Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Neuronal Entropy in a Network Model of the Cortico-Basal Ganglia during Deep Brain Stimulation

Neuronal entropy changes are observed in the basal ganglia circuit in Parkinson’s disease (PD). These changes are observed in both single unit recordings from globus pallidus (GP) neurons and in local field potential (LFP) recordings from the subthalamic nucleus (STN). These changes are hypothesized as representing changes in the information coding capacity of the network, with PD resulting in a reduction in the coding capacity of the basal ganglia network. Entropy changes in the LFP and in single unit recordings are investigated in a detailed physiological model of the cortico-basal ganglia network during STN deep brain stimulation (DBS). The model incorporates extracellular stimulation of STN afferent fibers, with both orthodromic and antidromic activation, and simulation of the LFP detected at a differential recording electrode. LFP sample entropy and beta-band oscillation power were found to be altered following the application of DBS. The ring pattern entropy of GP neurons in the network were observed to decrease during high frequency stimulation and increase during low frequency stimulation. Simulation results were consistent with experimentally reported changes in neuronal entropy during DBS.
Induction Motor Fault Diagnosis Using Discrete Wavelet Transform

In this paper, a general methodology is constructed by using experimentally measured stator current signals under full load condition of an induction motor. The measured stator current data for various single-and multi-electrical faults of the induction motor are investigated to obtain signatures for fault diagnosis. In this study, the discrete wavelet transform (DWT) is chosen for signal processing. The threshold and energy values at each decomposition level for the DWT analysis are evaluated. The threshold values appear to be more consistent than the energy values at different data windows of the measurement data, and thus, the threshold can serve as a reliable parameter for fault diagnosis.
A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks

With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.
Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis

The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI < 1 events/h), mild (1 events/h ≤ AHI < 5 events/h), moderate (5 events/h ≤ AHI < 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09–0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value < 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.
WORKING WITH VOCABULARY CARDS IN THE CLASSROOM

Vocabulary knowledge is a very important component of language proficiency and is crucial to student success in translational university settings. This article outlines how vocabulary flashcards were used as part of a vocabulary acquisition program in English for Academic Purposes Nursing Foundation program at university in the Middle East. Best practice recommendations by Nation (2013) are outlined regarding choosing words, and making and using word cards for explicit vocabulary learning. Explanations are given on how these recommendations were put into practice in the classroom. It is hoped that the explanations provided will serve to guide interested educators in developing vocabulary acquisition program using flashcards in their classroom. Article DOI: https://dx.doi. org/10.20319/pijtel.2018. 23.120132 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Enhanced Mobility Load Balancing Algorithm for 5G Small Cell Networks

The mobility load balancing is introduced to avoid the quality of service degradation due to imbalanced load through small cells of the network. This paper proposes a mobility load balancing algorithm for 5G small cell networks. It considers a load of neighbor cells and the edge-user equipments for each overloaded cell to determine load balancing efficiency factor. This factor specifies the sequence of overloaded cells for the algorithm operation. Therefore, by using load balancing efficiency factor, the offloading efficiency increases. The performance of our proposed algorithm is assessed through system-level simulations. Results show that the algorithm minimizes the standard deviation and increases the network throughput compared to algorithms from the literature.
A Robust Algorithm for Selecting Optimal Regularization Parameter Based on Bilateral Accumulative Area

The choice of regularization parameters is very important for the reconstruction result in the inverse problem of electrocardiology. In this study, the bilateral accumulative area detector is introduced to estimate the optimal parameter points of the L-curve and Generalized Cross-Validation method to Tikhonov regularization and truncated singular value decomposition. The experimental results suggest that this method can achieve high validity and high robustness for the estimation of regularization parameters compared to conventional methods.
Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals

People generally agree that emotion processing differs between male and female. However, current hypothesis of sex differences needs more objective evidence and quantitative assessment. In this paper, we investigate the sex difference in classifying ve emotions from eletroencephalograph and eye movement signals. We adopt two neural-network-based clas-si ers to objectively investigate sex differences from different perspectives. From experimental results, we nd the following three observations: (1) a general higher accuracy of same-sex strategy suggests sex-speci c factors have in uence on emotion classi cation; (2) both blink duration and frequency differ from female to male and they are negatively correlated under different emotional states; and (3) there are larger differences of brain activities in the Theta, Alpha, and Beta bands between male and female for disgust, sad, and neutral emotions.
Checkpoints Assignment on Cyber-Physical Digital Microfluidic Biochips for Early Detection of Hardware Trojans

Present security study involving analysis of manipulation of individual droplets of samples and reagents by digital microfluidic biochip has remarked that the biochip design flow is vulnerable to piracy attacks, hardware Trojans attacks, overproduction, Denial-of-Service attacks, and counterfeiting. Attackers can introduce bioprotocol manipulation attacks against biochips used for medical diagnosis, biochemical analysis, and frequent diseases detection in healthcare industry. Among these attacks, hardware Trojans have created a major threatening issue in its security concern with multiple ways to crack the sensitive data or alter original functionality by doing malicious operations in biochips. In this paper, we present a systematic algorithm for the assignment of checkpoints required for error-recovery of available bioprotocols in case of hardware Trojans attacks in performing operations by biochip. Moreover, it can guide the placement and timing of checkpoints so that the result of an attack is reduced, and hence enhance the security concerns of digital microfluidic biochips. Comparative study with traditional checkpoint schemes demonstrate the superiority of the proposed algorithm without overhead of the bioprotocol completion time with higher error detection accuracy.
Smart Sensors: Analysis of Different Types of IoT Sensors

Internet of Things (IoT) is a revolutionary technology. It is revolutionizing our world with trillions of sensors and actuators by creating a smart environment around us. In scientific research, sensors are considered as a prospective field. Ubiquitous sensing abilities offer shared information to develop a common operating picture. IoT sensors are efficiently used in various IoT applications for creating a smart environment. This paper presents several IoT sensors and also explains various sensor based IoT applications. Furthermore, after analyzing different sensor applications, this article enlightens which IoT application requires which type of sensor. In the future, this work will serve as the basis for further research work in the related area.
Automatic and Accurate Non-contact Obstructive Sleep Apnea Detection using Wavelet Information Entropy Spectrum

An accurate non-contact novel detection method for obstructive sleep apnea (OSA) has been developed and will be presented here. Typically, non-contact measurement is preferred for babies, brutally burnt patients, and patients with respiratory contagious diseases, etc. The developed method is based on wavelet information entropy concepts that would clearly classify apnea by the strong abnormality, complex structure and disorder of the patient’s respiratory signal. The accuracy of this method has been experimentally validated, and demonstrated over 93% accuracy using a bio-radar.
Feasibility of Long-Term Daily Life Electrocardiogram Monitoring Based on a Wearable Armband Device

A study on the feasibility of obtaining usable electrocardiogram (ECG) signals from a wearable armband during 24-hour continuous monitoring is presented. The wearable armband records 3-channel ECG and, unlike the conventional Holter monitors, it is convenient for long-term daily life monitoring because it uses no obstructive leads and it is based on dry (no gels) electrodes, which do not cause skin irritation. An optimal channel selector is presented, based on a linear classifier using features that are related to the ECG signal quality. In addition, this linear classifier is also used for artifact detection. The developed optimal channel selector and artifact detector are applied to 24-hour armband ECG recordings from 5 subjects. For reference comparison, the subjects also wore a Holter device. The armband obtained usable data during 51.07±13.54% (inter-subject mean ± standard deviation) of the non-bed recording time, and the mean heart rate was accurately (relative error with respect to the Holter less than 10%) estimated from the armband selected ECG channel from 94.39±3.41% of the usable data. During the bed recording time, the percentage of usable data was 93.54±2.92%, and mean heart rate was estimated accurately from 97.01±1.80% of those data. These results suggest that the armband device is potentially feasible for a long-term daily life heart rate monitoring based on the presented channel selector and artifact detector, especially during the bed time.
Conditional Adversarial Transfer for Glaucoma Diagnosis

Deep learning has achieved great success in image classification task when given sufficient labeled training images. However, in fundus image based glaucoma diagnosis, we often have very limited training data due to expensive cost in data labeling. Moreover, when facing a new application environment, it is difficult to train a network with limited labeled training images. In this case, some images from some auxiliary domains (i.e., source domain) could be exploited to improve the performance. Unfortunately, direct using the source domain data may not achieve promising performance for the domain of interest (i.e., target domain) due to reasons like distribution discrepancy between two domains. In this paper, focusing on glaucoma diagnosis, we propose a deep adversarial transfer learning method conditioned on label information to match the distributions of source and target domains, so that the labeled source images can be leveraged to improve the classification performance in the target domain. Different from the most existing adversarial transfer learning methods which consider marginal distribution matching only, we seek to match the label conditional distributions by handling images with different labels separately. We conduct experiments on three glaucoma datasets and adopt multiple evaluation metrics to verify the effectiveness of our proposed method.
The effect of curvature and torsion of inclusions on effective permittivity of composite materials

We consider the problem of finding the effective dielectric permittivity of a fiber composite, accounting for curvature and torsion of inclusions. For this purpose, we adopt the Maxwell-Garnett approach based on the solution of the electrostatic problem for a single inclusion in a homogeneous electric field. It is assumed that the diameter of the inclusion is small in comparison with its radius of curvature and torsion. The electric potential is sought in the form of a series in powers of the distance to the inclusion. As an example of the results, the effective dielectric constant of the composite material with inclusions of a helical-line shape is investigated.
A new task scheduling strategy based on improved ant colony algorithm in IaaS layer

In the IaaS layer of cloud computing, the cloud platform is required to implement efficient scheduling for large-scale tasks to meet the needs of both users and cloud service providers. In this paper, a multi-objective task scheduling model is established to optimize task completion time, cost and load balancing degree. Furthermore, an improved ant colony algorithm is proposed to solve this problem. The proposed algorithm firstly uses genetic operations to initialize the pheromone, and establishes an ant search model that can speed up convergence, while the pseudo-random proportional rule is used to select the next node of the path. Then, a weighted constraint function of time and cost is established to evaluate the quality of the scheduling’s solution, and the Analytic Hierarchy Process (AHP) decision is used to adjust the weighting factors. Moreover, a load balancing method is introduced in the path heuristic function of ant colony algorithm. Compared with other advanced algorithms, the proposed algorithm can significantly reduce task completion time and cost, while accelerating the convergence of the optimal solution and improving load balancing degree greatly.
Binary Grey Wolf Optimizer based Feature Selection for Nucleolar and Centromere Staining Pattern Classification in Indirect Immunofluorescence Images

In this work, an attempt is made to distinguish nucleolar and centromere staining patterns using Bag-of-Keypoint Features (BoKF) model and Binary Grey Wolf Optimization (BGWO) based feature selection. Fluorescent staining patterns are produced by Indirect Immunofluorescence (IIF) Imaging and the patterns considered for this study are taken from a publicly available online database. The IIF images are pre-processed using edge-aware local contrast enhancement method. The contrast enhanced images are subjected to BoKF framework and Speeded up Robust Feature keypoints are extracted. Further, the most significant features are identified using BGWO and are fed to k-Nearest Neighbor (kNN) for classification. The results show that the BGWO features are able to classify the nucleolar and centromere patterns with an average accuracy of 91.6%. Results also indicate that the prominent features obtained using BGWO can improve the discrimination performance of IIF staining patterns. Hence it appears that the BGWO based feature selection could enable the computer aided diagnosis of autoimmune diseases.
An Investigation of Dimensionality Reduction Techniques for EMG-based Force Estimation

In this paper, extracted features in time and frequency domain, from high-density surface electromyogram (HD-sEMG) signals acquired from the long head and short head of biceps brachii, and brachioradialis during isometric elbow flexion are used to estimate force induced at the wrist using an artificial neural network (ANN). Different hidden layer sizes were considered to investigate its effect on the model accuracy. Also, we applied two dimensionality reduction techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), on the feature set and investigated their effects on force estimation accuracy.
Development of Highly Constrained 1 m Ka-Band Mesh Deployable Offset Reflector Antenna for Next Generation CubeSat Radars

The emergence of CubeSats has opened up the possibilities of advanced space missions with lower costs and faster development times. A major factor that limits the functionality of CubeSats is the absence of high-gain antennas (HGAs) that can sustain a high data-rate link for communications or provide the required spatial resolution for remote sensing. In this work, we discuss the development of one of the largest apertures at Ka-band for CubeSats—a 1 m mesh deployable offset reflector antenna, with a stowed volume of 3U ( $10\times 10\times 30 \,\,\text {cm}^{3}$ )—to enable precipitation radars that can achieve the required spatial resolution while meeting the stringent mechanical constraints posed by the small CubeSat volume. We detail the critical aspects of this antenna design, including RF characterization, antenna fabrication, and measurement. We also describe a novel deployment mechanism that facilitates the packaging of such a large aperture in a small volume. The antenna demonstrated a measured efficiency of 60%, with a half-power beamwidth of 0.60° at 35.75 GHz. The cost reduction afforded by CubeSats makes launching a constellation of such radar based CubeSats practical, allowing higher temporal sampling rates, which is essential for the observation of weather processes with a short-time evolution.
Ground Segmentation From Outdoor Environments in Rural Areas

The understanding of the complexity of outdoor environments is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination and without a well-defined road. In this context, the detection of ground and obstacles plays a relevant role in giving the first impressions of the external surroundings to a machine. Furthermore, it can guide independent movements and decisions. In this study, we introduce a segmentation method that detects ground and non-ground points of complex scenes under different exposures to illumination, textures, and shading. We prepared a dataset with images collected from some environments in which trees are prominent obstacles. The proposed method uses contrast templates, statistical measures, and morphological operators to reach the ground segmentation. Experiments showed satisfactory results in which trees were well detected and the ground was efficiently segmented with the maintenance of the structure of the image.
Improved LEACH-M Protocol for Aerial Sensor Networks

When the node aircraft of AWSN (Aerial Wireless Sensor Networks) patrols around, the topology of the network changes in real time, and individual sensor node may fly away from their home clusters to cause communication failures. To ensure smooth network communication, this paper proposes an improved LEACH-M protocol for Aerial sensor networks. Under comparison with the traditional LEACH protocol, the protocol is improved based on the existing LEACH-M protocol and MCR protocol. An efficient solution for outlier node is proposed to ensure that it can re-home quickly and efficiently. The experimental results show that, by contrast with the traditional LEACH-M protocol and MCR protocol, the improved LEACH-M protocol extends network lifetime, increases the efficiency of information transmission, and optimizes its performance.
Dynamic Service Selection based on User Feedback in the IoT Environment

With the expansion of IoT technologies, the number of devices deployed around the world as well as the number of services is increasing rapidly. Service selection is a process to choose services that meet the functional and non-functional properties for each user. Some of the features of the IoT environment make this process more complicated. For example, user’s mobility constantly changes the connected service collections. Because the dynamic nature of the environment leads to changes in the QoS (quality of service), judging a service that satisfies the needs of users in the future is difficult based on previous QoS static values. Moreover, an IoT service relies on its device, which is affected by CPU processing power and battery capacity. Hence, long-term and stable service cannot be provided to users. In order to solve the above problems, we need a service selection method that can consider the quality of the service and reflect the service context in real time. In this paper, we propose a novel IoT service selection approach by using the linear reward inaction (LRI) algorithm to learn all subjective assessments from the latest user feedback and objective assessments of service. In addition, to reduce the effect of unreasonable feedback from users in different contexts, the similarity between users is considered in the LRI algorithm.
Inter-Beat Interval Estimation from Facial Video Based on Reliability of BVP Signals

Inter-beat interval (IBI) and heart rate variability (HRV) are important cardiac parameters that provide physiological and emotional states of a person. In this paper, we present a framework for accurate IBI and HRV estimation from a facial video based on the reliability of extracted blood volume pulse (BVP) signals. Our framework first extracts candidate BVP signals from randomly sampled multiple face patches. The BVP signals are then assessed based on a reliability metric to select the most reliable BVP signal, from which IBI and HRV are calculated. In experiments, we evaluate three reliability metrics and demonstrate that our framework can estimate IBI and HRV more accurately than a conventional single face region-based framework.