Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Quantification and Simulation of Hippocampal Images Treated with Kainic Acid from Rat Brain Slices

In the animal models, Kainic acid (KA) has been commonly used in inducing epilepsy especially Temporal lobe epilepsy (TLE) which is more common in adult human. Following administration of KA, there are several biochemical, mitochondrial and neuronal dysfunctional changes occurred in many brain regions including hippocampus and its sub region namely CA1, CA3 and in the dentate gyrus (DG). some dysfunctional changes can be studied and quantified by histopathological methods. This study aimed to quantify the microscopic images stained with hematoxylin and eosin (H&E) after treated with KA in different concentrations. Following analysis, the neurodegenerative changes induced by higher concentrations of KA showed significantly different in its Feret diameter, shape descriptors and integrated density compared to lower dose of KA. In order to optimize the KA induced neurodegeneration a model with simulation study is necessary to improve the KA doses and treatment duration, which might be helpful to save the time.
Analyzing Tweets on New Norm: Work from Home during COVID-19 Outbreak

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of employee concerns due to pandemic.
Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM

Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women. Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate. This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM. First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction. Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches. Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built. Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%, an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%. Consequently, it has important practical value and scientific research significance.
Transmission Power Control for Anchor-Assisted Localization in Wireless Sensor Networks

Energy-efficiency and localization play significant roles in Wireless Sensor Networks. Localization provides geographical information about the sensors deployed in a certain geographical region. Single mobile anchor-assisted localization is considered as an energy-efficient approach where numerous GPS-equipped sensors are removed to reduce the consumption of energy in the network. In recent years, various path planning mechanisms for mobile anchor have been proposed with single transmission power assigned to the anchor. Preservation of power is critical while performing mobile beacon-assisted localization so as to increase the lifetime. Providing the ability to dynamically adjust transmission power for the anchor node does not only reduce the energy consumption but also improves the overall performance of the localization. In this article, we propose a new trajectory, Anchor-power, that provides transmission power control for the mobile beacon. Anchor-power efficiently adjusts the transmission power of the beacon node for the reliable transmission of beacon messages. Experimental results reveal that our proposed approach enables unknown sensors to locate themselves with better accuracy in comparison with the existing models. Anchor-power improves performance in terms of accuracy and energy efficiency along with the localization success ratio and localization time.
Outphasing Class-E/ F2 Power Amplifier using a Quadrature Hybrid as Non-Isolating Combiner

This paper presents a GaN HEMT outphasing power amplifier (PA), based on a class-E/ F2 topology with reduced sensitivity to variable resistive load operation, designed to provide output power control over an extended range with high efficiency. A 90-degree hybrid coupler is used as non-isolating combiner, incorporating an appropriate reactive termination on its fourth port. Besides its many intrinsic advantages, the mutual load modulation trajectories may be rotated by simply adjusting a capacitor value or alternatively the length of an open-circuited transmission line. Once implemented at UHF band, the outphasing PA provides a drain efficiency higher than 70% up to an output power 10 dB below its peak value (42.2 dBm).
Individual Tree Crown Detection using GAN and RetinaNet on Tropical Forest

The detection performance of tree crowns in forest environment has not been satisfactory compared to common objects, especially using aerial RGB imagery. Previous methods regarding Individual Tree Crown Detection (ITCD) utilizes different data sources to improve the detection rate due to the noisy image. Image enhancement methods such as super-resolution provide a solution to the noisy image by reconstructing the image using the low-resolution image. Generative Adversarial Network (GAN)-based model has shown success in super-resolution techniques. However, the GAN-based model created artefacts that may hinder the accuracy of the detection. In this paper, a noise-cancelling GAN-based model is proposed by averaging the weights of a compressed image and non-compressed image. The proposed method forces the network to discriminate the noise to generate a more photorealistic image. This method is inspired by super-resolution GAN (SRGAN) architecture with Residual Dense Network as the generator network. A two-stage object detection RetinaNet model is then used to detect the individual tree crowns in a sequential fashion. Extensive experiments have been conducted on a self-assembled tree crown dataset which showed the proposed model is more superior than a non-enhanced model with 0.6017 and 0.5908 respectively. Based on the results of the proposed method, the super-resolution technique can be used in conjunction with object detection algorithm to improve the detection in ITCD to improve the detection rate.
A Surface Acoustic Wave Pressure Sensor Based on Micro-Corona-Discharging Effect

This paper proposes a gas pressure sensor based on the field-induced electron emission and micro-corona phenomenon on the metal interdigitated (IDT) electrodes of the surface acoustic wave resonator (SAWR). The SAWR was fabricated on ST-cut quartz substrate with aluminum IDT electrodes and reflectors. With the micron-scale finger-gap of IDT electrodes covered with SiO2 passivation layer, the enhanced field-electrons emission from the edges of IDT fingers result in the micro-corona-discharging and thus the change in the dynamic resistance of SAWR. A high Q-factor SAWR was fabricated and the pressure-dependent S11 were tested and analyzed using this SAWR. Under gas pressure range from 0 ~ 601 kPa, the dynamic resistance of the SAWR varies from 24.569 Ω to 41.589 Ω at 25°C. The resistance rate reaches 69.3%, which is far higher than that of traditional piezoresistive sensors.
An Integrated Microfluidic System for Early Diagnosis of Breast Cancer in Liquid Biopsy by Using Microrna and FET Biosensors

Breast cancer is one of the leading cancers among women globally with a high mortality rate if not diagnosed in early stages. Liquid biopsy could serve as a non-invasive method for early diagnosis of biomarkers (such as microRNA-195) in blood associated with breast cancer. In this study, we developed an integrated microfluidic system for detecting targeted microRNA-195 by using field-effective-transistor biosensors to quantify an extremely low concentration of microRNA after extraction of extracellular vesicles and microRNAs in plasma. The entire process could be automated with a limit of detection as low as 1 fM. It could be therefore promising for accurate and rapid diagnosis of breast cancer.
Early Prediction of Sepsis using Machine Learning

Sepsis is a fatal disease caused by infection. It has a significantly high mortality rate, particularly for patients in the ICU. The early and accurate detection of Sepsis is crucial as delayed treatment causes a sharp increase in the mortality rate. The proposed research aims to develop a classifier that accurately predicts Sepsis up to six hours before the clinical diagnosis of the disease. This is achieved using the patient’s EMR, vital signs and demographics. The research shows several imputation techniques and proposes a new filling algorithm known as Mixed Filling. The main features contributing to the classifier’s predictions have been described, thereby making the model more interpretable for medical personnel. Six models namely Random Forest, Logistic Regression, Light Gradient Boosting Machine, eXtreme Gradient Boosting, Neural Network and Long Short-Term Memory have been investigated for the classification of patients. The evaluation metrics that have been obtained are unprecedented and can be extremely useful for the timely and accurate prediction of Sepsis.
Multivariable Sensor Based on Mxene and Machine Learning for Selective Detections of VOCs

Two-dimensional transition metal carbides/nitrides, known as MXenes, have recently received significant attention for gas sensing applications. However, Mxenes have strong adsorption to many types of volatile organic compounds (VOCs), and therefore gas sensors based on MXenes generally have poor selectivity. Herein, we developed a Ti3C2TX based multivariable sensor which allows identification of different VOCs, and concentration prediction of the target VOC in variable backgrounds. The sensor’s responses from the broadband impedance spectra create a unique fingerprint for each VOC. Based on the methodologies of principal component analysis and linear discrimination analysis, we demonstrate highly accurate identifications for different types of VOCs and mixtures using this multivariable sensor. Furthermore, we demonstrate an accuracy of 93.2% for the prediction of ethanol concentrations in the presence of different concentrations of water and methanol. The high level of identification and concentration estimation shows the great potential of MXene based multivariable sensor for detection of target VOCs in the presence of known and unknown interferences.
Use of Mobile EEG in Decoding Hand Movement Speed and Position

With the majority of brain–computer interface (BCI) research currently restricted to the controlled settings in labs, there is a growing interest to study the feasibility of BCI applications in the real world. This interest is largely driven by the availability of ergonomic electroencephalography (EEG) recording devices. In this article, we investigate the feasibility of applying a commercial EEG device in motor decoding and propose a signal processing strategy to classify and reconstruct hand movement speed and position. An experiment is designed to simultaneously record EEG and hand position, while the user executes movement toward the left or right direction at two different speeds. A visual interface is designed to guide the user to perform the task under each condition. Data are recorded from 21 subjects. The classification of direction-dependent and -independent speeds is implemented using spatial and spectral features. An optimized movement parameter estimation strategy is proposed to reconstruct the instantaneous position and speed of the hand from EEG. Average performances of <inline-formula><tex-math notation="LaTeX">$73.36\ ({ \pm \! 11.95})\% $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$69.46\ ({ \pm \! 13.39})\% $</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$68.98\ ({ \pm \! 14.79})\% $</tex-math></inline-formula> are obtained for direction independent, right and left direction fast versus slow classification, respectively. The average correlation between recorded and reconstructed hand position and speed along the <italic>x</italic>-axis is in the range of [0.22, 0.57] and [0.26, 0.58], respectively. The reported results validate the use of commercial-grade EEG and low-frequency components of EEG relevant to motor decoding for real-world motor kinematics BCI applications.
Cost and Energy Efficient Distributed Computation: Wireless Sensor Networks on Uniform Grid

Wireless Sensors that are small in size and limited battery capacity are widely used for several different applications. For this reason energy efficient as well as cost of the overall network are the two main factors influencing the Wireless Sensor Networks (WSN) performance. In this research paper, in Wireless Sensor Network, energy efficient placement of sensors on the uniform rectangular grid (as sensor field) is proposed. Also, distributed computation of minimum and/or maximum over such sensor field is discussed (for minimizing computations and communications delay).
A Review on Statistical Importance and Biomarkers Identification in Hashimoto Thyroiditis Disease

Alter in biological media can be identified using biomarkers. For both pathogenesis and epidemiology, the biomarker plays an important role. Antecedent biomarkers are partially associated with the etiology. With the use of computational techniques for statistical analysis, better results can be possible. Irradiation, selenium, vitamin D, chemical issues like halogenated organochlorines, transthyretin are biomarkers of exposure in the thyroid disorder, where these biomarkers may or may not involve directly in the etiology. Biomarkers of thyroid disease include image-guided diagnostic tests like ultrasound and sonogram. Invasive diagnostic tests such as ultrasound-guided FNA, biopsy, hormone test, and antibody test are putative biomarkers of thyroid disorder. TSH test, Thyroperoxidase antibodies (TPOAb), Thyroglobulin Antibodies (TgAb), and Thyroid-stimulating hormone receptor antibodies (TRAb) are hormone and antibody test plays a significant role in the diagnosis of Hashimoto Thyroiditis (HT). This study explores the knowledge of Hashimoto thyroiditis and delineates novel biomarkers associated with it.
Energy Storage Integrated Solar PV Arrays Based Microgrid Control Unified to 3P4W Grid

This work presents a new control for a battery energy storage (BES) integrated solar photovoltaic (PV) arrays-based microgrid (MG) synchronised to the three phase four wire (3P4W) grid. The maximum power is obtained via two PV arrays through distinctive maximum power point (MPP) tracking technique. A boost converter is utilized to connect the first PV array at the main voltage source converter (VSC) DC link, where a BES is also linked to it. In the single stage topology, the second PV array’s output is integrated directly at ancillary VSC’s DC link. The main VSC’s control approach is based on the current control technique under the grid interactive mode and during the grid discontinuation, it shifts to the voltage control approach under an islanded mode of operation. The rating of MG is enhanced as the terminals of each VSC in parallel, are connected at the point of common coupling (PCC). The nonlinear loads are integrated VSC’s terminals. The solar power is fed to the grid in the grid interactive mode and the system’s operational reliability is enhanced in the standalone mode. The main VSC’s dual mode smooth control is implemented via the power electronics switch. At PCC, the main VSC voltage control, sustains the frequency and voltage, therefore, the ancillary VSC’s, operation is always with the current control. An incessant power is distributed to the load side. The utilization of PV array power feed-forward term in the current control of VSCs enhance the dynamic performance. The system compensates the neutral current and takes care power quality concerns i.e. alleviation of current harmonics and power factor improvement. In the MATLAB environment, the microgrid behaviour is perceived satisfactory under steady state and dynamic circumstances such as, solar power perturbation, load unbalance and load perturbation.
Design and Analysis of an Ultra-Broadband Polarization-Independent Wide-Angle Plasmonic THz Absorber

Broadband metamaterial absorbers in the terahertz (THz) range are of great importance because of their applications, especially in the field of THz detection and imaging. Characteristics of an ideal absorber include wide bandwidth and insensitivity to polarization and incidence angle. Here we propose a new structure of an ultra-broadband THz absorber by adding an excess dielectric layer on a metamaterial structure with two stacked chromium square resonators. The excess layer besides protecting chromium resonator from oxidation creates an extra Fabry–Pérot cavity that extends the bandwidth of the structure. The relative bandwidth of this structure for 90% absorption exceeds 150% and covers frequencies ranging from 0.52 to 3.66 THz, which is an advantage over the works reported in the literature review. The number of resonating layers is decreased related to the earlier works too. The proposed structure is not sensitive to the polarization of the incident radiation, at the same time its absorption is almost insensitive to the incident angle too. For both TE and TM polarization the absorption stays more than 80% up to 60° of the incident angles. Furthermore, the insensitivity of the proposed device to misalignment between layers is evaluated for misalignment up to $28~\mu \text{m}$ . The results show that the structure is insensitive to misalignment which makes its fabrication easy. We have simulated the proposed structure using the numerical finite element method (FEM). Also, we have extracted a circuit model for the suggested absorber which is in accordance to the numerical model and can be used to reduce the designing process time. Correspondingly, we efficiently explain the absorption mechanism using the circuit model and magnetic field analysis in the structure. We have shown that the broad bandwidth of the structure is the result of coupling between various modes of Fabry–Perot (FP) cavity resonances and surface plasmons (SPs). The FP resonances are coupled through the gap resonators between the different layers. This means absorbers are more compact than the convectional $\text{n}\lambda $ /4 wave plate absorbers.
ECCNet: An Ensemble of Compact Convolution Neural Network for Pain Severity Assessment from Face images

Pain is an indication of physical discomfort, and its assessment is crucial for the medical diagnosis of the patient. Currently, the self-report are known to be the gold standard for pain assessment. However, it is highly subjective and prone to human errors. Therefore, in this paper facial expression based fully automated pain severity assessment system is proposed. Although significant work is done within the pain research area from facial expressions, the volume of the research has been conducted for either binary (pain/no pain) or four class classification problems. Moreover, previous approaches utilized a single neural network with a large number of trainable parameters. A single deep neural network might not achieve optimum performance due to instability. Hence, in this study Ensemble of Compact Convolutional Neural Networks (ECCNet) has been proposed for assessing various pain intensities from the facial images. The proposed system represents an effective way of boosting the performance of the pain classifier by combining various heterogeneous compact CNNs into an ensemble. The proposed ECCNet system utilizes multiple CNN topologies along with different configuration settings. Here, we have suggested that learning of multiple heterogeneous compact CNNs results in more generalized pain classifiers than learning from independent baselearners. Given this, the proposed system utilizes three compact CNNs (i.e. variant of VGG, MobileNet, and GoogleNet) and integrated their predictions using the average ensemble rule. Moreover, various augmentation techniques are used to improve the generalization capability of each network. The proposed system has been evaluated extensively on the UNBC-McMaster shoulder pain dataset for five-level of pain intensities. The experimental results demonstrate the significance of the proposed ensemble method, which achieved 91.41 % of F1-score for pain severity assessment.
Smart Manufacturing Scheduling System: DQN based on Cooperative Edge Computing

In this paper, Deep Q-Network (DQN) was adopted to solve the Job shop Scheduling Problem (JSP) in the smart factory process. On the other hand, cloud computing has sensitive issues in the manufacturing process such as communication delay time and security problems. Research on various aspects of introducing an edge computing system to replace it has been conducted. We propose cooperative scheduling among edge devices in a Multi access Edge Computing (MEC) structure for scheduling without the help of a cloud center in a smart factory edge computing environment. Moreover, efficient DQN is used for experiments based on transfer learning data, and the proposed framework is compared and analyzed with existing frameworks from the perspective of provider a smart factory service.