Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Endoscopic Polyp Classification using GAN Generated Synthetic Data Augmentation

Early diagnosis of cancer in polyps detected in the endoscopic video frames helps in better prognosis and clinical management. For this, the polyp regions are exhaustively analyzed by an endoscopist. In this paper, an automated polyp classifier in a deep learning framework is proposed. As the availability of ground truth data for colonic polyp is always in paucity, data augmentation is indispensable in such task. Our work proposes the use of Generative adversial networks (GANs) for synthetic data generation. For classification, a CNN is trained which discriminate between normal (benign) and cancer (malignanat) polyps. Experiments carried on two databases prove that the proposed data augmentation technique can efficiently be used in the classification of colonic polyps. Also, our proposed method compares the performance achieved using classical augmentation approach which is generally considered in limited data scenario. Experimental results show that the classification accuracy is competitive to the state-of-the-art methods.
Target Classification Using Combined YOLO-SVM in High-Resolution Automotive FMCW Radar

In this paper, we propose a method to classify the human and vehicle objects by combining a support vector machine (SVM) and the deep learning model, you only look once (YOLO), in a high-resolution automotive radar system. To enhance the classification performance, the boundaries of targets estimated from the YOLO model are delivered to the SVM. Then, the overall classification accuracy can be improved by combining the results from the YOLO and SVM with predefined target boundaries. The results show that the proposed method has a better classification performance than those obtained using only YOLO or SVM. From the actual measurements, the proposed method can classify humans and vehicles over 90% accuracy in high-resolution automotive radar.
Antimicrobial‐Producing Lactic Acid Bacteria Isolated from Raw Barley and Sorghum

From a total of four thousand presumed lactic acid bacteria, obtained from raw, unmalted sorghum and barley, 308 isolates were shown to exhibit inhibitory activity against the indicator strain Listeria innocua 4202. Six of these inhibitor-producing isolates were selected for further study on the basis of their relatively wide antimicrobial spectrum, which showed that these producers inhibited several Gram-positive bacteria, including a range of beer spoiling bacteria. The proteinaceous nature, anti-microbial activity against closely related species, heat resistance and pH stability of the inhibitory substances produced by these six bacteria identified these compounds as bacteriocins. All six isolates were shown to secrete the inhibitory compounds into the cell free supernatants. Bacteriocins produced by five of the six producers were purified to homogeneity. Further analytic data was obtained for three of the inhibitory compounds by means of mass spectroscopy and/or N-terminal amino acid sequencing.
Edge on Wheels With OMNIBUS Networking for 6G Technology

In recent years, both the scientific community and the industry have focused on moving computational resources with remote data centres from the centralized cloud to decentralised computing, making them closer to the source or the so called “edge” of the network. This is due to the fact that the cloud system alone cannot sufficiently support the huge demands of future networks with the massive growth of new, time-critical applications such as self-driving vehicles, Augmented Reality/Virtual Reality techniques, advanced robotics and critical remote control of smart Internet-of-Things applications. While decentralised edge computing will form the backbone of future heterogeneous networks, it still remains at its infancy stage. Currently, there is no comprehensive platform. In this article, we propose a novel decentralised edge architecture, a solution called OMNIBUS, which enables a continuous distribution of computational capacity for end-devices in different localities by exploiting moving vehicles as storage and computation resources. Scalability and adaptability are the main features that differentiate the proposed solution from existing edge computing models. The proposed solution has the potential to scale infinitely, which will lead to a significant increase in network speed. The OMNIBUS solution rests on developing two predictive models: (i) to learn timing and direction of vehicular movements to ascertain computational capacity for a given locale, and (ii) to introduce a theoretical framework for sequential to parallel conversion in learning, optimisation and caching under contingent circumstances due to vehicles in motion.
Robust Adaptive Control for DC-DC Buck Converters with Load Fluctuation

This paper mainly develops a robust adaptive tracking control scheme to counter effects caused by load fluctuation and external disturbances. An adaptive controller based on appropriate control strategy and adaptive law is presented based on Lyapunov stability theory. The output voltage asymptotic tracking of the buck converters is obtained even under the influence of load fluctuation and external disturbances. Simulation results also reveal the availability of the proposed adaptive control scheme.
Cauchy’s Generalized Problem in Solving a Nonlinear Three-parameter Spectral Problem

The paper proposes a new numerical method for solving a three-parameter nonlinear spectral problem for holomorphic operator-functions, defined in Banach spaces. The concept of a generalized Cauchy problem is introduced, which is to solve a system of two equations in partial first-order derivatives with a common initial condition. This makes it possible to find connected components of spectrum (CCS) in the form of three-dimensional smooth spectral surfaces.
A virtual simulation environment using deep learning for autonomous vehicles obstacle avoidance

Autonomous vehicles which are capable of operating independently will be commercially available in the near future. Autonomous driving systems are becoming more complicated and must be successfully checked before implementation. Within this framework, falls our research work. The key purpose of this paper is to implement a simulation environment for autonomous vehicles. We first created this environment which is a novel high fidelity driving simulator that can connect arbitrary interfaces, build simulated worlds consisting of scenarios and incidents experienced by drivers in real-world driving, and incorporate fully autonomous driving. The simulator makes possible to clone the behavior of human driver face as well as some complex situations such as obstacle avoidance maneuvers. The work consists in creating a virtual simulation environment to collect training data used to train vehicles on how to steer themselves. The simulator is, thus, like a video game of car racing. Indeed we used the scenes to make some driving experiences. After collecting the training data, we chose to use deep learning explicitly Convolutional Neural Networks to create a model for autonomous vehicles that avoid obstacles. Clearly, the true challenge for an autonomous vehicle is to navigate without the possibility of collision. This simulator is invested to assess the performance of an autonomous vehicle and to analyze its self-driving activities. In this method, the suggested solution proves to be feasible, efficient and reliable for autonomous vehicle simulation research.
Inductive-Resonant Wireless Power Transfer System with Hybrid Modulation

This paper deals with inductive-resonant wireless power transfer (WPT) systems based on multilevel T-type inverters with simultaneous modulation of switching frequency and duty cycle. The effect of the hybrid modulation on the performance characteristics of closed-loop WPT systems with Ttype multilevel inverters for wireless battery charging is studied in details by using simulation. Performance characteristics of the WPT system with the hybrid modulation are compared with that of WPT system without modulations and with classical switching frequency modulation. It is shown that T-type multilevel-inverter based WPT systems with hybrid modulation exhibit better performance than T-type multilevel-inverter based WPT systems with classical switching frequency modulation in terms of lower RMS and peak values of currents as well as better efficiency.
Website Trust to user online based Shari'ah Model determinant: A Case Study toward user behavior model

The development of online business has increased rapidly, and this is a change in trade and transactions. Consumer trust is very important in online business. Trust will ensure user satisfaction and comfort and become one of the intentions to make transactions. Today, many Muslim users have taken advantage of online businesses in making various transactions. However, customer trust, as one of the factors to measure success, must control e-commerce and also the attractiveness of websites and web service products that offer quality services. Studies show that trust has a significant effect on the acceptance and willingness of system users. Based on the literature, the role of trust in electronic transactions is seen from an Islamic perspective, and according to the consideration of Muslim users that the percentage is relatively significant. For this reason, the concept of Sharia is a must for the attitudes of Muslim internet users who are centered on religion which is likely to negatively impact the overall success of e-business. This paper analyzes the trust model in online business from a sharia perspective to build customer trust.
Latitudinal Dependency of Solar Irradiance - Solar Flare Index Relation over India during 1986–2018

The relation between solar activity indices and earth atmosphere is studied in this current investigation using correlation as well as periodic analysis. The solar flare index and solar irradiance signal of India over different latitude values are considered in this study as solar activity indices and Earth atmospheric parameter respectively. To evaluate their relationship, last 33 years (1986 – 2018) of data is considered which covers solar cycle 22, 23 and almost the complete solar cycle 24. The solar irradiance of India over different considered geographical location has a positive correlation during solar cycle 23 and a negative correlation during solar cycle 24 with solar flare index. It has also been observed that the maximum correlation is exist between 5° - 10° latitude and 30° - 35° latitude data and the correlation has an increasing trend with higher latitude. The periodicity analysis of solar irradiance signal exhibits a similar type of inherent period with solar flare index as well as other activity indices. It can be suggested that the effect of the solar flare index is exists on the solar irradiance signal of India over different considered geographical location according to the current statistical investigation during solar cycle 22 – 24.
Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

Continuous measurement of blood pressure is crucial to the assessment of many medical conditions. However, the current clinical gold standard involving an arterial catheter, occluding cuff, and other invasive procedures are performed in hospital settings while home-based devices can provide only intermittent measurement and are not as reliable. Therefore, there is a significant need for continuous non-invasive blood pressure (cNIBP) monitoring in the daily life. Pulse transit time (PTT)/pulse arrival time (PAT) - based blood pressure measurement has proven its potential to address this need. In this article, we present state-of-the-art devices and recent literature related to measurement technologies used in PTT/PAT - based methods for cNIBP monitoring. Various physiological signals which could be used to enable cNIBP in the home setting are categorized into two groups (i.e., proximal waveforms and distal waveforms) and are thoroughly discussed and compared. Given insightful analysis of these waveforms, we highlight their combinations to derive PTT/PAT values for BP measurement then discuss challenges presented from the cuffless and PTT/PAT - based nature of these devices. Finally, we conclude with future directions needed for home-based cNIBP adaptation and present societal broader impacts.
On the Design and Implementation of Real-Time Resource Access Protocols

Real-time operating systems (RTOS) should support resource access protocols to bound the maximum delay incurred by priority inversions. The implementation of such protocols must be lightweight because its performance affects the system schedulability. In this paper, we present an object-oriented design of real-time resource access protocols for single- and multi-core systems aiming at reducing the run-time overhead and increasing code re-usability. We implement the proposed design in an RTOS and measure the memory footprint and run-time overhead of the implementation in a modern processor. By applying the obtained overhead into the schedulability analysis of six protocols for synthetically generated task sets, our results indicate that proper implementation of resource access protocols has a low impact on the schedulability of real-time tasks.
A Study on Sizing of Substation for PV With Optimized Operation of BESS

Recent development and cost down of PV(Photovoltaic) technology drive change of power system structure. The participation of PV generation is increasing, and the size of each PV farm is getting larger. In order to integrate large PV farms into the main grid, substation for interconnection needs to be sized properly. Unlike substations for load and conventional generators, PV farm substation has an uneven utilization ratio due to characteristics of solar radiation. With proper sizing method for the capacity of the substation can reduce the building cost of facilities. A combination of an energy storage system can further reduce the capacity of the substation. Battery energy storage system (BESS) can shift the peak production of PV during the daytime to midnight. According to market circumstances, BESS can reduce further construction costs by producing profit based on time difference of electric cost. For proper sizing of substation capacity, several factors must be considered including environmental factors, market structure and BESS in the system. In this article, a series of assessment methodology is introduced to calculate the optimized capacity of substation and BESS for PV farm interconnection. The long-term solar radiation data is analyzed for a given site of the PV farm. Based on market structure, the operation of BESS is optimized to make maximum profit during operation. The iterative calculation of each step results in the calculation of the optimized capacity of BESS and substation for given PV farm size.
MIMO Radar Detection in Compound Gaussian Sea Clutter Using Joint Model and Sample Selection

This paper deals with the adaptive detection of moving target for multiple-input multiple-output (MIMO) radar in compound Gaussian sea clutter circumstances. A novel detector based on two-step generalized likelihood ratio test (GLRT) framework is developed by using model and sample selection (MSS) algorithm. Specifically, the type of detector is determined by the model selection (MS) approach according to the minimum of model fitting error using the Anderson-Darling (AD) test, and then the better secondary data are chosen from reference cells based on the samples selection (SS) criterion. Finally, the proposed detector on the basis of MSS algorithm is compared with traditional detectors using the real sea clutter data, i.e., IPIX data. The numerical results indicate that the novel MSS algorithm for detector exhibit more significant performances than the traditional detectors.
I-Gressus: Low-Cost Plantar Gait Monitoring System for Clinical Diagnosis

Plantar gait monitoring is an important aspect of clinical monitoring as diagnosis to determine the existence of walking abnormalities in patients. The current procedure followed within clinics is physical observation or making the use of platform systems. These methods are prone to bias and the psychological phenomena of Hawthorne Effect. Other more sophisticated methods prove to be expensive or infeasible to the average consumer class. To this end, we have developed a novel gait monitoring system: I-Gressus that can be worn by the patient for natural gait monitoring over long periods of time with an additional cloud based data logging capability. The system makes use of resistive pressure sensing array mounted on an insole controlled via an ESPS266 WiFiSoC which logs data wirelessly on a cloud based web application. This footwear can be worn throughout the day for continuous monitoring and is relatively inexpensive to manufacture and maintain. To evaluate the devised prototype testing scenarios with various control cases have been performed. The resulting analysis shows that the model is capable of rapid diagnosis and data logging that outperforms the utility offered by various other gait monitoring frameworks under the price of $ 135.
Sensor Selection for Multi-target Tracking in Phased Array Radar Network Under Hostile Environment

In this paper, a novel sensor selection scheme is presented for multi-target tracking (MTT) in phased array radar (PAR) network under hostile environment. We consider a scenario that the active PARs transmit beams to track multi-targets, meanwhile, the enemy targets equipped with passive radars can collaboratively locate positions of PARs according to the intercepted signals. The main idea of this scheme is to appropriately adjust the beams of multiple PARs, with the aim of improving their MTT performance while decreasing the tracking capability of the enemy targets. We formulate the sensor selection scheme as a non-convex optimization problem, and solve it through convex relaxation and a proposed two-step solution technique. Simulation results demonstrate the superior performance of the proposed method when compared with the traditional random beam allocation scheme.
Monitoring Fibrin Polymerization Effects on Whole Blood Coagulation Using a Microfluidic Dielectric Sensor

This paper reports on the use of a microfluidic dielectric sensor, termed ClotChip, for monitoring the fibrin polymerization effects on whole blood coagulation. The ClotChip sensor readout captures the temporal variation in the normalized real part of the blood dielectric permittivity at 1MHz as it undergoes coagulation. A new readout parameter, Smax, is identified in the ClotChip readout curve, which represents the maximum permittivity slope after a permittivity peak is reached. Experiments based on ex vivo treatment of whole blood samples with H-Gly-Pro-Arg-Pro-OH (GPRP) – a fibrin-polymerization inhibitor – demonstrate that Smax decreases with increasing GPRP concentrations corresponding to higher levels of fibrin inhibition. Furthermore, Smax is shown to exhibit a strong positive correlation (Pearson’s r = 0.883, p < 0.001, n = 12) with the α-angle readout parameter of rotational thromboelastometry – a clinical, viscoelastometry-based, comprehensive assay of blood coagulation. This work shows the ClotChip potential as a point-of-care platform for rapid assessment of fibrin-polymerization defects and their impact on the overall hemostatic status.
Development of Information and Communication Technology-Based Hybrid Learning in Group Dynamics Courses with Collaborative Problem Solving Models

The research objective is to develop a hybrid learning group dynamics subject with a Collaborative Problem Solving (CPS) model. The development step refers to the development formula proposed by Borg & Gall which is modified into potential and problem analysis, product development, product testing, product revision, product validation, and final product. The results of the study were in the form of three products, namely: (1) eight video lessons on group dynamics courses; (2) CPS syntax is phase 1 focusing attention, phase 2 project development, phase 3 imitation modification, phase 4 problem analysis, phase 5 problem solving, phase 6 report review, and phase 7 team assessment; and (3) one video implementation of hybrid learning in group dynamics courses with the CPS model.
Application of Cuckoo Controller to 9-Level NPC based APF to Improve Power Quality

Nowadays, the improvement of power quality is the major concern in power system scenario. As per literature analysis so far a huge number of technique implemented to improve power quality of the system. Out of these, the FACTS controllers plays a key role. This paper is presents a concept of series and shunt active filters implemented with 9-level inverters to reduce the harmonic distortions. The series active filter control structure is implemented with system and load voltages, the reference currents required for shunt active filter is implemented using PQ-control theory. To get better improvement of power quality and harmonic distortion, the series and shunt controllers are implemented with CUCKOO controller. This proposed system is tested and verified in MATLAB/SIMULINK.
Simulator Design for Interference Analysis in Complex Automotive Multi-User Traffic Scenarios

Since the number of radar sensors per vehicle has been increasing in order to improve the performance of Advanced Driver Assistance Systems, mutual interference has become one of the most important challenges for near future automotive radar systems. Depending on the sensor parameters and the diverse properties of the traffic scenario, the interference effects can significantly vary. The effort to measure and appraise the interference in real traffic scenarios leads to the importance of designing a tool suited to simulate and analyze the interference. This paper presents a fast simulator capable of modeling, estimating, and evaluating the effects of interference in complex multi-user traffic scenarios. A statistical analysis of the interference in a proposed scenario is performed in order to validate the presented simulator.