Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Graphene Nanoribbon-based Gate Performance Robustness under Temperature Variations

As CMOS scaling is reaching its limits, high power density and leakage, low reliability, and increasing IC production costs are prompting for developing new materials, devices, architectures, and computation paradigms. Additionally, temperature variations have a significant impact on devices and circuits reliability and performance. Graphene's remarkable properties make it a promising post Silicon frontrunner for carbon-based nanoelectronics. While for CMOS gates temperature effects have been largely investigated, for gates implemented with atomic-level Graphene Nanoribbons (GNRs), such effects have not been explored. This paper presents the results of such an analysis performed on a set of GNR-based Boolean gates by varying the operation temperature within the military range, i.e., -55°C to 125°C, and evaluating by means of SPICE simulations gate output signal integrity, propagation delay, and power consumption. Our simulation results reveal that GNR-based gates are robust with respect to temperature variation, e.g., 5.2% and 5.3% maximum variations of NAND output logic “1” (VOH) and logic “0” ($V$OL) voltage levels, respectively. Moreover, even in the worst condition GNR-based gates outperform CMOS FinFET 7nm counterparts, e.g., 1.6× smaller delay and 185× less power consumption for the INV case, which is strengthening their great potential as basic building blocks for future reliable, low-power, nanoscale carbon-based electronics.
An Integrated Fusion based Feature Extraction and Classification Model for Diabetic Retinopathy Diagnosis

Diabetic retinopathy (DR) becomes widespread among diabetic patient and cause vision loss in maximum number of people. The DR can be avoidance by controlling blood glucose level and proper prescription. As the DR involves different classes and complexity levels, it is hard to detect and diagnose DR. This paper introduces a new integrated fusion based feature extraction and classification model for DR diagnosis. The projected model involves a fusion model using gray level co-occurrence matrix and VGG-19 called FM-GLCM-VGG19 method. Initially, the input images undergo format conversion and then watershed based segmentation process gets executed. Then, the fusion model takes place to extract the useful set of features and finally softmax based classification process is carried out. The validation of the FM-GLCM-VGG19 Method takes place on Kaggle dataset and the simulation results revealed the betterment of the FM-GLCM-VGG19 Method by obtaining higher accuracy of 71.30%, sensitivity of 50.43% and specificity of 80.19% respectively.
A New Approach for Designing Stable Nonlinear bounded-Lipschitz Observers

Observer synthesis for nonlinear Lipschitz systems is still an open problem mainly due to the heuristic manner of obtaining the observer gain matrix. The design is constrained with hard stability restrictions typically imposed by the Lipschitz constant. In this paper, it is shown that when the Lipschitz terms are bounded the stability restrictions can be significantly relaxed and an easily implemented design is deployed which enables the observer linear part eigenvalues to be assigned with a desirable real part on the left of the system poles. The asymptotic stability of the estimation-error is guaranteed by employing a Lyapunov-type equation, which is absolutely compatible with the bounded conditions assumed for the nonlinear terms. As it is easily seen, the proposed observer can be directly integrated into a closed-loop system structure with any linear feedback controller capable to stabilize the original system. The validity of this method is verified by implementing the proposed design on a fundamental power system example. The simulation results fully support the theoretical analysis by exhibiting the easy way of the design which allows an enhanced observer-based control performance.
Order Statistics Based Best Two Transmit Antenna Selection for N × 1 MISO System

In this paper, we analyze the performance of the best two transmit antenna selection (BTTAS) scheme for N×1 multiple-input-single-output (MISO) system employing M-QAM constellation. The exact and asymptotic closed-form expressions of outage probability and symbol error rate (SER) are derived using order statistics over the Rayleigh fading channel for the considered system. This selection technique reduces the complexity and costs of the system associated with multiple transmit antennas while achieving the full diversity order. Further, numerical results show that the proposed BTTAS beamforming scheme achieves full diversity and provides a significant coding gain in comparison to the Alamouti space-time block code.
Dynamic Adjustment Mechanism based on OPC-UA Architecture for IIoT Applications

In much smart Industry Internet of Things (IIoT) applications, there are many emergencies information processing e.g. fire and urgent accidents. However, when data which is from different source and format is lack of collation, integration and preprocess, the server loading will be too heave to process the information on time. This study mainly proposes to establish an Open Platform Communications Unified Architecture (OPC UA) architecture system for IIoT application design and introduces a dynamic node adjustment mechanism to be applied under the OPC UA architecture. The dynamic adjustment node is designed with network bandwidth monitor to solve the problem of excessive network load caused by emergency information transmission in the IIoT applications.
A Horizontal Hall Sensor 3D Comsol Model

This paper presents a three-dimensional finite element method (FEM) model of horizontal Hall sensor in COMSOL Multiphysics. The model aims to simulate the sensor’s performance in a straightforward way and provides a means to simulate more complex applications with other physics modules. Compared with traditional two-dimensional models, the three-dimensional model is more flexible in designing Hall sensor and shows great significance in verifying the performance of sensors with different materials and geometries. By changing model parameters and comparing simulation results, an optimal design solution can be achieved effectively and economically. The presented Hall sensor achieves a sensitivity of 0.0669 V/(VT). Basic circuit modules of a complete sensor system are also introduced.
Similarity Calculation of 3D Model By Integrating Improved ACO Into HNN

It is important for 3D model retrieval to compute similarity between two models accurately. In order to calculate two models’ similarity, their face matching scheme need be found. Hopfield neural network (HNN) and ant colony optimization (ACO) algorithm can be used to match source faces and target ones. But, they all drop into local optimum easily and can not get optimal face matching scheme. This paper proposes a new method of computing model similarity by integrating improved ACO algorithm into HNN. Shape similarity between source face and target one is calculated based on the difference of face’s edge number. Structure similarity is computed according to adjacency correspondence relationship between source face and target one. Two faces’ similarity is calculated based on shape similarity and structure similarity, which is introduced into transfer probability. Indirect expectation heuristic is defined to improve ACO algorithm. Then, improved ACO algorithm is integrated into HNN to search for optimal face matching scheme. Model similarity is computed based on optimal sequence of face pairs. Experimental results show that compared with improved ACO algorithm, the proposed method improves the ranking effect of 14.29% of models, which can measure two models’ difference effectively.
A Survey of Application of ML and Data Mining Techniques for Smart Irrigation System

This paper reviews our current research in agriculture analytics on an open-source platform using data mining and machine learning techniques. Various sensors are used to collect data that provides real-time analytics on the weather forecast, soil moisture, air temperature, PH, humidity. The smart irrigation system is paired with different hardware and development application. The science of machine learning and data mining plays a significant role here. It is a data analysis process using multiple models and algorithms to trained data directly. Machine learning and data mining to hold the irrigation system in r leaner manner. For instance, it optimizes the use of water and provides a critical amount of water and fertility to increase production efficiency, reduce manpower involvement, and reduce crop diseases. The survey tests the effect of applicable techniques and how these techniques boost efficiency.
An Efficient Wilkinson Power Divider using Compact Stub Structure

A modified Wilkinson power divider/combiner is proposed in this paper using a compact stub structure which suppresses the higher harmonics. The design uses microstrip technology, where Open Circuit Stubs (OCS) is used as a replacement for the capacitors. Operating frequency of the proposed design is at 2.5 GHz and the dielectric substrate used is FR4 with a dielectric constant of 4.5 and substrate height of 1.5mm. Advanced design system (ADS) tool is used for the simulation of the proposed design. Fine-tuning of the design was performed using the Tuning and Optimization feature of the software. The designed power divider has Return Loss better than 37 dB and isolation better than 39 dB is obtained between the output ports. Voltage Standing Wave Ratio (VSWR) is found out to be less than 1.1. These dividers can be used in High power amplifiers, Telecom, satellite feeders etc. With these output values, the proposed design is expected to have a size reduction better than the existing methodologies and devices. A detailed comparative study is done and the improved results are presented and analyzed.
Real-World Efficacy of an Infrastructure-Free Geomagnetic Indoor Positioning System

Infrastructure-free Indoor Positioning Systems (IPS) are a relatively new advancement in the indoor localisation area. Traditionally, IPS's require a certain amount of dedicated infrastructure within a building to position a device accurately. There are now companies offering infrastructure-free IPS that claim they can obtain sub-metre accuracy. Some IPS's also claim to use geomagnetic positioning to achieve this accuracy. The aim here is to investigate the accuracy of an infrastructure-free IPS which offers geomagnetic positioning techniques. We explain its accuracy, what may negatively affect the accuracy of the system and to what extent are geomagnetic positioning techniques being used. An Android mobile application was developed with the primary purpose of investigating the accuracy of the IPS under various conditions. We found that the IPS at the beginning was three times more accurate than GPS and ten times more accurate at the end. The IPS was more responsive and more accurate on a device that had a magnetometer but accuracy dropped considerably when the internet connection is lost. Also, wet weather specifically impacted negatively on the accuracy.
Multiband Reconfigurable Antenna for Wireless Applications

Proposed work presents, compact, light-weight and low cost triband monopole reconfigurable antenna for wireless applications. The antenna offers lowest band 1.9 GHz - 2.1 GHz (UMTS), middle band operates between 2.7 GHz - 3.1 GHz (WiMAX) while the highest band operates over 4.8 GHz - 8.6 GHz (WLAN and ITS). The lowest, middle and the highest operating bands yield 10%, 13% and 51.5% bandwidth respectively. A compact size antenna within a FR4 substrate board, of area 20× 20 mm2 makes it feasible to be used within portable devices. The top view of the antenna structure comprises of a hook shaped radiating patch along with p-i-n diodes, while rectangular slot in ground plane completes the bottom view. Frequency reconfiguration using switching states of p-i-n diodes is achieved by altering the corresponding active length of the patch. Various combinations of states of diode resulting in different antenna configurations run single band, pair of dual band and triband response. To validate the proposed work, prototype of antenna is fabricated using the photolithography technology and tested. Simulated and measured return losses are in close agreement with each other. Other parameter like radiation pattern of the antenna presented is observed to be stable.
Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning

With the exponential growth of the number of vehicles, a series of traffic system problems continue to emerge. Vehicle detection plays an important role in the traffic system. Researchers have invested a lot of energy on it, and have made many achievements. However, there are still some deficiencies in the accuracy and robustness. The development of satellite remote sensing technology and VR panoramic technology provides technical support for this study. In order to detect vehicles more accurately, provide accurate and effective data information to relevant departments, and improve traffic conditions, this study uses deep learning algorithm to detect vehicles in high-resolution satellite remote sensing images. Firstly, the images in the images are classified by using the Alexnet network model, and then the vehicle target detection ability of the Faster R-CNN model algorithm is tested, and the algorithm is optimized by the method of model pruning and quantization, so that the average accuracy rate reaches 70.34%, and has strong robustness. Then the algorithm model is applied to the practical application of vehicle detection at an intersection. The traffic flow and the number of speeding vehicles are detected, and the false detection rate and missing detection rate are calculated. The results showed that the rate of missed detection and false detection was 0% on December 19, and the highest rate was 4.5% and 2.7% respectively. This shows that the vehicle detection based on deep learning algorithm in high resolution satellite remote sensing images has high accuracy, can effectively detect speeding vehicles, and can play a normal role in practical applications.
Effects of Ultra-Fast Charging System for Battery Size of Public Electric Bus

Most of the DC fast chargers have 50–150 kW power rates in the market. These power rates may give a suitable charging time for personal EVs but not for commercial electric vehicles. Thus, the DC fast charger should be improved to charge bigger battery capacities in an acceptable charging time. Especially, the regular DC fast chargers cannot give acceptable charging time for the electric buses and/or electric heavy trucks. The new battery and power electronic technology make it possible to use much more power rate for fast charging. These high-power rate chargers are called ultra-fast charging and/or extremely fast charging. In this study, the ultra-fast charging system has been used to reduce the charging time of public buses. The updated charger standards such as CHAdeMO and SAE have been taken into consideration. Accordingly, the 450 kW ultra-fast charging system is used for on-route charging to analyze the battery size of the electric bus. In addition, the benefit of having an on-route charging station has been evaluated and compared with having no charging station in terminals. Public bus routes have been used to optimize bus battery capacity according to the bus schedule, stop time, and the ultra-fast charger power limit. The optimum battery size is calculated and optimum route length has been proposed for a given scenario for the E-buses.
Load Profile Modeling Using High-Frequency Appliance Measurements for Nonintrusive Load Monitoring

Many load disaggregation algorithms have been presented, capable of identifying individual appliances' operation within a buildings' electrical load profile. Publicly available datasets offer the potential to evaluate and compare these algorithms. Due to differing application areas, datasets show inconsistent characteristics, so exploitation of this potential is not straightforward. Aim of the presented work is to contribute towards solving this problem. We present a methodology for utilizing high-frequency appliance measurements for load profile modeling, while trying to keep modifications of the measured appliance features to a minimum. The voltage measurements were used to separate the appliances' events and states in current for each grid period, to be able to recombine appliance behavior in a user-defined manner. The modeled load profiles showed high accuracy compared to aggregated measurements. The presented methodology can be applied to various datasets and appliance measurements, to create standardized load profiles for the development, evaluation and comparison of disaggregation algorithms.
A Wireless Power Transfer System for Biomedical Implants based on an isolated Class-E DC-DC Converter with Power Regulation Capability

In this paper, the design of a wireless power transfer system (WPT) targeting biomedical implants is considered. The novelty of the approach is to propose a co-design of the transmitter and receiver side based on the design of class-E isolated DC-DC converters. The solution, along with the simple introduction of a shunt regulator at the receiver, allows us to solve the problem of ensuring optimal efficiency in the WPT link. In conventional solutions, in order to cope with coupling factor and load variations, information from the receiver is needed, which is usually relayed back onto the transmitter by means of telemetry. With the proposed approach, a very simple minimum power point tracking (mPPT) algorithm can be used to maximize the WPT efficiency based on the information already available at the transmitter side. This reduces the complexity of the circuitry of the implant and thereby its power overhead and possibly its size, both being crucial constraints of a biomedical implant.
Cloning and Expression Analysis of GmMYB Genes Induced by Abiotic Stresses

Response to external environment is the outcome of stress-induced gene expression.In this paper,based on one stress-induced EST sequence,we cloned four R2R3-MYB genes from soybean cultivar Dongnong 42,whose genomic sequences con-sisted of three exons and two introns.Three of them corresponding to Gm02g1300,Gm03g38040,and Gm10g01340 are respectively consistent with the sequences of Williams 82.A mutation at the 375th single nucleotide in the sequence of Gm19g40650 from Dongnong 42 caused a synonymous amino acid substitution(E125-D125). To test the relationship of four MYB genes with stress resis-tance,we treated the seedlings of cultivar Dongnong 42 with abiotic stresses including salt,alkali,drought and low temperature in the artificial climate chamber.Quantitative PCR analysis indicated that all of the four genes were transient down-regulated or up-regulated when subjected to the stresses,but different in the expression time,level and tendency.Gm02g01300 was induced by drought stress while Gm03g38040 was strongly induced by multiple stresses,indicating that they play important roles in responding to external stresses.There were also differences in the expression of individual gene between cotyledons and embryos.These results under a variety of abiotic stress conditions suggest that the four R2R3-MYB genes are different not only in the expression patterns,but also in the regulation modes.
Timer-based Decision in Speed-Variant Data Forwarding for VANETs

Vehicular Clouds heavily rely on the underlying Vehicular Networks to discover, announce, and exchange services and resources. The assembly and control of such clouds depend upon reliable and efficient data dissemination among vehicles and roadside units. Dissemination allows for critical information to be spread efficiently and widely through the VANETs. It is, therefore, imperative to create a dissemination algorithm that reduces the number of redundant messages. The biggest concern is to define an effective and efficient data dissemination algorithm that can be used in critical areas in traffic like intersections. Several issues must be considered in dense vehicular regions, such as broadcast storms and redundancy. Several approaches have already shown a reduction in the redundancy and overhead. Still, they may need to improve on the variance of a dynamically changing topology and exponential growth rate of messages sent. These approaches include a speed adaptive probabilistic dissemination, which is a lightweight approach. This work focuses on controlling the dissemination pace by applying timers and limiting forwarding messages on the speed adaptive broadcast algorithm to reduce overhead and redundancy while ensuring the broadcast is transmitted evenly. The timers are useful in high-density traffic and conclusively showed a broadcast overhead reduction.
Vitamin D and non-alcoholic fatty liver disease: a systematic review and meta-analysis

Non-Alcoholic Fatty Liver Disease (NAFLD) describes a wide range of hepatic pathological conditions beginning with accumulation of lipids, especially triglycerides, in the hepatocytes in the absence of significant alcohol intake. NAFLD shows a strong association with obesity, type 2 diabetes (T2D), insulin resistance (IR), hyperlipidaemia and arterial hypertension. Hence it is referred to as the hepatic manifestation of the metabolic syndrome. Studies have shown significant inverse associations between 25-hydroxyvitamin D (25(OH)D) concentration and other diseases including diabetes, hyperlipidaemia, hypertension and peripheral vascular disease, suggesting a possible role for vitamin D in the pathogenesis of NAFLD . The aim of this study was to systematically review the association between NAFLD and vitamin D using data from both randomised control trials (RCT), cross-sectional and case-control studies. This was to quantify differences in 25(OH)D status between individuals with and without NAFLD, as well as the effect of vitamin D supplementation in NAFLD patients on metabolic function. The PUBMED database was electronically searched for relevant studies in adolescents and adults from inception up until April 2017. A total of 129 relevant studies were identified and 24 of these were suitable for inclusion in the systematic review. Meta-analysis of cross-sectional, cohort and case control studies suggested a statistically significantly lower serum 25 (OH) in those with NAFLD, compared with those without NAFLD (Mean Difference (random)= -16.80 nmol/L [-24.38, -9.21] I =96 % P <0.0001). In addition, meta-analysis of 4 randomised control trials (RCTs) suggested a statistically significant effect of vitaminD supplementation on serum 25 (OH) D levels (Mean Difference (random)= 53.08nmol/L [28.22,77.93] I =96 % P < 0.0001), Furthermore, meta-analysis of HOMA-IR suggested no statistically significant effect of vitaminD supplementation (MeanDifference (random) =−0.26 [-2.02, 1.51] I= 99 % P = 0.78). In conclusion, 25(OH)D status was lower in individuals with NAFLD than in those without NAFLD, suggesting that serum 25 (OH)D levels may be a factor in the development of NAFLD. However, it may also simply be a result of persons with NAFLD having higher adiposity than those without NAFLD, as increased adiposity is associated with reduced 25(OH)D concentration. This is the first study to report a meta-analysis of vitamin D supplementation in NAFLD patients. No effect was found of vitamin D supplementation on HOMA-IR. Therefore this review does not support the use of vitamin D supplementation for NAFLD patients in terms of improving metabolic function, although it could have other health benefits, such as for immune and musculoskeletal health.