Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Automated System for Detection of Diabetic Retinopathy

The problem of people with diabetes causes a condition known as Diabetic Retinopathy (DR). It is most common in the elderly age peoples. As diabetes progresses, patients' perceptions may begin to deteriorate and cause DR. People lose their sight because of this disease. To deal with DR, early detection is required. Patients will have to be examined by doctors regularly which is a waste of time and energy. In recent time, number of DR cases are increasing exponentially due the modern stress life style. Therefore, it is necessary to automate the detection and diagnosis process of the DR. In this paper, machine learning (ML) techniques are used to diagnose early DR. These are the Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Bayesian Separation and K-Means Clustering. These methods will be tested and compared with choosing the best method. 3000 images being processed for training and testing. Features are extracted from these fundus images using image processing techniques. After research, the results confirm that SVM is the best way to detect DR early with a higher level of accuracy compared to other classifiers.
Chronic kidney disease prediction using machine learning techniques

Early diagnosis and characterization are the important components in determining the treatment of chronic kidney disease (CKD). CKD is an ailment which tends to damage the kidney and affect their effective functioning of excreting waste and balancing body fluids. Some of the complications included are hypertension, anemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications. Early and error-free detection of CKD can be helpful in averting further deterioration of patient's health. These chronic diseases are prognosticated using various types of data mining classification approaches and machine learning (ML) algorithms. This Prediction is performed using Random Forest (RF) Classifier, Logistic Regression (LR) and K-Nearest Neighbor (K-NN) algorithm and Support Vector Machine (SVM). The data used is collected from the UCI Repository with 400 data sets with 25 attributes. This data has been fed into Classification algorithms. The experimental results show that K-NN, LR, SVM hands out an accuracy of 94%, 98% and 93.75% respectively. The RF classifier gives out a maximum accuracy of 100%
An Iterative-Optimization-Based Calibration Framework for VIO With Limited Prior Conditions

Most of the visual inertial positioning accuracy is constrained by the calibration accuracy of the sensor, however, there are few navigation systems that take the time offset and extrinsic parameters into account at the same time. In this paper, an iterative-optimization-based calibration framework for VIO (visual-inertial odometry) with limited prior conditions is proposed. In particular, a three-stage calibration method is presented, which includes the time offset estimation stage, the extrinsic and intrinsic parameters calibration stage, and global temporal and IMU parameter optimization stage. In the first stage, the time offset calibration model is proposed, which estimates the time delay by iteratively optimizing the orientation residual equation. In the second stage, based on the assumption of constant angle velocity and linear velocity between two continuous keyframes, an iterative-based coarse-to-fine calibration strategy is presented to calibrate the initial extrinsic and IMU intrinsic parameters. In the third stage, a global optimization-based algorithm is introduced to obtain a more accurate time offset value and IMU intrinsic parameters. What’s more worth mentioning is that the proposed method does not require any prior parameters, which greatly improves the universality of the navigation system. To evaluate the effectiveness and accuracy of the proposed algorithm, the method is tested on both simulation and public dataset, and also compared with classical systems. Abundant experimental results illustrate that the proposed method can effectively estimate the time offset, the IMU intrinsic and extrinsic parameters.
Dual-Band Circularly Polarized Compact Planar Dual Slot Monopole Antenna

A new dual-band circularly polarized (CP) monopole antenna with half elliptical slot and rectangular slot is given. This antenna uses coplanar-waveguide (CPW) - feeding and produces broad impedance bandwidth (IBW) and broad dual-band axial ratio bandwidth (ARBW). The antenna structure is simple and compact with dimensions of 22×21×1.6 mm3. The antenna is designed in two stages and parametric analysis is used to analyze the antenna performance. Proper selection of the slots, slot dimensions, and slot positions, broad circularly polarized bands are achieved. The impedance band is from 4.75 GHz to 10.62 GHz with a bandwidth of 5.87 GHz. The dual-band circular polarization is ranged from 5.46 GHz to 6.24 GHz and 8.82 GHz to 10.48 GHz. This antenna can be used in both the C-band and the X-band. The designed antenna has been developed and tested, and the results match those predicted by the simulation.
Optimal decision in MC supply chain with overconfident retailer based on the newsvendor model

In this paper, we analyze the optimal order-quantity decisions in a supply chain with mass customization (MC) manufacturer and overconfident retailers. First, we consider a newsvendor model in which an unbiased retailer sells mass customized products. The retailer needs to make order quantity decisions before the selling season. Meanwhile, the supplier is a mass customization manufacturer and implements modular production. The supply process is uncertain, as the real quantity the retailer received is the order quantity multiplied by a random yield rate. Second, two overconfident models are considered and theorems are proposed. In the first model, the behavioral bias of overconfidence only affects the retailer’s judgment of variance of market demand. In the second model, the behavior bias of overconfidence affects not only the retailer’s cognition of the variance of market demand, but also his cognition of the expectation of market demand. In addition, the relationship between the optimal decisions and the modularity level is obtained. Finally, we provide managerial insights for the decision makers of the retailers and the manufacturers on order quantity and modularity level, respectively. Mathematics Subject Classification. 90B05, 97M40. Received May 14, 2020. Accepted April 7, 2021.
COEDU-IN Project: an inclusive co-educational project for teaching computational thinking and digital skills at early ages

Learning to program is the new literacy of the 21st century. Computational thinking, closely related to programming, requires thinking and solving problems with different levels of abstraction and is independent of hardware devices. The early childhood education stage provides teachers with the opportunity to lay the foundations for a comprehensive quality education using innovative tools and technologies. Educational robotics in early childhood education becomes a tool that facilitates the acquisition of knowledge to children, playfully, based on the principles of interactivity, social interrelationships, collaborative work, creativity, constructivist and constructionist learning, and a student-centered didactic approach, allowing in turn that student can acquire digital competencies and develop logical and computational thinking in an underlying way. This project explores the current state of teaching and learning computational thinking and programming in early childhood education in an inclusive manner. Moreover, the lack of diversity and inequality is particularly latent in science, Technology, Engineering, and Mathematics (STEM) fields. Therefore, this work considers this problem and presents an inclusive coeducation approach to this new literacy, eliminating gender stereotypes and extending them to people with Down syndrome and hospitalized minors.
A Hierarchical Autonomous Driving Framework Combining Reinforcement Learning and Imitation Learning

Autonomous driving technology aims to make driving decisions based on information about the vehicle’s environment. Navigation-based autonomous driving in urban scenarios has more complex scenarios than in relatively simple scenarios such as highways and parking lots, and is a task that still needs to be explored over time. Imitation learning models based on supervised learning methods are limited by the amount of expert data collected. Models based on reinforcement learning methods are able to interact with the environment, but are data inefficient and require a lot of exploration to learn effective policy. We propose a method that combines imitation learning with reinforcement learning enabling agent to achieve a higher success rate in urban autonomous driving navigation scenarios. To solve the problem of inefficient reinforcement learning data, our method decomposes the action space into low-level action space and high-level actin space, where low-level action space is multiple pre-trained imitation learning action space is a combination of several pre-trained imitation learning action spaces based on different control signals (i.e., follow, straight, turn right, turn left). High-level action space includes different control signals, the agent executes a specific imitation learning policy by selecting control signals from the high-level action space through a DQN-based reinforcement learning approach. Moreover, we propose a new reward for high level action selection. Experiments on the CARLA driving benchmark demonstrate that our approach outperforms both imitation learning methods and reinforcement learning methods on a variety of navigation-based driving tasks.
Frequency-Modulation Controlled Load-Independent Class-E Inverter

This paper proposes the frequency-modulation (FM) controlled load-independent class-E inverter, which has robustness against simultaneous variations of the load and the components in the output resonant filter. The main idea is to apply the FM control of the output-voltage regulation to the load-independent class-E inverter. By applying that, the proposed inverter can keep the phase shift between the driving signal and the output voltage constant without using any time information. As a result, the proposed inverter can maintain the constant output voltage and high power-conversion efficiency operation at high frequencies despite the load variations and the component tolerances in the output resonant filter. We give analytical expressions of the proposed inverter and quantitative evaluations. Additionally, an experimental prototype of the proposed inverter was implemented. The theoretical and experimental results showed the validity and effectiveness of the proposed inverter. The implemented inverter achieved 95% efficiency with the 5.7 W output and 1-MHz operating frequency at the rated operation.
Enriching Didactic Similarity Measures of Concept Maps by a Deep Learning Based Approach

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher’s assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in Learning Analytics approaches. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map.
Video Analytics-based Ingredient Detection for Smart Chef using YOLOv3 and ImageAI

With the boom in internet usage every aspect of our daily lives has experienced a drastic change over a period of time. Searching the internet for food recipe videos is one of the most common searches around the world. Search results from internet are shown based on the keywords the video is tagged with and often it is observed that the videos are not much relevant and does not grab user's immediate attention. Our work is concerned with analyzing the objects and contents of videos to find the actual ingredients involved in them. The objective is to provide a list of the objects involved in the video. In this work, we have used YOLOv3 and ImageAI to detect objects from the video files. We consider using the COCO dataset for the analysis. So, the project is concerned about identifying relevant recipe videos conveniently using video analysis.
Design and Simulation of Cognitive Radio Networks with Selective Reporting SU Cluster

Primary users in cognitive radio network are assigned with specific bands. In any traditional Network SU should be able to tune into the channel, with one that has higher probability of being free. Presence of any free channel is reported to the Master node or Central node, which is also one of the secondary users. Hence, reducing the scanning time, and improving the quality of Service. Here, the Reporting SU selection can be executed in two methods random-selection reporting and best-selective reporting. An optimum Bayesian detector structure for spectrum sensing based is implemented instead of Energy detector. Finally, numerical and simulation are results are obtained that reveal the proposed strategies can remarkably reduce the sensing overhead without degrading the detection performance compared to traditional case.
A deep convolutional generative adversarial network-based fake fingerprint generation method

Fingerprints recognition (FPR) is considered as a secure and efficient way of personal identification verification. However, research of fake fingerprints generation by generative adversarial networks (GAN) disclosed a potential vulnerability of partial FPR systems. In this paper, we proposed two networks based on deep convolutional generative adversarial networks (DCGAN) to improve the performance of DCGAN by enhancing the performance of the discriminator. More specifically, the structure of discriminators in our network is modified by two approximate typical convolutional neural networks, AlexNet and VGG11. (We abbreviated them to Alex-GAN and VGG-GAN respectively) We evaluated the output of generators in our models by cosine similarity index every 10 epochs during training, which monitored the performance of the generator and was convenient for comparing two models. The results of cosine similarity and texture analysis show that VGG-GAN performs better than Alex-GAN. Although our models are unstable and unbalanced in discriminator and generator, we acquired notable results unexpectedly which demonstrate the competitiveness of our model.
Botnet Detection Based on Flow Summary and Graph Sampling with Machine Learning

With the development of botnets, detecting and preventing botnet attacks has become an important task of network security research. Existing works rarely consider timing patterns in botnets, and thus are not effective in realistic botnet detection, nor can they detect unknown botnets. To deal with these problems, this paper proposes a flow summary and graph sampling based botnet detection method using machine learning algorithms. Firstly, the network flow data is aggregated according to the source host IPs, and the flow summary records are generated within a duration of time window. Meanwhile, we use graph sampling technology to obtain a subset of entire graph, obtaining 4 graph features which are added to the flow summary records. Afterwards, decision tree, random forest and XGBoost machine learning classification models are built to validate the performance of our method. The experimental results on the Bot- IoT and CTU-13 datasets show that the method we proposed can effectively detect botnet traffic and unknown botnets.
RF-RES: Respiration Monitoring With COTS RFID Tags by Dopplershift

In health assessment and disease prevention, vital signs such as breathing rate, heartbeat rate, blood pressure and posturography play a critical role. Not only the elderly, but also young people need to pay attention to vital signs monitoring because more and more young people die of sudden diseases, such as sudden death and myocardial infarction. Traditional vital signs monitoring methods are invasive and inconvenient to use, especially for the elderly because these methods usually require users to wear some dedicated sensors. In recent years, some non-invasive vital signs monitoring methods based on wireless sensing have been proposed. Usually, these methods need the user to remain still. When the user walks around, these methods cannot work well. One of the challenges of mobile people breathing monitoring is that the small body chest displacement caused by breathing is disturbed by people’s walking. In this paper, we propose RF-RES, which is an accurate continuous breathing monitoring method based on RFID technology and uses a pair of tags to eliminate the influence of the user’s body movement. We merge the data of the tag pairs to eliminate the influence of the user’s entire body movement and propose an algorithm based on doppler frequency shift to track the user’s displacement during exercise. We conduct practical experiments to evaluate the performance with Impinj R420 and passive tags. The results show that RF-RES achieves accurate respiratory rate monitoring when estimating the respiratory rate of different users, with an average error of 0.54 BPM.
Performance of Bidirectional Isolated DC-DC Converter with PV system and Energy storage Bank

This paper proposes a bidirectional isolated DC-DC converter operating with photovoltaic (PV) systems. Photovoltaic systems have become the largest renewable energy source which is being used presently. Therefore, this paper illustrates analysis and implementation of PV systems with a DC grid using the new bidirectional isolated converter is a crucial study. This converter is used for low-power applications in combination with an energy storage bank. The topology introduced in this paper is double switch forward converter topology with modifications such as clamp diodes on the secondary side of the transformer are replaced by MOSFE T switches to achieve bidirectional power flow. For obtaining galvanic isolation between input and output, a high frequency transformer is used. The switching frequency chosen is 100 kHz. This prototype interfaces a 200 V DC bus and 24 V battery bank in addition to a solar panel with a power rating of 100 W. The prototype is simulated and evaluated under different operating conditions on MATLAB/SIMULINK software
Measurement Range Enhancement of a Scanning Probe by the Real Time Adjustment of the Interferometer Reference Mirror

Scanning probes on the micro/nano-coordinate measuring machines (micro/nano-CMMs) can produce on/off signals and achieve 3D measurement ability within a specific range. The authors have developed several scanning probes based on a 2D angle sensor and a miniature Michelson interferometer that can work within ±<inline-formula> <tex-math notation="LaTeX">$10~\mu \text{m}$ </tex-math></inline-formula>. When the probe’s lateral displacement is greater than <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{m}$ </tex-math></inline-formula>, the interferometer signal is easy to break, limiting the measurement range of these probes. A 2D angle fine-tuning stage using piezoelectric stack actuators is designed to adjust the reference mirror in real-time to solve signal separation between the reference beam and the measuring beam of the miniature Michelson interferometer. The coupling of the 2D angle fine-tuning stage in the x and y directions is analyzed, and the coupling model is established for a relatively precise compensation. Experimental results show that through the proposed 2D angle fine-tuning stage, the measurement range of the probe can be extended to ±<inline-formula> <tex-math notation="LaTeX">$30~\mu \text{m}$ </tex-math></inline-formula>, and the interference signal can still remain stable and uninterrupted.
EEG signal feature reduction and channel selection method in hand gesture recognition BCI system

With the purpose of determining the best feature set of BCI system based on EEG, this paper proposes a feature dimension reduction method on the basis of Pearson’s correlation coefficient. Feature extraction time and classification accuracy are used as evaluation criteria by using SVM classifier. Feature dimension is reduced from two aspects of feature type and number of channels. Time domain, frequency domain, time-frequency domain and spatial domain features are extracted for comparison in the designed hand gesture recognition experiment. Compared with the running time and classification accuracy of PCA and LDA algorithms, it is confirmed that the feature dimension reduction algorithm based on Pearson correlation coefficient can effectively reduce the feature extraction time and improve the classification accuracy, and obtain the most suitable system feature subset.
ARViz: Interactive Visualization of Association Rules for RDF Data Exploration

Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.