Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of screen-printing techniques for embedding ECG sensors in medical devices

Heart rate monitoring is the most important indicator to evaluate the clinical status of a newborn during birth. Approximately 90% of newborn infants make the transition from the intrauterine to extra uterine environment without major complications; however, the remaining 10% of newborn infants require assistance during this transition. Heart rate monitoring is required for guiding further interventions in the event of complications such as the need for resuscitation. In this work we evaluate the suitability of embedding electrometer-based-amplifier sensors employing novel screen-printing techniques into medical devices. We compare our results with traditional copper based wired electrodes. Our implementation was able to acquire electrocardiogram with enough signal to noise ratio, suitable for heart rate detection with a 1% loss of heart rate accuracy, compared with the copper-based electrodes. Our device has the potential to be embedded in devices for assisting births though heart rate monitoring.
Worst Case Latency Optimization-based Joint Computation Offloading and Scheduling for Interdependent Subtasks

In this paper, we consider a mobile edge computing (MEC) system consisting of a number of MEC servers and mobile devices (MDs) where each MD generates a series of subtasks characterized by their dependency relationships. Stressing the worst case performance optimization, we examine the maximal completion time of all the subtasks, and formulate the joint computation offloading and scheduling problem as a maximal completion time minimization problem. As the original optimization problem cannot be solved conveniently, we propose a heuristic algorithm which successively designs the computation offloading and scheduling strategy for various sets of subtasks based on the dependency relationships of the subtasks. In particular, for the subtasks of which the preceding subtasks have been completed, a priority- based subtask computing algorithm is further proposed. Numerical results demonstrate the effectiveness of the proposed scheme.
Fuzzy Logic MPPT for Grid Tie Solar Inverter

To extract maximum power from the solar PV system, various maximum power tracking techniques have been developed and tested. This paper presents a smart method for maximum power tracking based on fuzzy logic controller. The proposed system is simulated in MATLAB simulink. Single phase dual stage grid tied solar inverter topology is considered for simulation. Fuzzy logic based Maximum Power Point Tracking) MPPT algorithm is compared with the conventional MPPT (Incremental conductance plus Integral regular) algorithm. Comparative analysis using simulation results are presented. Simulation results show that the fuzzy logic based MPPT algorithm performs better compared to the conventional MPPT method.
Digital Channel Feature Map Assisted Airborne MIMO Communications

Due to the mobility of air platform (AP), the channel estimation (CE) and beamforming matrix update in airborne multiple-input multiple-output (MIMO) communication systems will result in significant consumption in pilot, power and computation resources, especially when the numbers of AP’s antennas and users are large. In this work, we present a digital channel feature (DCF) map assisted airborne MIMO transmission framework. DCF map is a database on local or online servers, which contains the site-specific channel features labeled by positions of AP and users. By exploiting the DCF map, we propose a CE-free beamforming scheme under max-min fairness for efficient downlink air-to-ground transmission. The resulting beamforming problem is solved efficiently by exploiting the inverse problem for power minimization. Simulation results show that the CE-free scheme can retain most of the average achieve rate achieved by perfect CSI based scheme. Since the proposed scheme relies only on the offline DCF, the results of this work can be further exploited in other high-level optimizations, e.g., AP deployment and network planning.
Predicting Internal Energy Consumption of a Wind Turbine Using Semi-Supervised Deep Learning

Most previous works on wind power generation focused on the impact of the external environment on the efficiency of energy generation, but ignored the energy consumption of internal parts of the wind turbine. Reducing internal energy consumption can not only improve the generation efficiency, but also reduce the maintenance cost of wind turbines. Therefore, this study uses deep learning to predict the energy consumption inside the wind turbine by installing dozens of sensors inside it, and finds the parts that have greater impact on energy consumption to reduce energy consumption and improve generating efficiency. Since most data on wind turbines is collected by humans currently, it is inevitable that the data will have missing or wrong. Due to the large number of parts inside the wind turbine, the collected data belongs to multi-dimension data. In order to use these data effectively, this study proposes a semi-supervised deep learning method which can correct the data to solve this problem. After all the data are corrected and the model is completely trained, this study uses the MCC method to judge the predicting results of the model. The results show that when the label data accounts for 15-20% of the total data the trained model has the best predictive ability. Therefore, this study suggests that when establishing a prediction model of internal energy consumption of wind turbine in the future, the label data should account for 15-20% of the total data. In this way, not only can train a model with considerable accuracy, but also the most economical ways to determine the amount of revision data.
A Generalized Model for Stacked Boost Single-Switch Converters

In this paper a generalisation for an $n$ stages Boost topology is proposed. The starting point is the two stages stacked boost converter. The dc conversion ratio and a set of properties are theoretically derived. The results are analysed using MATLAB™ and the simulations performed in CASPOC have validated the theoretical considerations. The converter is useful in applications with high step-up dc gain, where it can successfully replace a cascade of several classical Boost converters.
Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence

Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effective way to obtain energy-efficiency with small accuracy drops is to sequentially execute a set of increasingly complex models, early-stopping the procedure for “easy” inputs that can be confidently classified by the smallest models. As a stopping criterion, current methods employ a single threshold on the output probabilities produced by each model. In this work, we show that such a criterion is sub-optimal for datasets that include classes of different complexity, and we demonstrate a more general approach based on per-classes thresholds. With experiments on a low-power end-node, we show that our method can significantly reduce the energy consumption compared to the single-threshold approach.
Children's Drawing Psychological Analysis using Shallow Convolutional Neural Network

With the continuous development of civilization, people are paying more attention to mental health issues. Artificial intelligence (AI) technology assessed children's mental health is one of the hot topics in the related field. Drawing test is one of the mainstream psychological assessment methods, and the difficult problem is that how to use computer-assisted psychological analysis to achieve reliable results. Two challenges retain in the literature. First, drawings are different from natural images, which lack information factors such as color and textures, making it difficult for AI algorithms for psychological analysis. Second, the drawings reflect multiple mental health problems. The AI model has to discover hidden factors through a small amount of information displayed on the drawing. This paper proposes a shallow convolutional neural network for feature extraction of children's drawings, combining with the sigmoid function in the fully connected layer for multi-label classification. The classification results shown in the experimental results section justify that the proposed shallow CNN model can assist psychologists in psychological analysis from children's drawings.
Design and Development of IOT enabled IV infusion rate monitoring and control device for precision care and portability

This paper proposes a drip monitoring and control device that fits on the existing Intravenous (IV) infusion setup. Normally, doctors and nurses use their experience to estimate the time required for a drip bottle to be empty and for setting drip rate. This makes the IV infusion method to be vulnerable to human error and there are also other risks like back flow of the blood and many other if not attended when empty. This paper proposes an IoT (Internet of Things) based monitoring and control platform for IV infusion setup which enables doctors and nurses to monitor as well as control the IV infusion setup wirelessly while keeping the cost low and making the device highly reliable. The device can be divided into 3 units. The first unit houses an IR sensor for optical based drop detection, second unit houses a microcontroller with a 2.4Ghz Wi-Fi module and the other unit is the actuator to regulate drip rate. In addition, the device is tested along with different parameters like accuracy, stability, reliability, and battery capacity. Finally, the data from the IoT device is reliably transmitted to an online server using HTTP POST request (Hyper Text Transfer Protocol). Then this data is stored in a SQL (Structured Query Language) database and visualized on a webpage as well as a mobile app for easy readability. Also, the attendant could remotely control the device to set drip rate, shut or start and monitor the IV therapy.
Digital Transformation VIA 5G: Deployment Plans

Digital transformation is indispensable for achieving sustainable development, and deployment of fifth-generation wireless networks (5G) is instrumental for making digital transformation a reality. There are a number of challenges and technical choices that influence 5G deployment with impacts on efficiency, interoperability, and associated costs. These are of significant importance as there is no roadmap that would be universally applicable to all network operators for deploying 5G and beyond. In this paper, we contextualize various technical options for 5G deployment and discuss their impacts. Specifically, we focus on the timely utilization of various standards pertaining to radio access network (RAN), transport network (TN), and core network (CN) with a view to enhancing interoperability with existing networks/facilities. We also discuss how 5G standards are evolving.
OTA Performance Space Modeling for System-Level Optimization of SC-circuits

This paper is about high-level modeling of analog circuits based on its complete feasible performance space. The behavioral model is a multi-dimensional description of all enclosing surfaces of this space, approximated by a set of boundary points. An exploration method, applying the Normal Boundary Intersection with a novel constraint rotation method, is shown to calculate these boundary points. The solutions are found by transistor level spice simulations, which leads to a highly accurate approximated model. A fully-differential transconductance amplifier with folded cascode and class-AB output stage is taken as example circuit, whose performance space model is calculated by the deterministic exploration scheme. The resulting model is used to perform an efficient system level optimization towards accuracy of a switched capacitor circuit.
Overlapping Communities based on Relaxed Cliques

Detection of overlapping communities is one of the most important tasks in network science that allows to simulate various real-world objects like social networks. This paper describes an algorithm for finding overlapping communities using relaxed cliques. The use of relaxed cliques makes it possible to find communities (including overlapping ones) with a denser internal structure due to the requirement for more tight connectivity of nodes inside the relaxed clique. The developed algorithm has shown its efficiency in the analysis of communities in synthetic networks built with the LFR (Lancichinetti–Fortunato–Radicchi) graph generator.
Miniaturization of a Planar Cable-Driven 3D Printer using Optimization

In this paper, the wrench-feasible printable workspace is analyzed for a planar redundant cable-driven 3D printer. This workspace has a characteristic feature of maintaining cable tension within the prescribed positive tension bounds while avoiding interference of lower cables with the object being printed for all the poses in the workspace. Furthermore, the effect of various geometric parameters on the workspace of the robot is studied. Finally, the miniaturization of a cable-driven 3D printer is achieved for a specified workspace by optimizing geometric parameters of the robot using the genetic algorithm.
Limitation of SPADs quantum efficiency due to the dopants concentration gradient

Single-photon avalanche diodes are highly sensitive devices capable of registering the arrival of an individual photon. They are biased beyond breakdown voltage, and thus the electron-hole pairs generated by any incident photon is accelerated by the strong electric field triggering an avalanche current. This paper examines the role of the dopants concentration gradient in the gathering of photons in these devices, and how it can be engineered to maximize quantum efficiency and explain his role to minimize undesirable effects like crosstalk.
Histogram of Oriented Gradients Feature Extraction Without Normalization

In this paper, the effects of normalization in the histogram of oriented gradients (HOG) are studied and a HOG feature extraction pipeline without normalization is proposed. In the proposed pipeline, the functionality of normalization is merged into the gradient generation step by replacing the original linear difference based gradients with logarithmic gradients. Due to the discrete property of the pixel values, the logarithmic operation can be easily implemented using a lookup table (LUT) with a depth of 2N, where N is the bit-width of the pixels. Theoretical analysis and experimental results show that the proposed normalization-free HOG feature based logarithmic gradient is close to the original version and can be used in the pedestrian detection algorithms without performance degradation. It is shown in the experiments that by skipping the time-consuming normalization step, the processing speed of HOG feature extraction can be significantly improved.
Design Integrated Staff Welcoming and Administration System Based on Facial Recognition for Smart University

In recent years, facial recognition technology is not only applied for security, healthcare, business but it is also exploited creatively in education and for smart university development. In this study, an Automated Employee Attendance Management System has been designed and implemented in a university’s building for welcoming staff and supporting university administration. The system starts with a facial detection module which is based on the pre-trained MultiTask Cascaded Convolutional Network model. Then the feature vectors are created by using the ResNet34 network which results in the 128-dimension embedding vector. Face recognition is carried out by using various techniques such as K-Nearest Neighbors, Support Vector Machine. Besides the welcoming front-end module, a web-based application for querying information to manage people entering the building is also built as a back-end module. The proposed face recognition models have been trained and tested on a collected face database and a self-built face database of employees who work at the university building. The evaluation results show high recognition rates in terms of Precision, Recall, Accuracy, Fl-score and reasonable processing time. The proposed system has been piloted at the university for further development and research on face recognition technologies and smart building management.
Road Detection Based on Region Grid Analysis Using Structural Similarity

Road detection is one of common used in autonomous vehicle robot. The fast road detection process is needed by autonomous vehicle robot for steering the robot following the road. So, in this paper proposes a method to detect roads using similarity analysis. An image is obtained by capturing from the camera sensor. This image is graded and then the region of image in the grid areas is analyzed by structural similarity (SSIM) index analysis. The structural similarity method compares the two images between region grid image area and references image. A reference image is a sample image that represents a road surface. In this paper, we accept the region grid image areas as a road surface, if it has SSIM index value greater than 0.995. This value is obtained from the results of repeated experiments. The experiment shows that the proposed method has capable detect the road surface in the region of the grid image areas, as shown in the image in the experimental result.
Automatic Text Summarization using Soft-Cosine Similarity and Centrality Measures

The exponentially evolving size of information today has made it difficult to find relevant information quickly and efficiently. A good extractive text summarizer not only provides the most significant information from the document but also helps the user to decide the relevance of the information. The proposed method is a knowledge-based, generic, extractive text summarization technique. Our approach is based on the centrality of a sentences in the graphical representation of the documents. The graph is constructed using the pair-wise softcosine similarity measures between the sentences derived using the S semantic relations presented in WordNet lexical database. Eigenvector centrality measure outperforms the weighted degree, betweenness and closeness centrality measures. The resultant summary is compared against the gold-standard summaries of BBC news articles from year 2004 to 2005 and DUC 2007 datasets. The ROUGE-I, -2 and -Lmetrices are used to evaluate the results and found that our approach performs better than LexRank, TextRank, Luhn and LSA baseline text summarizers.
LBIST-PUF: An LBIST Scheme Towards Efficient Challenge-Response Pairs Collection and Machine-Learning Attack Tolerance Improvement

Device identification using challenge-response pairs (CRPs), in which the response is obtained from a physically unclonable function (PUF), is a promising countermeasure for the counterfeit of integrated circuits (ICs). To achieve secure device identification, a large number of CRPs are collected by the manufacturers, thereby increasing the measurement costs. This paper proposes a novel scheme, which employs a logic built-in self-test (LBIST) circuit, to efficiently collect the CRPs during production tests. As a result, no additional measurement is required for the CRP collection. In addition, the proposed technique can counter machine-learning (ML) attacks because of the complicated relationship between challenge and response through the LBIST circuit. Through the proof-of-concept implementation, in which a field-programmable gate array (FPGA) is used, we demonstrate the PUF performance can be evaluated by a test pattern generated by the LBIST circuit. Furthermore, the vulnerability due to ML attacks using a support vector machine (SVM) and random forest (RF) is lowered by more than two times compared to the naive usage of PUF.
Preventive Readmission In Hospitals Using Machine Learning

This paper discusses the application of Machine Learning techniques to the prevention of readmissions in hospitals. Readmission in hospitals is generally defined as admitting a patient again within, generally, 30 days of initial admission. Readmission in hospitals in a specific time interval has to be avoided both for healthcare reasons and quality standards, and this index is being used as an important measure of examining quality of care. Several statistical figures and analyses have also been explored in literature. This paper also presents why the use of the exponentially rising field of Machine Learning is better to be applied in place of the existing models for acknowledging this problem, including assessments and analyses. This paper concludes the importance of using Machine Learning model and the risks involved in using hypothesis driven models.
REGIONAL ECONOMIC DEVELOPMENT DISPARITIES IN THE BALTIC STATES

The problem of inequalities in the economic development of regions is an important topic for economic researchers. Too large inequality influence economic situation and business environment. The aim of the work is to assess the regional disparities in the Baltic States. Methods used: standard deviation; structural divergence index. The uniqueness of the calculation is that it does not calculate the difference between the region and other regions, but it evaluates the aggregate internal difference of a group of regions. The results show that: (1) regional disparities are increasing; (2) there is a clear clustering of regions.
Image based Bird Species Identification

: Nowadays, birdwatching is a common hobby but to identify their species it requires the assistance of bird books. To provide birdwatchers a handy tool to admire the beauty of birds, we developed a deep learning platform to assist users in recognizing species of birds using a software based on the concept of image recognition. This software would recognize the input image by comparing the model with a trained model and then predict the bird species. The details would be given out as an output. Also, it will help us to build the dataset if any image captured or uploaded by the user is unavailable in the dataset then the user can add that image to the dataset.
Curcumin loaded organically modified silica (ORMOSIL) nanoparticle; a novel agent for cancer therapy

Curcumin is a polyphenol compound extracted from the rhizome of turmeric (Curcuma longa) and has a well established anticancer property. However poor aqueous solubility is a major hindrance for its bioavailability and therapeutic use. Nanoparticle based system can be developed to overcome this problem. Present study has been undertaken with curcumin loaded organically modified silica (ORMOSIL) nanoparticle (NP) to assess its anticancer property. In comparison to void ORMOSIL NP the curcumin loaded NP has shown marked cytotoxicity on hepatocellular carcinoma (HepG2) cells as well as reduction of Ehrlich Ascites Tumour (EAT) cell induced experimental solid tumour following intratumoral injection. There is increased apoptosis of cancer cells due to curcumin induced enhanced intracellular generation of reactive oxygen species (ROS). Thus curcumin loaded ORMOSIL NP may have a potential for use as anticancer therapy replacing or substituting more toxic anti–mitotic drugs and radiation.
Compact Ultra Wide-Band and Tri-Band Antenna for Portable Device

A compact ultra-wideband (UWB) and triband patch antenna with the partial ground plane is presented in this paper. Initially, the antenna is designed for UWB applications, operating at the UWB portion of the spectrum ranging from 3.1 GHz to 10.6 GHz, then it is modified to operate at three distinct frequencies of 2.45 GHz, 5 GHz, and 10.2 GHz. The proposed antenna is inspired by a classic rectangular patch antenna in which slots, stubs, and defected ground structure (DGS) were introduced to increase its operational bandwidth. Good results in terms of return loss are found in all resonant frequencies as well as for the single wideband. In addition, the proposed antenna has been compared with related works in the literature, to highlight its potential for future UWB and multiband portable devices.