Miracle-dz/UniFAR-FTUs huggingface dataset:
Cybersecurity in Medical Private Practice: Results of a Survey in Audiology

Despite well-documented cyber threats to patients' protected health information (PHI), sparse evidence exists about the state of cybersecurity behavior of health care workers and medical private practices. There is evidence of insecure behavior in hospital settings, even though specific insights about private practice are still absent. In addition to mandatory standards for securing PHI, such as the Health Insurance Portability & Accountability Act (HIPAA), small business viability and their patients' security and privacy are critically dependent upon technology availability and reliability. In this survey of 131 clinical audiologists we show that many lack time, staff expertise, or funds to deploy adequate cybersecurity that prevents and mitigates threats to security and privacy. We find widespread deployment of HIPAA-compliant cybersecurity, including antivirus software and individual logins. Only 9.9% of participants reported at least one data breach in 2019, significantly less than the average for small businesses and health care providers, and only 24.4% reported having cyber insurance. Practice owners view patient data as well protected and unlikely victims for cyber attacks and breaches. These results have important implications for cybersecurity products and services, and to medical professionals who must acknowledge the acute importance of cybersecurity in securing protected health information and mitigating risks. Small business private practice health care providers who are particularly sensitive to the impacts of cyber attacks and must prioritize and adopt countermeasures that decrease the risks to patients and their own businesses.
Scalable Point Cloud-based Reconstruction with Local Implicit Functions

Surface reconstruction from point clouds has been a well-studied research topic with applications in computer vision and computer graphics. Recently, several learningbased methods were proposed for 3D shape representation through implicit functions which among others can be used for point cloud-based reconstruction. Although delivering compelling results for synthetic object datasets of overseeable size, they fail to represent larger scenes accurately, presumably due to the use of only one global latent code for encoding an entire scene or object. We propose to encode only parts of objects with features attached to unstructured point clouds. To this end we use a hierarchical feature map in 3D space, extracted from the input point clouds, with which local latent shape encodings can be queried at arbitrary positions. We use a permutohedral lattice to process the hierarchical feature maps sparsely and efficiently. This enables accurate and detailed point cloud-based reconstructions for large amounts of points in a time-efficient manner, showing good generalization capabilities across different datasets. Experiments on synthetic and real world datasets demonstrate the reconstruction capability of our method and compare favorably to state-of-the-art methods.
Design of high gain Low Noise Amplifier for RF Receiver Systems

The number of Radio Frequency elements in wireless communication are increasing day to day , as number of radio frequency devices increases the amount of power consumption and level of noise increases at the RF Receiver, Further the Low Noise Amplifier is used to increase the strength of weak RF signal and is helpful for amplification of signal, Therefore the LNA plays an important role in Wireless RF communication system. LNA is able to remove the noise present in the signal. The Proposed LNA design aims to meet the desired improvements like high gain, low noise, low power consumption. The proposed design obtained with the results of gain is 34.34dB, Noise figure is 3.65dB, and power consumption is 4.5mw.
Review of Isolated DC Microgrid Coalescing Maximum Power Point Tracking and PV Array to Produce Electric Energy

Isolated DC microgrid is independent of utility grid. It consists of distributed energy resource (DERs) as Photovoltaic (PV) solar array along with battery and connected loads. The objective of this concept is to cater the growing energy demand. In country like India, till now conventional energy resources have been used which are limited deposits. To equitable the rising demand for energy the best way is to switch with renewable resources for energy generation. Also in the obscure areas where it is nearly impossible to transmit electricity, this concept of isolated dc microgrid to electrify the remote areas can be implemented. Another advantage is that; it earns carbon credits. The entire system incorporates PV array along with Maximum Power Point Tracking (MPPT) algorithm and battery to maintain the voltage level since the nature of solar is intermittent in nature as the sun's radiance fluctuates every time. This is achieved by using MPPT charge controller. The electronically operating dc to dc converter is MPPT, which stands for Maximun Power Point Tracking. The energy derived from solar and battery is enhanced by MPPT controller. It also protects the battery from overcharging thus ensuring battery protection. The voltage obtained from MPPT is dc voltage and to run the ac load inverter circuit is used. This paper provides MPPT design using the perturb and observe method.
Improving the representation of radiation interception and photosynthesis for climate model applications

The Joint UK Land Environment Simulator (JULES) (which is based on MetOffice Surface Exchange Scheme MOSES), the land surface scheme of the Hadley Centre General Circulation Models (GCM) has been improved to contain an explicit description of light interception for different canopy levels, which consequently leads to a multilayer approach to scaling from leaf to canopy level photosynthesis. We test the improved JULES model at a site in the Amazonian rainforest by comparing against measurements of vertical profiles of radiation through the canopy, eddy covariance measurements of carbon and energy fluxes, and also measurements of carbon isotopic fractionation from top canopy leaves. Overall, the new light interception formulation improves modelled photosynthetic carbon uptake compared to the standard big leaf approach used in the original JULES formulation. Additional model improvement was not significant when incorporating more realistic vertical variation of photosynthetic capacity. Even with the improved representation of radiation interception, JULES simulations of net carbon uptake underestimate eddy covariance measurements by 14%. This discrepancy can be removed by either increasing the photosynthetic capacity throughout the canopy or by explicitly including light inhibition of leaf respiration. Along with published evidence of such inhibition of leaf respiration, our study suggests this effect should be considered for inclusion in other GCMs.
Application of Adaptive Back-Propagation Neural Networks for Parkinson’s Disease Prediction

Parkinson’s disease (PD) is a common neurodegenerative disease that has affected millions of people worldwide and is more prevalent in older people. Early detection of this disease remains a challenging task. Recently, vocal and speech data are widely used to detect this disease. In this study, we proposed two classification schemes for improving the identification accuracy of PD cases from voice measurements. First, we applied a variable adaptive moment-based backpropagation algorithm of ANN called BPVAM. Then, we investigated the combination of dimensionality reduction method using principal component analysis (PCA) with BPVAM for classification of the same dataset. The main objective was improving the prediction of PD in the early stages by increasing the sensitivity of the system to dealing with data in its fine detail. In experiments, it was proved that robustness of the system was improved by including features with largest variances using PCA which helped the model to learn the patterns earlier in the training process. Results indicated that BPVAM-PCA was relatively more effective than BPVAM. In addition, these methods were also compared with some other well-known algorithms.
Exploring the Design Efficiency of Random Microfluidic Mixers

With the development of microfluidic technologies, the increasing application of microfluidic devices in various research fields has brought up the urgent need for device performance simulation before fabrication to increase the design efficiency and reduce unnecessary trials and errors. As a fundamental function unit, the mixing performance is critical for a successful microfluidic device. Here in this study, we present a method of increasing the design efficiency of random micromixers. We have first generated the random libraries of micromixers constrained to different rectilinear grid configurations from <inline-formula> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> using MATLAB, and finite element analysis was used to simulate the performance of the micromixers. Then the most appropriate grid configuration was investigated on the aspects of fluid velocity and concentration distribution, simulation cost, and micromixer chip size. Our study results showed that the <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> grid configuration of random micromixer is an optimal balance to achieve the most abundant fluid velocity and solute concentration, while has acceptable simulation cost and reduced chip size. In summary, the <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> grid configuration is appropriate to provide enough random micromixer designs to meet user desire, not only time-effective but also cost-effective.
Removal of sulfamethoxazole from wastewater treatment plant effluents by a boron-doped diamond electrode

The high inflow of pharmaceutical pollutants from wastewater into the aquatic environment requires new technologies for effective wastewater treatment. The current research focusses on the application of borondoped diamond (BDD) electrodes for the removal of the persistent antibiotic sulfamethoxazole (SMX) from wastewater effluent. Tests were performed with real secondary effluents from a wastewater treatment plant spiked with SMX at the initial concentration of 5.5 µM. The BDD electrode was operated at two different current densities (208 and 333 mA/cm²). The results showed a very good degradation of SMX. Four previously reported transformation products (TPs) were detected. These intermediates disappeared gradually with the extension of treatment time. SMX was removed during the first 5 to 10 minutes, depending on the current density applied to the electrode. However, much more time was required to remove the majority of the detected TPs. The hazardousness of the TPs is not known and in case it is negligible, the treatment time could be shortened. This will be reflected in less energy consumption and consequently lower costs.
Comparative Study of Face Recognition Approaches

Face recognition systems have become one of the most important and interesting research fields in recent years. These systems are widely used in various disciplines, despite the existence of different surveillance systems such as hand geometry, iris scan, as well as fingerprints. Following their simplicity and non-intrusive nature, they will be the next generation solution expected to support security and surveillance applications. Various approaches are being developed including local, holistic, and hybrid approaches. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. After that, we are going to expose the advantages and the disadvantages of these schemes in terms of robustness, accuracy, complexity, and discrimination. Finally, we will criticize the implementation results of various mentioned databases.
Pedestrian Travel Distance Estimation Using Optical Flow and Smartphone Sensors

The extensive proliferation of smartphones presents a new opportunity for precise indoor and outdoor location-based services. Although various smartphone-based positioning technologies have been investigated, no practical and economical method has been developed where global positioning system (GPS) cannot be applied. Since it is difficult to estimate travel distance accurately with a low-cost inertial sensor embedded in a smartphone, several methods have been proposed to estimate stride. However, the estimation of stride is not accurate as it depends on walking posture. In this article, a novel approach is proposed to estimate the travel distance of the smartphone using optical flow (OF) technology, which is applied to the video image captured from the smartphone camera. Several algorithms have been developed to solve various problems that occur when a user walks holding a smartphone. The feature trace method proposed in this article improves the travel distance estimation accuracy greatly. Several motion compensation methods for the OF are proposed also using smartphone sensor data. The performance of the proposed methods is verified by experiments in three different places with two kinds of smartphones. The estimation accuracy of the travel distance by the proposed method is obtained from all the experimental data. As a result, the average travel distance error rate is 2.6%; for the 5% distance error rate, the error CDF is 86%; and for the 10%, it is 100%. The proposed methods could be applied to track the smartphone of the pedestrian with precise position accuracy indoors and outdoors.
Authentication of Individuals based on Wrist Vein Images by Extracting Phase of Fractional Fourier Transform

Nowadays, biometric systems play a main role in personal information protection. Each person has a distinct pattern of wrist veins that can be used for authentication. Fractional Fourier transform (FrFT) changes signal to complex form. In this paper, FrFT was utilized for the time-frequency analysis of images. After the pre-processing stage and extracting veins from the background, the phase of FrFT coefficients was computed for each image. The PUT database was used in the proposed method for verifying individuals. This database consists of 1200 wrist and 1200 palm vein images. In this paper, wrist vein images were only utilized. Receiver Operating Characteristic (ROC) and Support Vector Machine (SVM) were used for feature selection and classification, respectively. The results showed that wrist vein images can be used for verification and FrFT is a capable tool for feature extraction as the average accuracy was obtained 99.65±0.95% in the operating point.
Patterns of Exposing Integrity of 28nm-node High-k Gate Dielectric on p-substrate with Nitridation Treatments

Well-designed test patters are useful to investigate the integrity of gate dielectric, especially in advanced nano-node process technology with high-k Hf-based dielectric. Besides monitoring the growth of gate dielectric, the antenna effect coming from the back-end of line (BEOL) process or etch contribution can be detected through the demonstration of electrical performance of sub-threshold voltage, interface-state density, and gate leakage. Adopting the capacitance-voltage (C-V) and gate leakage measurement, the integrity of high-k gate dielectric with the assistance of fringe and area patterns can be reflected. This study mainly focused on gate dielectric on p-substrate with the electrical measurement. Through the data analysis, we also performed the performance comparison with various annealing temperature as nitridation treatment.
Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

In this paper, we present a simple-structure Pedestrian Dead Reckon (PDR) system based on commercial IMU sensor and UWB ranging system. In PDR system, the accuracy of step and heading angle estimation completely decide the precision of location result. In order to select proper zero-velocity intervals for step and heading estimations in the foot stance/still stage, a modified zero-velocity detection way called Heuristic Step Detection (HSD) has been designed based on zero-velocity update algorithm (ZUPT). Based on the modified zero-velocity detection algorithm, i.e., HSD, an Kalman-type filter is used to get rough heading angle by fusing zero-velocity information and single UWB anchor ranging results. After that, a constrained sigma point based filter is used to further constrain heading angle range. Moreover, the range measures, provided by a UWB localization system with only one reference anchor, are used to correct the pedestrian step length. Trough UWB ranging measures analysis between two consecutive steps, the step length and pedestrian heading direction correcting processes are related to each other, and as a result, a more stable positioning result can be gotten. The corresponding practical experiments are conducted in real indoor environment over 2000 meters, and the results show that, compared with only INS-aided PDR, our scheme can reduce the average position error by more than 80%, and it can achieve long-term high accuracy and robust localization results.
A Fake News Dissemination Model Based on Updating Reliability and Doubt among Individuals

As social media has become more widely used, fake news has become an increasingly serious problem. The representative countermeasures against fake news are fake news detection and automated fact-checking. However, these countermeasures are not sufficient because people using social media tend to ignore facts that contradict their current beliefs. Therefore, developing effective countermeasures requires understanding the nature of fake news dissemination. Previous models related to this aim have been proposed for describing and analyzing opinion dissemination among people. However, these models are not adequate because they are based on the assumptions that ignore the presence of fake. That is, they assume that people believe their friends equally without doubting and that reliability among people does not change. In this paper, we propose a model that can better describe the opinion dissemination in the presence of fake news. In our model, each person updates the reliability of and doubt about his or her friends and exchanges opinions among each other. Applying the proposed model to artificial and real-world social networks, we found three clues to analyze the nature of fake news dissemination: 1) people can less accurately perceive that fake news is fake than they can perceive that real news is real. 2) it takes much more time for people to perceive fake news to be fake than to perceive real news to be real. 3) the results of findings 1 and 2 concerning fake news are because people become skeptical about friends in the presence of fake news and therefore people do not update opinions much.
Data Sources for Urban Traffic Prediction: A Review on Classification, Comparison and Technologies

Traffic prediction plays a vital role in the process of urban traffic management. Various traffic prediction applications will include different traffic analysis parameters, such as traffic signal control, navigation systems, incident detection, etc. Generally, traffic prediction is considered as a data-intensive task and it includes challenges in capturing, pre-processing, integrating, and analyzing the huge volume of spatial - temporal data. The performance of the traffic prediction is mainly dependent on the right, reliable, and compatible data sources. In this paper, an attempt has been made to deploy a classification of data sources for traffic prediction including innovative sources such as social media and cellular network data. Contributions towards traffic prediction under each data source are presented. Also, a comparative study has been carried out on various data sources based on the quality parameters such as data accuracy and reliability, pre-processing complexity, data adequacy, infrastructure cost, and maintenance overhead.
Online Estimation of Near-Surface Water Current Fields Using Horizontally Mounted ADCPs on Inland Vessels

Knowledge of the near-surface current conditions along rivers allows improved planning of fuel-efficient shipping routes and the development of accurate automatic track keeping and maneuvering systems for inland vessels. This paper investigates the use of acoustic Doppler current profilers (ADCP) mounted horizontally on moving vessels to generate adaptive maps of water current fields close to the surface. A new method is proposed that uses a probabilistic Gaussian process model in combination with a Kalman filter update step to model the current field. This approach allows to condense the incoming stream of measurement data to a manageable set of velocity vectors at predefined grid points. A probabilistic model enables to take localization uncertainty and ADCP measurement noise into account and delivers information about the confidence level of the model at different locations. Simulation results are presented that demonstrate a fast and accurate adaption to an exemplary ground truth current field.
Robust Neural Tracking Controller Design for a Class of Discrete-Time Nonlinear Systems Under Arbitrary Switching

In this article, the problem of robust tracking control is investigated for a class of discrete-time switched nonlinear uncertain system in strict-feedback form, and a novel robust neural tracking control scheme through backstepping technique is proposed for the first time. In order to handle the problem of causality contradiction, the original discrete-time nonlinear switched system is transformed into a special maximum n-step ahead predictor which suits the application of backstepping technique. The radial basis function(RBF) neural networks are adopted to approximate the unknown lumped function at the last step of backstepping procedure, not necessary to approximate all the virtual control laws at each intermediate step. Thus, the process of controller design is significantly simplified in contrast with the existing results. The stability analysis proves the proposed approach guarantees the semi-globally uniformly ultimately bounded(SGUUB) of the closed-loop system, and the tracking error asymptotically converges to an arbitrarily small neighborhood of origin by appropriately designing control parameters. Finally, simulation results are provided to demonstrate the correctness and effectiveness of the proposed scheme.
Monitoring Pandemic Precautionary Protocols using Real-time Surveillance and Artificial Intelligence

The worst possible situation faced by humanity, COVID-19, is proliferating across more than 180 countries and about 37,000,000 confirmed cases, along with 1,000,000 deaths worldwide as of October 2020. The absence of any medical and strategic expertise is a colossal problem, and lack of immunity against it increases the risk of being affected by the virus. Since the absence of a vaccine is an issue, social spacing and face covering are primary precautionary methods apt in this situation. This study proposes automation with a deep learning framework for monitoring social distancing using surveillance video footage and face mask detection in public and crowded places as a mandatory rule set for pandemic terms using computer vision. The paper proposes a framework is based on YOLO object detection model to define the background and human beings with bounding boxes and assigned Identifications. In the same framework, a trained module checks for any unmasked individual. The automation will give useful data and understanding for the pandemic’s current evaluation; this data will help analyse the individuals who do not follow health protocol norms.