Miracle-dz/UniFAR-FTUs huggingface dataset:
A new method for the detection of epilepsy and epileptic seizures based on the variance of EEG signals and its derivatives with a simple kernel trick

In this paper, we propose a new method for the automatic diagnosis of epilepsy via encephalographic signals (EEG). Our objective is the detection of epilepsy and epileptic seizures through EEG of healthy subjects (H), epileptic subject (E) and epileptic subject during seizures (S). Two novelties are deliberated in this paper. In the first method, we have exploited EEG and its derivatives, which gives significant results from calculations of just three features, the variances of the signals and its first and second derivatives. In the second one, we have used a kernel trick that allows an implicit redescription of the extracted features, by the conversion of the nonlinear problem to linear space, which ultimately facilitates the classification step and gives reliable result in fast running time. The experimental test via the Bonn EEG dataset proves the efficiency of the proposed method, an accuracy of 100 % is achieved in seizures detection problem and of 99.8 % in epilepsy detection problem, moreover for the differentiation of three cases 99.85 % of accuracy was achieved.
Deep Learning based Ensemble Approach to Predict Student Academic Performance: Case Study

Traditional practices of education, medicine, and economics often use collective expert methods to find acceptable solutions to complex issues. The computer model of such methods is an ensemble classification which combines prediction of a number base classifier trained by various machine learning techniques. The predictive sample in the educational process is always not balanced - it leads to a shift in estimates. The basic idea of this work is that the binary forecasting on unbalanced datasets is carried out by a Meta-classifier - an ensemble of different classification algorithm (Decision tree J48, Naive base NB, Multilayer perception MLP, k- Neighbour Nearest k-NN and Support Vector Machine SVM) which trained by stacking, bagging and boosting procedures on SMOTE, RUS, ROS balanced samples. The experiments conducted on moderately unbalanced dataset of 350 tuples of student data with using simple voting Meta-ensemble models, show that the best overall accuracy A and F1 measure of minority class were achieved by the Meta-ensembles containing 5-15 members classifiers trained on samples prepared by SMOTE.It is also proposed that these of Meta-ensembles can be used to resolve quantification learning problem – novel task to predict collective performance. As in our recent works the deterministic outputs of Meta-ensemble members can be transformed to probabilistic outputs and then to prediction of class frequency. After analyzing the results of quantification, it becomes obvious that the most optimal design of the meta-ensembles is to collect pre-boosted classifiers trained on the SMOTE sample.
Application of Circuit-Breaker Standards in Short-Circuit Current Calculation for Generator Circuit-Breakers

Interruption of fault current with delayed current zeros (DCZ) is one of the peculiarities of generator circuit-breaker (GCB) that differentiates it from other medium-voltage and high-voltage circuit-breakers. This paper aims to provide a guideline for precise calculation of short-circuit current requirements for GCB. The methods described in IEEE and IEC standards are analyzed and their impact on the selection of GCB is investigated. In particular, the standard IEC 60909, IEEE C37.010 and standards specific for GCB application IEEE C37.013 which is superseded by new dual logo standard IEC/IEEE 62271-37-013 are the scope of this paper. Calculation of two main interrupting duties of GCB are addressed: 1) generator-source short-circuit currents and 2) out-of-phase fault currents. Analytical results are verified with the simulation results obtained from ATP-EMTP. In conclusion, the question of which standard shall be used to accurately evaluate the short-circuit current requirements for the selection of a GCB is well-answered.
An automated pH‐controlled culture system for laboratory‐based ocean acidification experiments

Using a spectrophotometric pH measurement system, an automated 12‐tank culture system was developed, which is capable of maintaining pH levels between 7.51 and 8.00 (local, in‐situ pH) within 0.02 pH units at 15.1°C. The precise control of pH in the individual culture tanks was achieved through the addition of CO2‐enriched seawater to unamended seawater. A feed‐back system, which automatically adjusted the mixing ratio of unamended and CO2‐enriched seawater, ensured that the pH in each tank was kept within 0.02 pH units of target values. After each tank adjustment, the spectrophotometric pH measurement was combined with a dissolved inorganic carbon measurement to automatically re‐calculate carbonate chemistry in each tank. The system was used to study the growth of the geniculate coralline alga Arthrocardia corymbosa at pH levels of 7.52, 7.76, and 7.98.
De-Authentication Attacks on Rogue UAVs

In this new commercial era, Unmanned Aerial Vehicles (UAVs) popularly known as drones have become easily accessible, and their possible unlawful use poses new security threats. Therefore, limiting the illegal use of UAVs in some regions, such as airports, military camps, nuclear power plants, and international borders has become highly recommended. In remote control systems, most commercially available UAVs rely on spread spectrum techniques such as direct sequencing and frequency hopping. This minimizes the impact of interference from adjacent communications systems and increases resistance to jamming.In this research, an efficient mechanism is designed to attack and disconnect Wi-Fi controlled UAVs using Raspberry Pi 3 motherboard (System On Chip-SOC). The proposed technique in this prototype uses the de-authentication process to attack UAVs Wi-Fi module that connects the flight controller in the UAV to the ground control station. The experimental results show this method can effectively be used to cease and disconnect the UAVs operating within a 50 m radius around the proposed prototype without disturbing any other non-target electronic signals around.
Potential Well Based FDSOI MOSFET: A Novel Planar Device for 10 nm Gate Length

A novel planar device having potential wells (PW) in the source and drain regions of MOSFET is reported. The device was realized by introducing wells in the BOX under source/drain regions of FDSOI MOSFET and is called PWFDSOI MOSFET. The simulations confirmed that the potential wells played key role in reducing the OFF current of the device by orders of magnitude in a scaled device having gate length of 10 nm while reducing the ON current marginally thus improving the ON-to-OFF current ratio (ION/IOFF ) of the device. An ION/IOFF ratio of ~7 x 105 was realized in a 10 nm gate length (6 nm node) PWFDSOI MOSFET having subthreshold swing (SS) of 87 mV/decade. The study was done on devices with unstrained silicon channel.
An IoT based Environment Monitoring System

In recent years, people are getting more conscious of the environment they are living in. This consciousness is driving the need to develop a reliable environmental monitoring system. An environmental air quality monitoring system also has industrial application. In mining or in heavy industry, there is a possibility of air contamination by different harmful gases. In such hazardous situations, an environmental monitoring system can potentially save the life of the workers. In such large-scale sensor deployment, there are data collection, data management, connection, and power consumption issues. IoT technology is specifically suited for this sort of need. This paper presents an IoT based framework that effectively monitors the change in an environment using sensors, microcontroller, and IoT based technology. Users can monitor temperature, humidity, detect the presence of harmful gases both in the indoor and outdoor environment using the proposed module. The data is stored in the web server and the user can access the data anywhere in the world through an internet connection. In the proposed work a web application is developed to provide vital information to the user. The user can also set up a notification for critical changes in the sensor data. In comparison to other closely related systems, the proposed system is a low-cost one, accurate and user friendly. It is also cloud-based and has easy monitoring and data visualization modules. The system has been evaluated in different stages. After testing all the functions in different conditions, it shows a high degree of accuracy and reliability.
Latent-Based Adversarial Neural Networks for Facial Affect Estimations

There is a growing interest in affective computing research nowadays given its crucial role in bridging humans with computers. This progress has recently been accelerated due to the emergence of bigger dataset. One recent advance in this field is the use of adversarial learning to improve model learning through augmented samples. However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet. This technique may also improve the performance of affective models, as analogously demonstrated in related fields, such as computer vision. To expand this analysis, in this work, we explore the use of latent features through our proposed adversarial-based networks for valence and arousal recognition in the wild. Specifically, our models operate by aggregating several modalities to our discriminator, which is further conditioned to the extracted latent features by the generator. Our experiments on the recently released SEWA dataset suggest the progressive improvements of our results. Finally, we show our competitive results on the Affective Behavior Analysis in-the-Wild (ABAW) challenge dataset.
Localization of Closely-Spaced Speech Sources Based on Small Microphone Arrays

Indoor source localization based on a microphone array is still a difficult problem especially for closely-spaced sources in strong reverberation environments, in such cases a large number of microphones are often utilized. Considering the industrial design and cost, source localization using a small microphone array in complex conditions is worthy of investigation. In this paper, we propose a small-size array based localization method under the condition of close proximity and strong reverberation. The essence of the proposed method lies in the detection of single source time-frequency (TF) points (SSPs) of each source, which simplifies the problem of multisource localization in close proximity into several single-source localization ones. The detected SSPs are then used for precise source localization in a sparse Bayesian framework, wherein the reflection coefficient can be simultaneously estimated by implementing a Bayesian dictionary learning. Experimental results confirm the effectiveness of our method in locating spatially-close sources compared with some state-of-the-art methods.
Microgrid Implementation in Unbalanced Nature of Feeder using Conventional Technique

This paper introduces a micro-grid implementation in a real time model of the IEEE 13-node test feeder to establish a quantified real-time platform. Solar photovoltaic electricity and wind turbines used in micro grids are considered as non-conventional energy sources. The main of this research work is to understand the useful concept for incorporating environmental friendly sources and analysis, where micro-grid performs along with the micro-grid response and the system reliability response as required. At the relevant bus, the renewable sources (i.e. PV system & wind generator) are linked to the imbalance the radial feeder. Micro-grid is a system that interacts with resources across the central grid through the distribution system but it can operate independently. Using the Matlab Simulink software to model a micro-grid with a three-phase unbalanced power supply together with battery as a storage device. The results demonstrate a significant improvement in the voltage and current profile of different time-shifting buses.
Audit Committee and Intellectual Capital Disclosures

The purpose of this paper is to examine the impact of the audit committee (AC) attributes on intellectual capital disclosures (ICDs) in the Gulf Cooperation Council (GCC) listed firms’ annual reports. The empirical evidence derived from the regression model indicates that there is a significant positive association between three attributes of AC (i.e. AC independence, AC meetings, and AC experience) and ICDs. This study contributes to the literature by providing new evidence from emerging stock markets on the importance of the AC in enhancing the level of ICDs.
In-Bed Biosignal Acquisition From Conventional Differential Pressure Sensor Based on Thermal Flow Principle

We propose a non-invasive pneumatic biosensing method that measures the pressure on a tube placed under a bed cushion. Although similar methods use microphones for pressure sensing, the high sensitivity of microphones may cause problems such as saturation, slow recovery from saturation, narrow dynamic range, and low signal-to-noise (S/N) ratio. However, the proposed method uses a differential pressure sensor based on the thermal flow principle which has been developed and commercialized recently. We derive theoretical models of both microphone and thermal flow measurements to show that the proposed method can solve the problems of methods based on microphones. Basic experimental results validate both models providing the time constant (65 s), saturation range (−80–+40 Pa), noise level (4.02 Pa), dynamic range (32 dB) and S/N ratio (21 dB) of the low-frequency microphone developed for biosignal measurement and the superior time constant (0.012 s), saturation range (−500–+500 Pa), noise level (0.82 Pa), dynamic range (62 dB) and S/N ratio (56 dB) of the proposed method based on a differential pressure sensor. Furthermore, the effective S/N ratio in the measurement of heart beat signal was also compared between the low-frequency microphone and the proposed method. The former showed S/N ratio (22.1 dB), and the latter showed S/N ratio (28.1dB).
Transfer Learning for Breast Cancer Classification using Small Dataset of Ultrasound Images

Globally Breast cancer has been identified as probably the most deadly type of cancer among women. This proposed work implements a sophisticated Breast Cancer Prognosis from Ultrasound Dataset using Transfer Learning. The two great challenges for the Researchers are Breast cancer analysis and the Prognosis. Malignancy patients are influenced by many aspects. All these factors and the outward symptoms in the individual are recorded making use of scanning devices such as Ultrasound pictures, histopathology, cytology, mammogram, etc. After that this data has been used for the medical diagnosis of cancer utilizing the Transfer learning method. This paper aims to compare & analyze our suggested model Transfer learning and deep learning techniques for Breast cancer Recognition from Ultrasound images predicated on parameters viz. Accuracy, Precision, Losses, Recall, and Area under Curve (AUC). The outcome of the proposed method results in best accuracy and obtains minimum losses when dealing with machine learning and deep learning approaches for the diagnosis of breast cancer in Ultrasound Images.
Bioscouring of cellulosic textiles

A study on bioscouring of cotton yarn with the pectinase enzyme was carried out and the results compared with the usual alkali scoured material. The results indicate that the bioscoured yarn has lower percentage of non-cellulosic constituents (impurities), higher tenacity, higher absorbency and the same whiteness after hydrogen peroxide bleaching as compared with the Solomatic bleached yarn. A similar study on cotton fabric indicates that a combined desizing and bioscouring is not feasible at least at ambient temperature. More work, however, is required to compare bioscouring followed by bleaching with the Solomatic or two step scouring and bleaching methods. One interesting finding is that the efficiency of washing of the desized fabric improves on adding ethylene diamine tetraacetic acid sequestrating agent in the wash liquor. Additional advantages of bioscouring are lower energy cost and more easily biodegradable effluent in comparison to alkali scouring.
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment

With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we collect a largescale dataset that was designed for remote heart rate estimation recorded with various illumination variations to evaluate the performance of the rPPG algorithm (Green, ICA, and POS). We also propose a low-light enhancement solution (technical solution) for remote heart rate estimation under the low-light condition. Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment. The proposed solution for rPPG process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal.
Location Accuracy Estimates of an Ingested Wireless Endoscopy Capsule based on an RSSI Cost Function

The accurate location of an internal radio transmitter is important for long-term patient monitoring. An inverse solution for the surface field attenuation-based algorithm provides a simple position estimation but is degraded by noise and movement artefacts. Using the minimum cost function between the predicted and experimentally received signal strength indicators (RSSI) for four skin-surface receivers, the localization accuracy within 10 mm was achieved even with movement and external Gaussian noise. The relationship between RMSE value of each coordinate component in 3D under various noise levels provides a location accuracy estimate.
Architectures and emerging trends in Internet of Things and Cloud computing: a literature review

The Internet of Things (IoT) enables interconnection between different types of devices using information and communication technologies. This interconnection generates a large amounts of data that require transmission, analysis and storage. The benefit of Cloud Computing has extended the capacities of devices since they are limited in terms of storage capacity and computing power. In order to ensure the integration between IoT and Cloud Computing, different architectures of an IoT system in a cloud environment have been proposed. The purpose of this article is to present these architectures, expose a synthesis of them to find out their emerging trends, and also to identify the layers that are common to them. The results presented are mainly the identification of these layers, and the emerging trends in IoT systems in a cloud environment, which aim mainly to bring the cloud resources more closely to devices, in order to reduce latency.
Detection-Verification-Cascade-Based Low-Cost Precise Vehicle Positioning Exploiting Extended Digital Map

This paper proposes a map-matching based precise positioning system for highways. The proposed system fuses multiple sensors such as a camera, conventional GPS, and vehicle motion sensors widely adapted in mass-produced vehicles. While conventional map-matching based systems consume a large amount of computation because of the landmark detection, the proposed system achieves both precise positioning and small computations by introducing a detection-verification-cascade strategy: the proposed system generates vehicle position candidates per lane by utilizing lane endpoints detectable with small computation, and then it selects the candidate on an ego-lane by verifying the existence of road signs within the region of interest generated by a map and each candidate. To reduce the computation further, the proposed system adapts an extended Kalman filter (EKF) as a positioning filter instead of a particle filter. As the noise distribution of the EKF is limited to a unimodal, the proposed system maintains multiple EKFs to track the candidates for each lane until the ego lane is identified. After ego-lane identification, the system leaves only one EKF to track the candidate on an ego-lane. The proposed system achieves 0.17m average positioning error in highway situations and it takes about 53ms to process the whole procedure from landmark detection to position estimation in low-end hardware equipped with a cortex-A9 CPU whose clock is 1.0 GHz.
Efficient Auto key based Encryption and Decryption using GICK and GDCK methods

Security services and share information is provided by the computer network. The computer network is by default there is not security. The Attackers can use this provision to hack and steal private information. Confidentiality, creation, changes, and truthful of data is will be big problems in the network. Many types of research have given many methods regarding this, from these methods Generating Initial Chromosome Key called Generating Dynamic Chromosome Key (GDCK), which is a novel approach. With the help of the RSA (Rivest Shamir Adleman) algorithm, GICK and GDCK have created an initial key. The proposed method has produced new techniques using genetic fitness function for the sender and receiver. The outcome of GICK and GDCK has been verified by NIST (National Institute of Standards Technology) tools and analyzes randomness of auto-generated keys with various methods. The proposed system has involved three examines; it has been yield better P-Values 6.44, 7.05, and 8.05 while comparing existing methods.
Biological Sample Imaging by Ptychography with Laterally 1-D Scanning

The most biological samples are transparent and have no amplitude modulation for the incident wave. It is hard to observe the sample without dyeing in traditional microscope. Phase retrieval algorithm can reconstruct complex-valued sample with several diffraction intensity patterns. In this paper, a lensless imaging method is designed by diffuser 1-D moving, which decreases acquisition dada time, simplifies the experimental equipment and reduces experiment cost compared to typical ptychography based on 2-D shifting. A diffuser is placed into imaging system to improve resolution, which brought higher frequency information into the captured patterns via scattering effect. In field reconstruction, the additional amplitude constraint is added into the iterative phase retrieval algorithm. The proposed algorithm achieved faster convergence and reconstructed high-quality images. For the biological sample image, a wide field of view was realized with 20 diffraction patterns.