Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Fidelity and High-Efficiency Digital Class-D Audio Power Amplifier

This study presents a high-fidelity and high-efficiency digital class-D audio power amplifier (CDA), which consists of digital and analog modules. To realize a compatible digital input, a fully digital audio digital-to-analog converter (DAC) is implemented on MATLAB and Xilinx System Generator, which consists of a 16x interpolation filter, a fourth-order four-bit quantized delta-sigma (ΔΣ) modulator, and a uniform-sampling pulse width modulator. The CDA utilizes the closed-loop negative feedback and loop-filtering technologies to minimize distortion. The audio DAC, which is based on a field-programmable gate array, consumes 0.128 W and uses 7100 LUTs, which achieves 11.2% of the resource utilization rate. The analog module is fabricated in a 0.18 µm BCD technology. The postlayout simulation results show that the CDA delivers an output power of 1 W with 93.3% efficiency to a 4 Ω speaker and achieves 0.0138% of the total harmonic distortion (THD) with a transient noise for a 1 kHz input sinusoidal test tone and 3.6 V supply. The output power reaches up to 2.73 W for 1% THD (with transient noise). The proposed amplifier occupies an active area of 1 mm2.
Effective data pre-processing for AutoML

Data pre-processing plays a key role in a data analytics process (e.g., supervised learning). It encompasses a broad range of activities that span from correcting errors to selecting the most relevant features for the analysis phase. There is no clear evidence, or rules defined, on how pre-processing transformations (e,g., normalization, discretization, etc.) impact the final results of the analysis. The problem is exacerbated when transformations are combined into pre-processing pipeline prototypes. Data scientists cannot easily foresee the impact of pipeline prototypes and hence require a method to discriminate between them and find the most relevant ones (e.g., with highest positive impact) for their study at hand. Once found, these pipelines can be optimized using AutoML in order to generate executable pipelines (i.e., with parametrized operators for each transformation). In this work, we study the impact of transformations in general, and the impact of transformations when combined together into pipelines. We develop a generic method that allows to find effective pipeline prototypes. Evaluated using Scikit-learn, our effective pipeline prototypes, when optimized, provide results that get 90% of the optimal predictive accuracy in the median, but with a cost that is 24 times smaller.
UmakaData Extension: Toward Realization of a Practical SPARQL Endpoint Discovery Service for Life Sciences

UmakaData shows a list of SPARQL endpoints that provide life science data with reliability scores, called Umaka scores, concerned with properties such as data freshness, accessibility, and performance. UmakaData monitors 72 SPARQL endpoints and scores these endpoints by executing SPARQL queries daily. Recently, in order to realize a class and property catalogue service for each endpoint that helps users write suitable SPARQL queries, an RDF data schema explorer called LOD Surfer crawler accessed SPARQL endpoints that were ranked in the top 50 for Umaka scores. This poster presents our current progress on the Umaka data service and its recent extension.
3D Mesh Model Classification with a Capsule Network

With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimensional representation as a complex data structure provides an effective shape approximate representation for the three-dimensional object. Although the traditional method can extract the characteristics of the three-dimensional object through the graphical method, it cannot be applied to more complex objects. However, due to the complexity and irregularity of the mesh data, it is difficult to directly apply convolutional neural networks to 3D mesh data processing. Considering this problem, we propose a deep learning method based on a capsule network to effectively classify mesh data. We first design a polynomial convolution template. Through a sliding operation similar to a two-dimensional image convolution window, we directly sample on the grid surface, and use the window sampling surface as the minimum unit of calculation. Because a high-order polynomial can effectively represent a surface, we fit the approximate shape of the surface through the polynomial, use the polynomial parameter as the shape feature of the surface, and add the center point coordinates and normal vector of the surface as the pose feature of the surface. The feature is used as the feature vector of the surface. At the same time, to solve the problem of the introduction of a large number of pooling layers in traditional convolutional neural networks, the capsule network is introduced. For the problem of nonuniform size of the input grid model, the capsule network attitude parameter learning method is improved by sharing the weight of the attitude matrix. The amount of model parameters is reduced, and the training efficiency of the 3D mesh model is further improved. The experiment is compared with the traditional method and the latest two methods on the SHREC15 data set. Compared with the MeshNet and MeshCNN, the average recognition accuracy in the original test set is improved by 3.4% and 2.1%, and the average after fusion of features the accuracy reaches 93.8%. At the same time, under the premise of short training time, this method can also achieve considerable recognition results through experimental verification. The three-dimensional mesh classification method proposed in this paper combines the advantages of graphics and deep learning methods, and effectively improves the classification effect of 3D mesh model.
Emotion and sentiment analysis of tweets using BERT

The huge diffusion of social networks has made available an unprecedented amount of publicly-available user-generated data, which may be analyzed in order to determine people’s opinions and emotions. In this paper we investigate the use of Bidirectional Encoder Representations from Transformers (BERT) models for both sentiment analysis and emotion recognition of Twitter data. We define two separate classifiers for the two tasks and we evaluate the performance of the obtained models on real-world tweet datasets. Experiments show that the models achieve an accuracy of 0.92 and 0.90 on, respectively, sentiment analysis and emotion recognition.
Artificial Intelligence based Driver Vigilance System for Accident Prevention

Accident prevention is the key facet of road safety. As the number of road accidents in Bangladesh is rising exponentially, precautionary steps are required to prevent this from happening. Many researchers are working on accident prediction models that are commonly used in road safety research. Artificial intelligence (AI) is particularly important in many real-world applications where data are not consistent and are affected by random changes. In this work, we propose an AI-based driver vigilance system for assisting drivers with accident prevention. The system detects drowsiness of the driver from dash camera using the Convolutional Neural Network algorithm; detects anomalies in the heartbeat using two-layer long short term memory algorithm and detects over-speed using GPS and front camera. The suggested model uses a Neuro-Fuzzy controller to integrate these inputs and generates alerts and controls brakes if necessary using drowsiness, heartbeat and speed variables.
An Anonymous Key Distribution Scheme for Group Healthcare Services in 5G-Enabled Multi-Server Environments

Fifth generation (5G) mobile technology enables a new kind of network that provides high peak data rates, ultra-low latency communication and high user density. Electronic healthcare (e-health) allows the data to be stored and shared in a highly efficient and flexible manner. Group e-health services help in improving long-term results of the treatments due to its collaborative characteristic. The services including real-time remote patient monitoring and transmission of large health data files can be facilitated by e-health systems enabled with the 5G network. Since the communication channel is open, security and privacy in the system should be taken into account. Our work proposes a key distribution scheme for group healthcare services in 5G network environments. We construct various healthcare domains and apply the proposed scheme to the group services. The paper also introduces Single Sign-On (SSO), a cost-efficient solution, for multi-server architecture of the constructed system. Security and privacy of the scheme are enforced by a three-factor authentication mechanism (integrating smart card, password and biometrics) and strong user anonymity property. We provide security proof of the proposed scheme using various well-known tools including RoR model, BAN logic and AVISPA simulation. Results of various performance comparisons indicate that our scheme provides most functions and bears rational costs, compared with its related works.
Social Distance Alert System to Control Virus Spread using UWB RTLS in Corporate Environments

Recent pandemic situation of covid-19 which is caused due to the outbreak of highly dangerous and easily spreadable corona virus is an increasing concern throughout the entire world. The purpose of this study is to develop an alert system which alerts employees to maintain social distance in corporate working environments. A real-time location systems (RTLS) based on ultra wide band (UWB) wireless technology gives most accurate locations of approximately 10cm using methods like trilateration and TDOA (Time Difference of Arrival). Co-ordinates of the location can be obtained by installing RTLS in predefined area which are used to calculate the distance between Mobile UWB Devices (MUD’s). An alert triggered by a system to maintain distance if distance between the employees is less than the prescribed social distance can keep the work premises safe and control the spread of corona virus. This study can be a great solution to control spread of virus in corporate working environments which are mostly of confined in size and indoor in nature.
Making Buildings Smarter and Energy-Efficient—Using the Internet of Things Platform

Smart grid, as a concept tries to revolutionize the conventional grid structure by incorporating Information Technology components and making the communication bidirectional. However, there is a greater thrust to make the distribution and utility side of the grid smarter owing to a greater number of end-users and increase in energy consumption. It needs to be prioritized as the global energy consumption levels are increasing by the day. The United Nations Sustainable Development Goals (Goal 7) to ensure energy security and sustainable energy development also need to be met. This article discusses the salient points and characteristics of energy-efficient buildings, which have been occupying significant research space over the last decade owing to advances in Information Technology and Smart grid technology. This article would also help to broaden the research scope by amalgamating several domains such as energy, construction technology, and information technology into one.
Functional MRI of neuro-electro-magnetic oscillations: Statistical processing in the presence of system imperfections

Direct detection of magnetic fields elicited by neuronal activity using Magnetic Resonance Imaging (MRI) has been a long standing research goal, due to its potential to overcome limitations that are inherent to BOLD fMRI. The MRI signal can be sensitized to oscillating magnetic fields using spin-lock preparations. However, the susceptibility of spin-lock sequences to system imperfections has so far hindered their translational potential for in vivo experiments. Moreover, the sensitivity of the neuro-current MRI signal to the phase of neuro-electric oscillations generates high variance time courses that are not suited for analysis with traditional fMRI data processing techniques. In this work we study the impact of various MRI system imperfections on neuro-current MRI in simulations. Furthermore we propose Statistical Variance Mapping (SVarM) as a new data processing technique for generating activity maps from neuro-current MRI signal variance. Bloch simulations demonstrated substantial variations of signal intensity for a 400 Hz range of off-resonances and a 360° range of neuro-current oscillating phases. SVarM was tested on synthetic neuro-current data simulated with various degrees of system imperfections. The proposed technique was compared to the previously developed NEMO processing, which is based on mean analysis of time courses. Simulation results show improved resilience against Bo inhomogeneities with SVarM compared with NEMO processing (Dice coefficient of activation maps: 64.07% SVarM, 57.76% NEMO, $\mathrm{p} < 0.02$). Comparable or slightly improved robustness against $\mathrm{B}_{1}^{+}$ inhomogeneities was observed as well as higher sensitivity in the absence of $\mathrm{B}_{1}^{+}$ inhomogeneities (Dice score of activation maps: 58.34% SVarM, 49.70% NEMO, $\mathrm{p} < 0.01$). Finally, SVarM achieved better specificity for low SNR, resulting in activation maps with fewer false positive voxels (FP rate: 0.53% SVarM, 19.28% NEMO, $\mathrm{p} < 0.01$). These results underscore the importance of dedicated data processing methods and robust pulse sequences to facilitate the widespread use of direct neuro-current MRI in the presence of system imperfections.
Scrutinizing of Fake News using Machine Learning Techniques

Fake news usually comprises of false or misleading information that has the aim of damaging the personality of people. Furthermore, Fake news contains the information that has not been properly researched and proved information, wherein it spreads false alarm via different social media applications. In order to overcome this significant bottleneck, a computational model is proposed here with a likelihood machine learning technique that detects the fake news. TF-IDF vectorizer on the proposed dataset uses the classification algorithm like SGD (Stochastic Gradient Descent) Classifier for achieving a better efficiency etc. The TFIDF (Term Frequency Inverse Document Frequency) vectorizer gives the classification accuracy of the proposed model and converts a raw document into a confusion matrix of TF-IDF vectorizer. After all, the accuracy score helps to know the implementation of the proposed model. Also, the proposed model helps to define the correctness of results for achieving a maximum accuracy.
Printing of Low Cost Sensors by Additive Manufacturing

Additive manufacturing (AM) techniques has vast future in electronics, soft-robotics, 3D/4D printing, and bio-applications. Fabricating high resolution printed structures by avoiding the extreme processing conditions required advanced AM techniques. Printed sensors are surpassing due to their cost efficiency, ease of fabrication, flexibility, and environmental friendly operation. Advancement in additive manufacturing based technology, the resolution and performance efficiency is increasing rapidly. In this paper manufacturing techniques like Electrohydrodynamics (EHD) and Direct Ink Writing are highlighted along with the fabricated sensors. Printing of strain gauge, flex, temperature and humidity sensors were done, with minimum printed width using variety of conductive inks on flexible substrates. Electrical characterization and morphology were carried out to check the sensors response and roughness of the film. These printed sensors has applications in monitoring food, agriculture, medical and industrial environments. The performances of the fabricated sensors can further be enhanced considering industrial environmental monitoring and other related applications.
Partial Shading Performance Evaluation of Bifacial PV Array Configurations

Effective power utilization of potential solar power is limited by intermittent environmental conditions. Such conditions (i.e., dust) lead to partial shading effects which reduce the amount of solar irradiance incident on the PV modules thereby, reducing the power generation. This paper presents simulation analysis of PV array reconfiguration as a mitigation technique for partial shading. It is found that the hybrid Honey-Comb-Total-Cross-Tied PV array configuration significantly mitigates partial shading losses under all shading conditions. The latter makes great application for bifacial solar PV topologies.
A comprehensive optical and electrical properties analysis of Mg doped ZnO nanocrystalline thin-films for optoelectronic applications

In this research, the influence of doping variations of Mg-doped zinc oxide (ZnO) and the impact upon the optical and electrical properties were analyzed. The sol-gel spin coating method was employed to deposit Mg-doped ZnO (MZO) thin-films. The thin-films have been deposited on glass substrates, maintaining a uniform thickness of 200 nm (with 2% fabrication tolerance). Ultraviolet-visible (UV-Vis) Spectrophotometer has been utilized to determine different optical and ECOPIA Hall effect measurement system has been used to determine electrical parameters. With the increment of doping concentration, nonlinear variation of skin depth has been observed. The maximum figure of merit observed in the current research is 562 (10−6 Ω−1) for doping concentration of 4 at. %, sheet resistance of 1725 (Ω/square) and transmittance (99%). For doping concentration of 8 at. %, high transmittance (99%) and small sheet resistance (1725) (Ω/square) has been observed for 4 at. % MZO. The above mentioned transmittance and small sheet resistance is highly desirable for fabrication as dye-sensitized solar cells. Moreover, their high absorption indices ( 1-1.4 cm for 6 at.% MZO) might be suitable for filters and modulators, switches etc.
Small Target Detection Based on Squared Cross Entropy and Dense Feature Pyramid Networks

At present, the research hotspot of small target detection mainly focuses on the methods based on deep learning, and such algorithms still have some problems that need to be solved, such as the foreground-background class imbalance and the poor performance of multi-scale detection. For the foreground-background class imbalance, the Squared Cross Entropy (SCE) loss function is proposed here to help solve the problem. Meanwhile, as Feature Pyramid Networks (FPN) is a powerful means to deal with multi-scale detection problems, a new Dense FPN structure is designed based on FPN. The Dense FPN removes the up-sampling process in FPN, and after each feature extraction layer, a continuous convolutional layer with a decreasing number of layers is added. According to the experimental results, Dense FPN outperform the original FPN on various evaluation indicators like <inline-formula> <tex-math notation="LaTeX">$AP_{S}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$AP_{M}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$AP_{L}$ </tex-math></inline-formula>, showing the excellent performance of the Dense FPN in dealing with multi-scale detection problems.
Photocurrents Recovery in GaN UV Sensors Using Microheaters at Low Temperatures

At various low-temperature conditions, it is difficult to obtain an accurate sensing response due to temperature-dependent material properties such as bandgap and resistivity of semiconductors. In this study, a gallium nitride (GaN)-based ultraviolet (UV) photodetector with a microheater was demonstrated to compensate for the low-temperature effects. A parallel-type platinum microheater array was fabricated to supply thermal energy by Joule heating. In addition, a silicon oxide layer was deposited between the heater and the GaN surface, allowing an independent voltage supply. Therefore, the change in the signal level was successfully recovered to the initial state in the temperature range of $-27.4-11.5 ^{\circ }\text{C}$ within ~0.64% error without electrical interference. This study supports an active, accurate, and reliable method for the stable measurement of UV signals in various low-temperature environments such as freezer warehouses, Antarctic research stations, and in space.
A Novel Residual 3-D Convolutional Network for Alzheimer's disease diagnosis based on raw MRI scans

One of the most widely used deep learning architectures for image classification, Convolutional Neural Networks (CNNs) are used in a diverse range of research areas. Over the past five years, CNNs have been extended for use in disease classification and diagnosis based on body imaging data. In this paper, we propose one such CNN model to diagnose Alzheimer's Disease using raw, volumetric Magnetic Resonance Imaging (MRI) scans. The MRI dataset used contains 857 scans (302 AD and 555 Normal Control) in total and was procured from the ADNI study. The performance of the proposed residual CNN was compared with 3-D ResNet-18 with and without an attention mechanism. Finally, 3-D Gradient-weighted Class Activation Mapping was used to evaluate how effective the models were in recognizing brain regions pertaining to AD. The best performing model obtained an accuracy of 91%.
A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.