Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation on the surface states evolution of polycrystalline silicon nanowires for gas sensing application

We perform a feasibility study on the application of thin film polycrystalline silicon (polysilicon) nanowires for gas sensing application upon exposure into different gas ambient. The nano-sensors are developed in a relatively cheap reactive ion etching system using a low-density plasma based etch and mature 3 micron lithography. Systematic measurements in air, vacuum, hydrogen and isopropyl alcohol demonstrate promising gas sensitivity and impressive response/recovery times in room temperature. The modification of surface states in nanowires upon exposure into different gas ambient is confirmed through 3D numerical simulation. This investigation demonstrates the potential of polysilicon nanowires for detecting molecules in gas phase thereby reveals that the polysilicon nanowire sensor is promising for a simple, low cost, manufacturable route for gas-sensor fabrication.
Sub-Sample Time Delay Estimation via Auxiliary-Function-Based Iterative Updates

We propose an efficient iterative method to estimate a sub-sample time delay between two signals. We formulate it as the optimization problem of maximizing the generalized cross correlation (GCC) of the two signals in terms of a continuous time delay parameter. The maximization is carried out with an auxiliary function method. First, we prove that when written as a sum of cosines, the GCC can be lower bounded at any point by a quadratic function. By repeatedly maximizing this lower-bound, an alternative update algorithm for the estimation of the time delay is derived. We follow through with numerical experiments highlighting that given a reasonable initial estimate, the proposed method converges quickly to the maximum of the GCC. In addition, we show that the method is robust to noise and attains the Cramér–Rao lower bound (CRLB).
Analyzing Acoustics of Indian Music Audio Signal Using Timbre and Pitch Features for Raga Identification

The huge amount of Indian Music is available on the web. The base of Indian Music is Ragas. The classification and tagging of Indian Music based on Raga will help in efficient retrieval of data. The classification based on metadata may provide wrong result as the erroneous, incomplete information is provided. The analysis of acoustic features provides accurate information of data. The melody of music can be extracted by pitch values, its variations, and patterns formed by it. In the literature, researchers used Pitch Features with Pitch Class Distribution and observed the loss of temporal properties. The Phrased based methods are developed but for very restrictive input. The Mel Frequency Cepstrum Coefficient provides timbre information, which is useful for singer detection, instrument detection and not used for Raga Identification. The paper provides comparison in the result of Raga Identification using Mel Frequency Cepstral Coefficient and various Pitch Detection Algorithms (PDAs) for Indian Classical Music.
Computation Task Assignment in Vehicular Fog Computing: A Learning Approach via Neighbor Advice

In the recent years, fog computing has been proposed as a promising paradigm to enhance the performance of latency-critical applications by hosting them within idle computation nodes (Fog Nodes, FNs) at the edge of the network. Such a paradigm has been further extended to the vehicular fog computing (VFC) scenario by leveraging available computing resources offered by modern vehicles, thus allowing them to process different types of computation tasks on behalf of onboard users or nearby vehicles. Within this context, the proper assignment of computation tasks to Vehicular FNs is an important issue that is currently under active research. To address this issue, online learning approaches, where the performances of the Vehicular FNs in terms of task execution delay are learnt via trial and error, are starting to gain in popularity. This is mainly motivated by the inherent uncertainty caused by mobility and fluctuating resource availabilities in VFC environments. However, since the process of learning from scratch in such a dynamic vehicular environment may lead to a degradation in the learning performance, this paper proposes the use of an advising mechanism, where a roadside unit (RSU) who has already learnt the performances of the vehicles within its range, uses its acquired knowledge to provide advice to a neighbor RSU who does not have enough experience allowing it to make efficient assignment decisions on its own. To evaluate this approach, we used realistic vehicular mobility traces to simulate the VFC scenario. The obtained results show that our proposed approach improves the learning performance compared to the case where no advice is leveraged.
Load-Dependent Rotating Performance of Motorized Spindles: Measurement and Evaluation Using Multi-Zones Error Map

The rotating performance of motorized spindles is critical to the dimensional accuracy and surface quality of the parts to be machined. This paper proposes a novel measurement strategy and an evaluation method of spindle’s rotating performance, considering the loads imposed on the spindle. To be specific, cutting loads are simplified as a unified function to guide the load simulation, and the load-induced error is eliminated using a position adjustment procedure. In a certain speed-load combination, the spindle’s rotating performance under the unified load function is measured using a data envelope method. To extend the assessment to all typical work conditions, an evaluation scheme for different speed and load combinations is proposed based on a visual ideal of Multi-Zones Error Map (MZE Map). The necessity of considering the load-dependent rotating performance is demonstrated via several tests conducted on proposed load simulation platform. Besides, the procedures for developing the MZE Map are performed, and the results express an overview relationship between rotating performance and different speed-load conditions. The proposed method provides a new approach to evaluate the rotating performance considering the actual load conditions. This load-dependent oriented evaluating strategy contributes to a more precise health evaluation and management method for motorized spindles in actual machining process.
Double Q–learning Agent for Othello Board Game

This paper presents the first application of the Double Q–learning algorithm to the game of Othello. Reinforcement learning has previously been successfully applied to Othello using the canonical reinforcement learning algorithms, Q–learning and TD–learning. However, the algorithms suffer from considerable drawbacks. Q–learning frequently tends to be overoptimistic during evaluation, while TD–learning can get stuck in local minima. To overcome the disadvantages of the existing work, we propose using a Double Q–learning agent to play Othello and prove that it performs better than the existing learning agents. In addition to developing and implementing the Double Q–learning agent, we implement the Q–learning and TD– learning agents. The agents are trained and tested against two fixed opponents: a random player and a heuristic player. The performance of the Double Q–learning agent is compared with performance of the existing learning agents. The Double Q– learning agent outperforms them, although it takes longer, on average, to make each move. Further, we show that the Double Q–learning agent performs at its best with two hidden layers using the tanh function.
Features Quality Impact on Cyber Physical Security Systems

With the rapid growth of the number of connected devices on the internet, a significant increase in internet and network applications in the last decade led to the appearance of function-based networks called the Internet of Things (IoT). Such interest and dependency come with exponentially increasing security threats. Those threats are very dynamic, and change continuously despite the fact that IoT devices may not go through the same speed for security fixes, updates, etc. As a result, research trends focus on utilizing data analytics to build intelligence and autonomous mechanisms to enable IoT systems to respond to threats in real time. To this end, our project goal is to evaluate methods to build fast and efficient intrusion detection systems (IDS) that meet the high scalability and dynamicity of IoT environments.In this research, we studied three different datasets used to represent IoT network traffic and used them as a benchmark to evaluate many IDSs’ performances. Results showed that features’ quality has an impact on classification models and thus IDS performance. Our results showed also that the optimum time-window of an IDS preferred to be long (decay factor 0.01 or 0.1) when evaluating the BotNet_IoT dataset.
Bias-Dependent Contact Resistance Characterization of Carbon Nanotube FETs

An extraction method for a bias-dependent contact resistance is presented and applied to simulation and experimental data of single and multi-tube carbon nanotube field effect-transistors (CNTFETs) with different channel lengths and Schottky barrier heights. The method relies on the individual device characteristics, i.e., it can be applied to different CNTFET technologies. Furthermore, it does not require the fabrication of test structures or challenging model calibration. The extracted values correspond to reference values in simulations and are as well similar to the ones obtained for the fabricated devices with other more expensive, technology-specific analytical and physics-based approaches. Hence, the extraction method presented here can be considered as an efficient and accurate alternative. By revealing the bias-dependence of the contact parameters, rarely reported in the literature, the nature of the Schottky barriers can be characterized and the contact transparency can de idenitifed.
Application of Deep Learning in Wood Classification

This paper presents a deep learning model for the classification of 25 precious wood species. 25 precious wood species were sampled, observed and photographed under a 20-fold magnifying glass after polishing, and then classified by professors to mark the pictures. We removed the blurred part of the original images and got the effective region of images. The 25 kinds of wood pictures were flipped, and 192 pictures were obtained for each type of wood. A total of 4800 pictures were collected, so the experimental data sets were obtained. In this paper, data sets are divided according to the ratio of 60%:20%:20%. Among them, the 60% of the dataset is used as the training set, 20% as validation set and 20% as testing set. The purpose is that using training sets to train the models as much as possible, and retain a certain number of images to validate and test the model. The corresponding structure and weight of the model were directly used which the pre-trained model of the VGG19 network trained on the ImageNet dataset, and applied to the classification of 25 precious woods. The deep learning network achieves a good fitting effect by learning from a large number of labeled wood sample data and extracting effective features. It also has a good generalization performance on the test set.
Tuberculosis Detection In Chest X-Ray Images Using Optimized Gray Level Co-Occurrence Matrix Features

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium Tuberculosis (MTB). Chest X-ray (CXR) image has been the main tool for detecting lung TB historically. CXR images are analyzed by radiologists to determine whether or not there are signs of TB in the lungs. The results of the analysis by radiologists in analyzing CXR images are influenced by the subjectivity of radiologists, such as experience from radiologists, conditions of observation, fatigue, and others. The subjectivity factor of the radiologist can be overcome by the computer aided diagnosis system. This paper proposed a TB detection system on CXR images using optimized Gray Level Co-Occurrence Matrix (GLCM) features as the input. GLCM is optimized using the Principal Component Analysis (PCA) and then classified using the Support Vector Machine (SVM). In this paper, CXR images were classified as normal, primary TB (PTB) and secondary TB (STB). The results of this paper indicate that the classification system with optimized GLCM as input has better performance than the classification system with regular GLCM as input. The classification system with optimized GLCM as input in the 8-fold cross validation test has an accuracy of 100% for the normal class, 98.72% for the PTB class and 98.72% for the STB class.
A Hybrid Approach Handwritten Character Recognition for Mizo using Artificial Neural Network

In the past decade we have seen a rapid advancement in object recognition, however Mizo Handwritten Character Recognition (MHCR) remains an untapped field. In this study a handwritten is collected from 20 different writers each consisting of 456 Mizo characters. In total 20 X 456= 9120 characters are used for testing the proposed system. In this process of recognition, the challenging factor is due to the fact that Mizo handwritten consists of vowels character that are made up of multiple isolated blobs (pixel) such as circumflex (^) on top vowel character. This make segmentation of each individual character difficult and challenging. Therefore, to implement MHCR, a hybrid approach character segmentation using bounding box and morphological dilation is combined, which merges the isolated blobs of Mizo character into a single entity. A hybrid approach feature extraction using a combination of zoning and topological feature is implemented. These features are used for classification and recognition. To evaluated the performance of MHCR model an experiment is carried out using 4 different types of Artificial Neural Network Architecture. Each Architecture is compared and analysed. The Back Propagation Neural Network has the highest accuracy with a recognition rates of 98%. This proposed hybrid technique will help in building an automatic MHCR system for practical applications.
An Evaluation of Model-Free Control Strategies for Quadrotor Type Unmanned Aerial Vehicles

The present study addresses two representative model-free control strategies namely, model-free intelligent PID (i-PID) and type-2 fuzzy adaptive PID in control of a quadrotor type vertical take-off and landing (VTOL) unmanned aerial vehicle. The objectives of this study are i) to summarize the modeling and flight control methods of quadrotor in some classifications, ii) to investigate merits and demerits of model-free control strategies, iii) to compare the control performance in terms of some quantitative performance criteria. The results illustrate the performance of such methodologies applied to the quadrotor system.
MEC-Driven UAV-Enabled Routine Inspection Scheme in Wind Farm Under Wind Influence

As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.
Comparison of Radiation Effects in Custom and Commercially Fabricated Resistive Memory Devices

The radiation response of TaO<sub><italic>x</italic></sub>-based resistive memory (RRAM) devices fabricated in academic (Set A) and industrial (Set B) settings was compared. Ionization damage from a <sup>60</sup>Co gamma source did not cause any changes in device resistance for either device type, up to 45 Mrad(Si). Displacement damage from a heavy ion beam caused a decrease in resistance at <inline-formula> <tex-math notation="LaTeX">$1 \times 10 ^{21}$ </tex-math></inline-formula> oxygen displacements per cm<sup>3</sup> in Set B devices in the high-resistance state (HRS); meanwhile, Set A devices did not exhibit any decrease in resistance due to displacement damage. Both types of devices exhibited an increase in resistance around <inline-formula> <tex-math notation="LaTeX">$3 \times 10 ^{22}$ </tex-math></inline-formula> oxygen displacements per cm<sup>3</sup>, possibly due to the damage at the oxide/metal interfaces. These extremely high levels of damage represent near-total atomic disruption, and if this level of damage was ever reached, other circuit elements would likely fail before the RRAM devices in this article. Overall, both sets of devices were much more resistant to radiation effects than the similar devices reported in the literature. Displacement damage effects were only observed in the Set A devices once the displacement-induced oxygen vacancies surpassed the intrinsic vacancy concentration in the devices, suggesting that high oxygen vacancy concentration played a role in the devices’ high tolerance to displacement damage.
Transient Voltage Distribution Characteristics of Ultrahigh-Voltage Transformer Windings under Impulses with Different Front Time

Lightning impulse withstand voltage test is conducted to examine the dielectric strength of turn-to-turn insulation under the circumstance of uneven voltage distribution, but large entrance capacitance of ultrahigh-voltage transformers makes it difficult to generate standard lightning impulse (SLI) in the on-site test, especially for the front time. In this paper, to evaluate the feasibility of substituting test impulses with longer front time for SLI, the transient voltages of the outmost turn of each disc of the 1000kV transformer windings under impulses with different front time were measured by the coupling capacitive sensor. The results showed that, for the low-voltage winding and the high-voltage winding, whose high voltage leads were located at the top and middle, respectively, the maximum voltage distributions were almost independent of the front time; while for the mid-voltage winding, whose lead was located at the bottom, the maximum voltage distribution changed as the front time varied. For all three windings, the front time of applied impulse had a little effect on the frequency components of the transient voltages, which could significantly influence the dielectric behavior of oil-paper insulation. Based on those results, the equivalence between the SLI and impulse with longer wavefront was discussed and so was the effect of very fast transient overvoltage on transformers.
Choosing the Reinforcement Learning Method for Modeling DDos Attacks

Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. In the field of DDoS attacks, as in all other areas of cybersecurity, the battle between the shield and the sword does not stop. Attackers are using increasingly sophisticated methods. Solution providers follow them, releasing new products in order to prevent malicious intent. Old tools stop working, new approaches and tools are required in order not to become a victim of cybercriminals. This document discusses the development path that tools for countering DDoS attacks go through under the influence of changing cybercriminal approaches. This paper discusses reinforcement training methods, gives a brief description of them, and examines the scope of reinforcement training in defense against DDoS attacks. The terminology of reinforced learning is also described, and an agent-based learning algorithm is described.
Dark Channel Prior and Global Contrast Stretching based Hybrid Defogging Image Technique

Since the foggy images undergo from low contrast and resolution due to diffusion of light and poor visibility conditions. So, Fog elimination is extremely preferred in both estimation picture making and computer vision applications. Proposed technique uses a Dark Channel Prior with contrast stretching to remove fog and improve the contrast of fog free image, respectively. Using Dark Channel Prior method one can directly take away the thickness of the haze and recover a high quality haze free image. Contrast Stretching is applied in the resulted image of dark channel prior method to improve the contrast of image. The noise that affect foggy image can also be ease by using the median low pass filter. By using this technique the visual quality and color of the foggy image can be correct effectively. Experiments are conducted on PSNR and RMSE parameters. Experimental Result shows that proposed method contains least average RMSE values and Higher PSNR values among other methods.
Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

One of the stage that performed during a maternal and fetal health monitoring is the calculation of fetal heart rate and uterine contraction using Cardiotocography (CTG). The aim of fetal health using CTG is to avoid morbidity and mortality in fetus at risk of hypoxia. This paper propose a hypoxia detection by using classification. In this study, we improve deep learning method in order to increase its capability in detecting hypoxia. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and adjusting classifier layers. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and increasing classifier layers. For whole dataset that achieved by proposed method is 81%. The improved DenseNet achieved 50%, 43%, 46% precision, recall and f1-score respectively for hypoxia class that is not achieved by standard DenseNet.
Aggregated Classifiers for State Diagnostics of the Technical Object

In this paper the method to plot the aggregated classifier is suggested to forecast the technical object operation. When receiving new information about object operation indicators it turns out to be necessary to update the classification model. It can be done using the pseudo-gradient procedure for updating basic model parameters and for updating the aggregated classifier structure as well if necessary.