Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

A novel, low-cost approach to high-accuracy robot localization for agricultural applications using an image-processing triple-laser-guided (TLG) system coupled with an inertial measurement unit (IMU) is presented in this paper. The TLG system consists of a laser-pointing unit (LPU) at the base station and a laser-target unit (LTU) at the mobile robot. The robot’s XYZ position and heading are determined from the positions and the angles relative to the field of both LPU and LTU. The robot’s roll and pitch are determined by the IMU sensor fusion with complementary filter. The IMU-coupled TLG system is demonstrated on an outdoor, $20 \times 21$ m flat field at various light intensities. The overall lateral and vertical accuracies of the IMU-coupled TLG system are 1.68 cm and 0.59 cm, respectively. The overall heading, roll, and pitch accuracies of the IMU-coupled TLG system are 0.90°, 0.78°, and 0.76°, respectively. The lateral and heading accuracies of the IMU-coupled TLG system are found to be comparable to commercially available GNSS-INS systems from NovAtel and Trimble, while the total cost of the IMU-coupled TLG system is only a fraction of the total cost of the commercially available localization systems.
Converged Medium Access Control and Dynamic Bandwidth Allocation for Radio-Over-Fiber Networks

For supporting the upcoming applications, wireless mobile users require high bandwidth. To serve these requirements, a wireless network that has an optical backbone with a colossal bandwidth capacity is required. The radio-over-fiber (RoF) network is a promising solution that can provide high bandwidth. To accomplish this goal, we propose a converged medium access control (MAC) protocol for the RoF network, which uses a single-gate or multi-gate polling algorithm for time and wavelength allocation to the users. We propose an analytical delay model for single and multiple wavelength architectures, considering the user identification and data transfer delay for single-gate and multi-gate polling algorithms. We verify it with the results obtained from simulations. Finally, we present the mean delay and packet loss rate of the proposed algorithm by varying the user load and traffic burstiness and prove that the proposed algorithm gives better delay performance and a lower packet loss than the state-of-the-art algorithms.
Recruitment System with Placement Prediction

The availability of information and the facility for the user to take action on the information collected have been revolutionized by the use of the Internet and the World Wide Web. The placement process can be managed using the internet which arises a need to develop a web-based placement management system specifically by the recruiters and the software engineers that can be used as a Recruitment system (Online TnP portal). This system can be used as an application for both candidates and recruiters. Advanced features for recruiters are available as they can shortlist candidates for further rounds according to their requirements on the basis of the probability obtained. The current recruitment system recruiters do not possess candidate information apart from his/her CV. This proposed system aims to analyze the candidate performance and recommend candidates fittest for the job using Random Forest Regressor algorithm that will help to maximize the placement probability of candidates easing the recruiter’s task. Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. This system will provide ease and efficiency in recruitment process.
A Robust Intrusion Detection System using Ensemble Machine Learning

In today’s world there is a rising need for network attack analysis because of rising cyber threats and attacks worldwide. Network traffic, if monitored dynamically in real-time could prevent a big cyber attack or even alert before. In this paper, UNSW-NB 15 dataset is used on Ensemble method to analyze the network traffic. The major contribution of this paper is the novel Algorithms powered by boosting algorithm to come up with the best classifier from the list of classifiers. It compares the classifier in terms of accuracy as well as training time which is significant in real-time analysis of network traffic. We performed experiment in which we took 10 classifiers and our proposed algorithm came up with XGB Classifier as the best one from the set in terms of accuracy and training time combined. We have demonstrated the comparison between running time of complete experiment on Central Processing Unit (CPU) and Graphical Processing Unit GPU).
Reduction of PAPR by Convolutional Neural Network with Soft Feed-back in an Underwater Acoustic OFDM Communication

The intelligent neural networks have proved to be the most efficient and less complex for orthogonal frequency divisional multiplexing (OFDM) communication in the last decade. Therefore, in this paper, a convolutional neural network method with soft feed-back is employed for the reduction of peak to average power ratio (PAPR) in an underwater acoustic OFDM communication system. The PAPR performance shows a great enhancement by using a convolutional neural network. The simulation results prove the excellency of the overall system, as it is the simplest, intelligent, and reliable for mitigation of PAPR, considering an equitable performance of bit error rate (BER).
F-score Based EEG Channel Selection Methods for Emotion Recognition

Emotion, as an advanced function of the human brain, affects kinds of human behaviors. Electroencephalographs (EEG) are widely used in the field of emotion classification owing to their low cost and portability. In this work, we study the effects of a non-linear EEG feature and a channel selection method on emotion recognition. First, the fractal dimension(FD) which could reflect the state of the brain is extracted with a sliding window. The top seven channels are screened out by calculating the F-score from the whole samples. Then, based on the signals from forehead channels, filtered channels and associated channels, emotions on valence and arousal are classified by Support Vector Machine(SVM) and K Nearest Neighbours(KNN). The result shows that the forehead channels Fp2, AF8, Fpz play an important role in valence classification. When combining the forehead channels with other channels that have higher F-score, the SVM classifier has a better accuracy on the whole set with 89.37% on valence and 87.07% on arousal. Besides, the overall accuracy calculated on each participants with associated channels get significant improvement. Especially, the KNN classifier has a much better result on every subject. This phenomenon indicates that by combining the higher F-score channels with the forehead channels, the associated channels can not only take advantage of the forehead channels' ability to categorize emotions but also consider individual differences.
Application of Fixed Frequency Sliding Mode Control for D-STATCOM in Power Systems

D-STATCOM is a Flexible AC Transmission System (FACT) device used to control the reactive power flow in the distribution system. It can be operated in inductive/capacitive operation modes to inject/absorb reactive power. In this paper, a fixed frequency sliding mode control (FFSMC) is designed for the application in a D-STATCOM. To realize the proposed FFSMC, two test cases are performed. In the first case, the voltage sag/swell of the main grid is considered. Meanwhile, in the second case, an unbalanced load condition is considered. The D-STATCOM is situated at the point of common coupling (PCC) to mitigate these issues as well as to improve the voltage stability and power quality. The proposed FFSMC design is based on a constant switching frequency operation and Gao's reaching law is used to eliminate the chattering phenomenon. The stability of the proposed controller is proved as well. The real-time simulation is carried out by using the MATLAB/Simulink environment to examine the performance of the proposed control. Moreover, the simulation results of the proposed FFSMC are compared with conventional proportional-integral (PI) control. The results indicate the advantages of proposed FFSMC in terms of low THD, robustness, and fast-tracking.
On the Optimal Design of Underactuated Fingers Using Rolling Contact Joints

The design of underactuated fingers using rolling contact joints is addressed in this article in order to increase performance in robotic and prosthetic hands applications. Rolling contact joints have proven to be a promising alternative to revolute joints by increasing the range of motion, introducing compliance and decreasing internal friction. The conditions of optimal design for fingers using such joints are however not well stated. In order to fully exploit the potential of rolling-contact-joint based fingers, their design space is extended and performance metrics adapted to underactuated fingers are used in this letter. An optimization based on these metrics is performed over the large design space using genetic algorithms to find an optimal design. The solution obtained is then used to build a prototype and its performance is compared to that predicted by the model and to a comparative design using a realistic test bench.
Voice Recognition Based Security System Using Convolutional Neural Network

Following review depicts a unique speech recognition technique, based on planned analysis and utilization of Neural Network and Google API using speech’s characteristics. Multifactor security system pioneered for the authentication of vocal modalities and identification. Undergone project drives completely unique strategy of independent convolution layers structure and involvement of totally unique convolutions includes spectrum and Mel-frequency cepstral coefficient. This review takes in the statistical analysis of sound using scaled up and scaled down spectrograms, conjointly by exploitation the Google Speech-to-text API turns speech to pass code, it will be cross-verified for extended security purpose. Our study reveals that the incorporated methodology and the result provided elucidate the inclination of research in this area and encouraged us to advance in this field.
Multimodal Surface Material Classification Based on Ensemble Learning with Optimized Features

In this paper, we propose a novel method for multimodal material classification based on ensemble learning and optimized features. The proposed method consists of three key steps. Firstly, we extract a set of features for each modality. Compared to existing methods, the extracted features are relatively simple but more effective when they are incorporated into the classifiers. Then, the feature selection algorithms, including Multi-Cluster Feature Selection (MCFS) and Laplacian Score (LS) are employed to reduce the feature dimension due to the curse of dimensionality. Finally, an ensemble learning method is proposed to integrate the merits of different feature selection methods. The effectiveness of the proposed method is demonstrated on the LMT-108 surface material dataset which includes multiple modalities such as sound, acceleration, and image. The experimental results have shown that our approach performs better than the competing methods.
On the Security of Authenticated Key Agreement Scheme for Fog-driven IoT Healthcare System

The convergence of Internet of Things (IoT) and cloud computing is due to the practical necessity for providing broader services to extensive user in distinct environments. However, cloud computing has numerous constraints for applications that require high-mobility and high latency, notably in adversarial situations (e.g. battlefields). These limitations can be elevated to some extent, in a fog computing model because it covers the gap between remote data-center and edge device. Since, the fog nodes are usually installed in remote areas, therefore, they impose the design of fool proof safety solution for a fog-based setting. Thus, to ensure the security and privacy of fog-based environment, numerous schemes have been developed by researchers. In the recent past, Jia et al. (Wireless Networks, DOI: 10.1007/s11276-018-1759-3) designed a fog-based three-party scheme for healthcare system using bilinear. They claim that their scheme can withstand common security attacks. However, in this work we investigated their scheme and show that their scheme has different susceptibilities such as revealing of secret parameters, and fog node impersonation attack. Moreover, it lacks the anonymity of user anonymity and has inefficient login phase. Consequently, we have suggestion with some necessary guidelines for attack resilience that are unheeded by Jia et al.
Review of Shortest Path Wireless Sensor Network Performance with Energy-Efficient Approaches

Improving the lifetime of a WSN which works in very cruel conditions, is one of the key difficulties looked by scientists and engineers of remote sensor network advancements. Dragging out the lifetime of WSN becomes considerably more significant in some delicate regions where re-energizing or supplanting the sensor hub batteries is unimaginable or not viable. In this proposition, the issue of improving the WSN’s lifetime is examined and is accomplished utilizing geography control calculations, which plan the sensors to preserve battery power mounted on them. Disjoint set K cover age method is applied for building the new and diminished geography that contains not many sensor hubs in dynamic state while taking care of the remainder of the hubs state. The idea of disjoint set K covers is utilized, where not many sensors are actuated and others are deactivated to save battery power for some time later. The sensors conveyed are isolated to frame a most extreme number of disjoint sensor subgroups and every subgroup is actuated for one discrete time-frame while different gatherings are in the rest mode to save the battery power.
Architecting Continuous Integration and Continuous Deployment for Microservice Architecture

Regardless of the size of organizations, they are migrating to microservice architecture for multiple reasons. Many of them have found that by embracing microservice architecture they can deliver software faster and efficiently. Managing small, fine-grained, and autonomous services is easier and allows teams to work on them independently. Adaptation of Agile practices together with methodologies such as the twelve-factor app for building microservices, enable flexibility, efficiency, and increase the speed of delivering the software. However, implementing a repeatable, adaptable, and reliable process for releasing software can be very challenging. Continuous Integration, Delivery, and Deployment workflows are determined by organization cultures and preferences as well as technical requirements. This paper presents an approach on how to design and implement a CI/CD process following the best practices on the build, test, deploy, and release processes. It also describes the challenges of designing CI/CD systems, and it outlines potential answers to those challenges.
Power Control of Solar Cell and Stable Energy Supply by High Voltage DC-DC Converter Equipped with Energy Storage

Here, the new structure of high-voltage gain DC-DC converter is proposed for photovoltaic (PV) power control. Since PV energy is not sustainable then an energy storage along with bidirectional DC-DC converter is used to control charging and discharging. In addition, to extract maximum power from the solar cell, perturb-and-observe (P&O) method is used after presenting the modeling of each par. The proposed control system is designed for boost converter and bidirectional converters. The power flow, voltage DC link, PV and battery parameters such as voltage, current and state of charge (SOC) in proposed system and its peak power point are evaluated under two scenarios of radiation reduction and temperature rise for a constant load by 2500W with a reference voltage of 500V.
Geo-Spatial Market Segmentation & Characterization Exploiting User Generated Text Through Transformers & Density-Based Clustering

In data analysis, context information plays a significant role in enhancing the quality of the insight obtained. Furthermore, spatial analysis helps understand spatial relationships among entities. Nevertheless, findings of a comprehensive literature review show that the characterization of geographic areas based on user generated content, such as text messages, has not been sufficiently explored. This paper focuses on investigating how to combine and exploit geographic information with user generated text content to detect geographic clusters of textual events, and infer relationships between each cluster and a fixed set of retail product categories, which we consider as an insightful way to perform spatial market segmentation. We propose a workflow composed of several machine learning models incorporating Transformers as an attention mechanism and BERT-based data augmentation capable of predicting product classes from Amazon product reviews and Twitter message corpora, and then characterizing the obtained geographic clusters based on their aggregated scores. The output of our system is an effective visualization of the geographic areas with their corresponding relevance score against a fixed set of categories. We trained a product document classifier achieving an F1-Score of 86% in the test set for product reviews, and of 76% in the test set for tweets; and validated our approach by manually annotating a subset of Twitter data with respect to ten product categories. Our approach provides practitioners with a mechanism to combine location context, a Transformer encoder, and transfer learning to derive insights from geo-spatial and text data; and researchers with opportunities to continue advancing the field.
Application of Data-Driven and Physics-Embedded Neural Networks in Wake Dominated Flows

In this paper, data-driven and physics-embedded deep neural networks (DNN) are analyzed as potential candidates for flow field reconstruction in fluid mechanics applications. The data-driven and physics-based algorithms are employed to model the canonical case of flow past a circular cylinder. It is shown that both types of DNNs model the flow fields well, with the physics-based alternative having a higher reconstruction accuracy. Moreover, it is seen that the data-driven model provides a reasonably accurate solution with a lower computational expense as compared to the physics-embedded model. Finally a discussion on the strengths and drawbacks of each model is presented compared to mainstream numerical methods.
Prediction of Type-2 Diabetes using Machine Learning Algorithms

Type 2 diabetes mellitus (DM) is a chronic condition whose prevalence has been gradually growing across the world. About 30 million people in India have Diabetes, and are many more at risk. Therefore, to preclude diabetes and its symptoms related to it, so early diagnosis is required. The idea behind utilizing different methods for hypothetical determination of Type-2 Diabetes dependent on indicative analysis the enhancement of the diagnosis period of the disease through the way toward assessing suggestive qualities and day by day habits, permitting the estimating of Type 2 Diabetes without the need of clinical tests through prescient analysis. An immense measure of clinical information is accessible today with respect to the infection, their manifestations, purposes behind sickness, and their consequences for wellbeing. Using various machine learning techniques, Since these algorithms are so accurate, the threat of Type-2 Diabetes can be prevised, which is crucial for the medical field.
Power Allocation Algorithm based on $N^{th}$ order Fibonacci Polynomial for Multi-User NOMA

In this paper, we propose a novel empirical power allocation algorithm based on individual term normalization of $N^{th}$ order Fibonacci polynomial for multi user Non Orthogonal Multiple Access (NOMA), providing a characteristic solution to allocation problem for n-users. A deterministic mathematical expression for same predetermined power allocation scheme for both superposition coding (SC) at base station domain and successive interference cancellation (SIC) at user domain has been formulated. Fibonacci polynomial has been defined and linked to power allocation algorithm corresponding to radial distance of users from BS with sole requirement of second order statistics (SOS). Holistic analytical reasoning has been carried out for generic NOMA system and an exact closed-form expression for bit error rate (BER) has been derived using probabilistic models, which has been applied to proposed allocation algorithm. Detailed analysis validates that proposed power allocation algorithm is independent of user channel state information (CSI) which averts cumbersome computations and no prior information regarding allocation is required to be relayed to user domain for successful SIC. Numerical and simulation results (upto 7 user NOMA) have been provided to demonstrate superior performance in terms of reliability and latency of the proposed algorithm in comparison to channel inversion, a CSI based algorithm.
Selection and Assignment of STEM Adjunct Faculty Using Text Data Mining

This paper presents the development and testing of a text data mining tool to assist in the selection and assignment of adjunct faculty to teach STEM courses. The tool scores the resume of a faculty member against course descriptions in a STEM graduate program. The tool returned a similarity score between a resume and course descriptions, which was then used as an indicator of faculty suitability to teach courses in the program. We enhanced the original tool with an improved user interface and deployed it to search for new faculty searches and in the process of assigning courses. A TD-IDF text analytic technique was used for similarity scoring. Our research question was to investigate whether a similarity-scoring tool for faculty resumes against course descriptions would be useful in the search and assignment process to hire faculty to teach specific courses. As part of our methods, we developed a friendly user interface to the existing tool using a student-centered coding contest. We applied the tool to the hiring and assignment of adjunct faculty. We measured success as the processing of a large number of open positions in a relatively short period of time and found a significantly high number of good fits between faculty and their course assignments. We investigated whether the scoring system positively correlated with the courses assigned to them.We successfully filled over 50 unassigned courses with appropriate faculty over a period of three months, where 30% were new hires. In the process, we discovered that the vast majority of the incumbent’s similarity scores positively correlated to the courses assigned to them. This generated sufficient confidence that the description scoring system has been integrated as part of our faculty hiring and assignment processes in our programs.
Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease

Artificial Intelligence has been proving a great boon in almost all the sector of industries. In recent times the demand for food has increased, whereas the supply still lacks. In order to meet these increasing demands, prevention and early detection of crop disease are some of the measures that must be inculcated in farming to save the plants at an early stage and thereby reducing the overall food loss. In this paper, we use a deep learning-based model and transfer learning-based models to classifying images of diseased plant leaves into 38 categories of plant disease based on its defect on a Plant Village dataset. Eight pre-trained models namely VGG16, VGG19, ResNet50, InceptionV3, InceptionResnetV2, MobileNet, MobileNetV2, DenseNet along with the one self-made model were used in our study. We found that DenseNet achieves the best result on the test data with an accuracy of 99%.