirements for communication networks. Software-defined network SDN technology is the current popular communication technology, which can separate the control and forwarding functions of the network. The former is attributed to the control layer, and the latter is still implemented by the underlying layer, thus achieving differentiated isolation of different types of services. However, the application of SDN technology in power communication network needs to be improved. Therefore, this paper designs the centralized control architecture of SDN-based distribution network communication network. The architecture adopts controller cluster technology, based on northbound and southbound interface schemes, and virtual power plants. As an example, the SDN-based communication resource management and control scheme is analyzed. In order to achieve reasonable allocation of network resources, centralized management and control, to solve the problem of access to business diversified communication methods to provide reliable support
Spatial visualization of uncertainty

Geospatial information has become more accessible since the early 2000s. Uncertainty has remained a constant in data, due to various factors, including scale and real world conceptualization. Geospatial products are frequently used to inform decision makers on key decisions, with little understanding of the quality of the data. However, accuracy assessments have improved significantly since the visual screening that was used in the 1950s, now providing statistics such as the Kappa coefficient, root mean square error (RMSE) and the confusion matrix. Two questions thus arise: 1) do those using the data inform themselves about the quality of data; and 2) can visualization of the uncertainty in spatial data aid in the communication of the data quality? This research was achieved in three tasks: 1) evaluate the South African perception on data quality; 2) develop an uncertainty visualization tool; 3) evaluate the uncertainty visualization tool. The first task was achieved through a quantitative survey of people working in the South African geospatial industry. Despite a limited response, the findings indicated that those working with geospatial data do not always seek to verify the quality of the data they are using. It also came to light that most of those who do not verify the quality of their data, would like to have the uncertainty in the data visualized. Task 2 aimed at developing a tool for the visualization of spatial uncertainty (Uview). Uview was based on the findings from Task 1 supplemented by recommendations from literature and other uncertainty visualization tools. The tool was developed for continuous raster datasets only and uses the z-score and modified z-score as its main statistics for visualization. Standard accuracy assessment statistics (global data quality statistics), such as RMSE and mean absolute error (MAE) have also been included in Uview to make it an accuracy assessment and uncertainty visualization tool for continuous raster data. Lastly Task 3, the evaluation of Uview was done using a two-pronged approach. The first part encompassed investigating the usability of the tool. In this phase the visualizations were used to derive relationships between digital elevation models (DEM), uncertainty and a watershed product. It was found that Uview does provide useful information, and watersheds are sensitive to deviations from true value at key locations more than the magnitude of the deviation. Stellenbosch University https://scholar.sun.ac.za
Compressive Focal Plane Array Imager Reconstruction Using Learning Based Regularization

In this paper, we develop a learning based regularization method for reconstructing compressive focal plane array imager (CFPAI). While many optimization algorithms employ proximal operators for regularization purposes such as total variation minimization, they are often inadequate to fully capture the likelihood of complex natural images. Recently, deep learning based approaches obtain promising results in different imaging problems, creating the possibility to use them as a regularizer in an optimization framework. Here, we utilize this approach in CFPAI obtaining spatially modulated and downsampled measurements of the incoming light intensity. We first formulate the problem of finding original high resolution image from its measurements as an optimization problem. Then, we solve the resulting problem using alternating direction method of multipliers (ADMM). In ADMM, we replace the proximal operator corresponding to the regularization function with a deep convolutional denoising network. Results show successful reconstruction performance in terms of reconstruction pSNR and visual quality even under significant noise levels.
EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning

We present EzPC, a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easy-to-write programs. EzPC provides formal correctness and security guarantees while maintaining performance and scalability. Previous language frameworks, such as CBMC-GC, ObliVM, SMCL, and Wysteria, generate protocols that use either arithmetic or boolean circuits exclusively. Our compiler is the first to generate protocols that combine both arithmetic and boolean circuits for better performance. We empirically demonstrate that the performance of the protocols generated by EzPC is comparable to or better than (in some cases upto 19x) their state-of-the-art, hand-crafted implementations, while EzPC protocols also outperform their boolean circuits only counterparts by as much as 25x.
Full Duplex DOCSIS: Opportunities and Challenges

The recently released full duplex extensions to the DOCSIS 3.1 standard (FDX DOCSIS) promise to greatly alleviate the shortage of upstream bandwidth that has plagued HFC networks since inception. However, there are a number of technical and implementation challenges in both the MAC and PHY layers of FDX DOCSIS that must be overcome in order for the standard to live up to its potential as a major enabler of next-generation services. Due to the relatively small and insular nature of the DOCSIS community, these challenges may not attract attention in the academic community commensurate with their importance. This article presents a high-level overview of FDX DOCSIS technology and describes some key technical challenges related to FDX DOCSIS which, if resolved appropriately, could provide significant value to the industry and society in general.
A DC Bus Signaling Based Autonomous Power Management Strategy for a Grid-connected PV-Battery System

Management of multiple energy sources without implementation of extra communication is challenge. This paper presents an autonomous power management strategy for a grid-connected PV-battery system. The DC-bus signaling method is used to achieve the power line communications so that the system can switch from the different working modes. The experimental results validated that with the proposed control strategy, the overall system can transit automatically between the different operation modes according to the availability of renewable energy, SOC of the battery, and the load. Furthermore, using the BDC as a power decoupling circuit, the bus voltage ripple can be further reduced as well.
Improvement of Underwater Acoustic Sensor Networks Performance with Fountain Codes

Prolonging the lifetime and providing a reliable communication performance of underwater acoustic sensor network (UASN) nodes are challenging issues due to the severe nature of the underwater acoustic channel. Traditional methods that ensure the reliability of communications by using retransmissions such as automatic repeat request (ARQ) negatively affect the lifetime of UASNs since they are not energy efficient. Fountain codes (FC), which is a forward error correction technique, aim to solve the energy efficiency problem of ARQ method by transmitting the original data frame and some redundant frames together in the network without using retransmissions. The number of redundant frames to be transmitted is dynamically adjusted to achieve a predetermined reliability criterion at the receiver node. In this study, an integer linear programming (ILP) method is proposed which maximizes the lifetime of UASNs by using FCs. Our results show that FC method provides better performance metrics (e.g., network lifetime, end-to-end delay, and energy consumption) than the traditional ARQ method.
Fuzzy Controller Optimization of Synchronous Belt Drive System Based on Improved whale Optimization Algorith

Synchronized belt drive system is a complex nonlinear system, and ordinary PID control is difficult to meet the performance requirements, so it is necessary to design a controller with better performance. In this paper, a fuzzy PID controller is designed, and an improved whale optimization algorithm with adaptive selection mechanism and adaptive convergence factor is proposed. The proposed algorithm is used to optimize the rule base of the fuzzy controller. Compared with the ordinary PID controller and the fuzzy PID controller optimized by whale optimization algorithms and the non-optimized fuzzy PID controller, the fuzzy PID controller optimized by improved whale optimization algorithms has smaller steady-state error and faster response speed, and can meet the actual engineering needs.
A Novel Nonlinearity Reduction Method in Disk Resonator Gyroscopes Based on Thevibration Amplification Effect

This paper shows a novel method to reduce electrostatic and capacitive nonlinearities in disk resonator gyroscopes (DRGs) via electrodes distribution optimization. Based on the vibration amplification effect in the DRG’s working mode, we found that by optimizing the positions of driving and sensing electrodes, nonlinearity of the DRG can be effectively reduced and oscillation amplitude x0 can be greatly improved to break the nonlinear limit. To verify this method, multi-layer driving electrodes are designed and effects of their positions on the DRG’s nonlinearity are analyzed and experimentally tested. The results show that x0 and sensitivity of the DRG can be increased significantly. Meanwhile, the angle random walk (ARW) and bias instability (BI) of the DRG can be reduced by 53% and 63% respectively.
Receiver Beacon Transmission Interval Design Using Machine Learning for Multi-Stage Wireless Sensor Networks

U-Bus Air is a protocol that can transmit packets by multi-hop communication, and is utilized in tall buildings and apartments. While U-Bus Air is a time asynchronous wireless sensor network, packet collisions cause degradation of communication quality as the number of terminals increases. A previous research has been proposed, an appropriate transmission cycle control algorithm based on the estimated collision probability by using collected topology information at a Fusion Center (FC). By using this algorithm, the communication performance is improved by feedback from FC to each terminal. However, its communication overhead becomes large due to topology information and feedback packets, and communication efficiency is degraded. In order to solve this problem, we propose a method to autonomously control receiver beacon transmission interval by leveraging decentralized Q-Learning. In this method, each terminal transmits information of a packet holding time at the same time as responding to the beacon. Finally, each terminal utilizes Q-Learning to derive an appropriate transmission interval. In computer simulation, we show an improvement of packet delivery rate (PDR), throughput and power consumption compared with the previous method.
Broadband Acoustical MEMS Transceivers for Simultaneous Range Finding and Microphone Applications

This work presents capacitive ultrasonic transceivers based on a dual-backplate MEMS microphone technology. The transducers exploit mechanical and acoustical system resonances in the low ultrasonic frequency range up to 100 kHz for both sending and receiving of ultrasonic signals. Requiring below 10V bias voltage and using standard MEMS microphone housings with dimensions of 4x3x1 mm³, the proposed system allows an integration into space and power critical systems like e.g. smartphones. While the ultrasonic transceiver functionality enables proximity sensing and presence detection applications, state-of-the-art audio performance of 68 dB(A) signal-to-noise ratio (SNR) is maintained.
Research of a Miniaturized Broadband Mems Phase Detector and its Temperature Effect for Application in Phase-Locked Loops

This paper presents the miniaturization of a broadband MEMS phase detector for X-band application and a new phase-locked loop system based on it. Miniaturization approach of capacitive loading is adopted and a size-reduction of 60% is achieved. Measured S-parameter shows return loss S11 smaller than −15 dB over X-band. Broadband property of phase detection is also verified through experiments. Temperature effect of the phase detector is studied. An integrated PLL circuit model is established with temperature compensation incorporated. Validity of the PLL is confirmed by simulation with respect to tracking and synchronization.
Vacuum Powered Pneumatic Actuator for Wearable Robots by the Kirigami of Polymeric Films

This paper shows vacuum powered pneumatic actuators designed for wearable robots using the Kirigami of polymeric thin films for the first time. Distinctive advancements have been achieved, including (1) lightweight and the flexible thin sheet of polymeric materials made by Kirigami designs to allow either bending or twisting movements; (2) capable of shape-transforming to the surrounding such as the attachments to the surface of the cloth. The design concept, material/fabrication process, and driving mechanism described in this paper can be potentially extended to a variety of flexible devices and soft robots.
High Sensitivity Surface Acoustic Wave Strain Sensor Based on PMN-PT Thick Film

In this paper, piezoelectric single crystal PMN-PT was processed by chemical and mechanical polishing method to form a surface acoustic wave (SAW) device for strain measurement. A wide measurement range and high sensitivity of this developed strain sensor were achieved, where the strain measurement range was 1000 με and the sensitivity achieved 1160.46 Hz / με. At the same time, the stability of SAW strain sensors was good. This small size (6.58 mm × 4.19 mm × 60 μm) and high sensitivity SAW strain sensor could be used for structural calibration, machine inspection, and aerospace detector in the future.
A Three-level Boost-Buck Converter for the Ultracapacitor Applications

A three-level boost-buck converter is proposed for ultracapacitor charging in vessel medium voltage direct current (MVDC) integrated power system (IPS). The topology, filters design, and control aspects are analyzed and discussed. A constant current control combining constant power control scheme is proposed to charge the ultracapacitor. The effectiveness of the proposed control scheme is verified by the simulation and hardware in the loop (HIL) experimental results.
A Novel UWB Filter with WLAN and RFID StopBand Rejection Characteristic using Tri-Stage Radial Loaded Stub Resonators

─ The paper presents a novel approach for designing compact ultra-wideband (UWB) band-pass filter with WLAN and RFID stop-band rejection characteristic which is obtained by using tri-stage radial loaded stub resonators. The main advantage of the proposed filter is that the Stopband can perfectly reject the WLAN and RFID (5.2GHz-6.1GHz) signals. The density and the equivalent model of one filter for testing the presented design is given depending on the odd/even excitation resonance condition. The characteristics of the filter are analyzed and discussed. To verify the proposed methods, two filters are designed and fabricated. Measured results show the proposed UWB properties from 3 GHz to 10.8 GHz and stop-band properties from 5.2 GHz to 6.1GHz. The designed filters can be integrated in UWB radio systems and can efficiently enhance the interference immunity from undesired signals such as wireless local area network (WLAN) and RFID. Index Terms Band-pass filter, dual-band, tristage radial loaded stub resonator, UWB, WLAN and RFID stop-band rejection
Incomplete-Market Prices for Real Estate First draft : October , 2011 Current version : June , 2012

This paper reconsiders the predictions of the standard real options models for real estate in the context of incomplete markets. We value vacant land as a European call option on a building that could be built on that land. Since we relax the completeness assumption of the Black-Scholes (1973) model, it is no longer possible to construct a replicating portfolio to price the option. To mitigate this problem, we use the good-deal bounds technique and arrive at closed-form solutions for the price of the real option. We determine an upper and a lower bound for the price of this option and find that, contrary to standard option theory, increasing the volatility of the underlying asset doesn’t necessarily increase the option value (the value of the vacant land). The lower bound option prices are always a decreasing function of the volatility of the underlying asset, which cannot be explained by a BlackScholes (1973) type of argument. This is however consistent with the presence of unhedgeable risk on the incomplete market, additional to the hedgeable component found on the complete market of the standard real options models. JEL codes: C02, D40, D52, D81
Data-driven Decision-making and the Role of Personality and Cognitive Style : A Systematic Literature

Data-driven decision-making (DDDM) is said to have huge benefits for organizations. Data are generally kept in IS, and humans use these data for informed decision-making. Therefore, DDDM is fundamentally related to the adoption and use of IS by individuals. IT adoption and use is influenced by personality and cognitive style, but not much is known on how personality and cognitive style influence the adoption and use of data-driven decision support systems. Our study aims to further our understanding of this influence. Through a structured literature review of IS literature, 55 papers were found and analyzed, resulting in 14 relevant topics and 7 key papers. Our findings indicate that personality and cognitive style may influence the adoption and use of data-driven decision support systems. We provide implications for future research to obtain a better understanding of DDDM, and practitioners can use our results to realize more successful DDDM.
Chapter 11 The Menopause and Oxidative Stress

Reproductive aging resulting in menopause is characterized by the permanent cessation of ovarian follicular activity. The signs and symptoms resulting from estrogen withdrawal can significantly disrupt a woman’s activities of daily living and sense of well being, while predisposing them to osteoporosis and heart disease. Current medical therapies are targeted at symptomatic relief or alleviating the hormonal deficiency itself to prevent its harmful sequelae. The progressive loss of estrogen and its protective effects, combined with deficient endogenous antioxidant, results in oxidative stress—which is implicated in the pathogenesis of vasomotor disturbances, loss of bone mass and heart disease in menopause. The link between oxidative stress and estrogen deficiency has been demonstrated by numerous studies. Based on this, hormonal replacement therapy, antioxidant supplementation, and lifestyle modification have been investigated for their efficacy and safety in the treatment and prevention of menopause-related symptoms and chronic disease processes.
Aphasia Using Transcranial Direct-Current Stimulation to Treat Stroke Patients With

Copyright © 2010 American Heart Association. All rights reserved. Print ISSN: 0039-2499. Online Stroke is published by the American Heart Association. 7272 Greenville Avenue, Dallas, TX 72514 DOI: 10.1161/STROKEAHA.109. 576785 published online Apr 15, 2010; Stroke Julie M. Baker, Chris Rorden and Julius Fridriksson Aphasia Using Transcranial Direct-Current Stimulation to Treat Stroke Patients With http://stroke.ahajournals. org located on the World Wide Web at: The online version of this article, along with updated information and services, is
Challenges Faced By the Youth in Promoting Peace through Sport in Kibera and Mathare Slums , Kenya

Sport can make a considerable contribution to peace building and conflict prevention especially in the areas of acceptance, social cohesion and building inter-group relations and understanding. It can transcend cultural, religious and ethnic divides. With its ability to bring people together, sport is a significance delivery mechanism for reconciliation and peace building programming. Though its benefits are significant in the community, development of sport experiences is hampered by challenges. This paper analyzes the challenges faced by the youth in promoting peace through sport in Kibera and Mathare informal settlements.
Entire Gabor Subspace Approach for Expression Recognition Using Locality Preserving Projection

This work presents the extraction of entire Gabor features for efficient expression recognition and classification. Phase information available in Gabor filter bank is not properly utilized in several existing works for face and expression recognition. In this work both Gabor magnitude feature vector (GMFV) and Gabor phase congruency vectors (GPFV) are projected separately by subspace methods with respect to preserving non redundant data and reducing redundant coefficients. Locality preserving projection (LPP) subspace method is used for preserving and projecting the Gabor vector feature space. Projected vectors are normalized and fused. This EGLPP approach is tested with Yale and FD database respectively. Proposed approach improves the recognition rate while compared with EGPCA, EGICA and EGKPCA approaches. Support vector machine classifier is used for expression classification. Keywords— Principal components, subspace, Gabor filter, locality preserving projection