Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Remote Nerve Stimulator for Cardiac Patients

Human heart is one of vital organs that sustain our lives and it is important to treat it as a valuable commodity. Abnormalities in heart function is perceived as one of the prominent causes of demise in the globe as a result of various issues related to the absolute working of the heart. Cardiac arrest is an unusual fall in heart functioning, alertness and respiration. Stated situations mostly arise due to the presence of complications with electrical activity of the human heart system, as a resultant worsen the ability of hearts to pump and break the continuous flow of blood in the human body. To overcome this usually an electric pulse or allowable shock was given through a defibrillator in order to normalize the heart rate. In this proposed work, a bio-signal such as an electrocardiogram (ECG) of the sufferer is frequently monitored and the relevant data are maintained in the cloud based on the developed supervisory control and data acquisition (SCADA) technique with regard to tackling the observed heart related complications in literature. In addition, the remote control of defibrillation is possible by employing the proposed strategy that excludes the overloading of healthcare workers.
Chewiness Evaluation System for 3D-Printed Noodles Using the Implantable Gel Biter

The increasing prevalence of 3D food printing technology is shaping culinary cultures, including the creation of noodles with specific thickness and form. However, a critical challenge exists in assessing the textural characteristics or chewiness (“koshi”) of these 3D-printed noodles, making it unclear whether they offer novel sensory experiences or mimic existing noodles. This study proposes a system to quantitatively evaluate and extrapolate the chewiness of 3D-printed noodles, using the “Implantable Gel Biter” which is a piezoelectric sensing mastication robot that mimics the human oral structure. These technologies work synergistically, allowing for the accurate assessment of noodle quality based on chewiness, with a focus on texture and elasticity. The developed system, underpinned by soft matter based reservoir computing, successfully identified the firmness characteristics of noodles with an accuracy rate of 81.9%, indicating its effectiveness in discerning variations in maximum stress in different noodles. This breakthrough could lead to the development of a new food texture analysis device.
Weed Segmentation: A Novel Dataset, Benchmark and YOLO Baseline

Farm ecosystems are seriously threatened by woody weeds’ invasion of other plant species. In addition to shading out and replacing native flora, these invasive plants actively compete with primary agricultural plants for scarce resources including sunlight, water, air, and soil nutrients. Weeds have a negative effect on crop quality and productivity in a number of ways, including lower yields and lower overall production efficiency. In order to overcome this obstacle, new approaches are needed. One such approach is the development of large-scale, carefully annotated image collections that are specialized for weed identification in different cropping systems. Our work presents a fresh resource—the Weed Segmentation dataset—in answer to this requirement. Using this dataset, we create a baseline for classifying weeds and use advanced data-driven artificial intelligence models for weed detection. Of the models that were assessed, YOLOv5s is the model that performs the best, scoring the maximum 54.64% mAP@[0.5:0.95:0.05]. This notable result shows YOLOv5s as a strong baseline for the suggested dataset, which is a big step forward for precision agriculture and weed control.
SeaNet: A Deep Learning Architecture for Enhanced Sea Surface Temperature Forecasting

Sea Surface Temperature plays a crucial role in the Earth's climate system, impacting weather patterns, ocean circulation, and marine habitats. Accurate SST forecasting is vital for various environmental and climatic applications, aiding in informed decision-making. This research proposes two neural architectures for automated time series forecasting of sea surface temperature, SeaNet-1 which is a combination of Long Short-Term Memory and Gated Recurrent Unit networks, and SeaNet-2, which is a transformer-based model with a relatively low number of parameters as compared to SeaNet-1. Experimental findings showed that the suggested models can predict sea surface temperature effectively and precisely. The experimental results achieved a remarkable R-squared score of 95.16% and an incredibly low RMSE of 27.28%. The proposed models perform better in terms of performance and computational efficiency when compared with other existing state-of-the-art models. The proposed models offer promising tools for oceanography, climate studies, and environmental management, supporting informed decision-making and sustainable resource utilization.
Development of a Stretchable and Removable Electrical Interconnection Solution for Ultra-Thin Electronic Components

Currently, the solutions for interconnecting electronic components with their active face facing on substrates are based on metallic soldering. The mechanical contacts are therefore rigid. In order to enhance the reliability of the bonding, underfill is usually used to redistribute the thermo-mechanical stress created by the Coefficient of Thermal Expansion (CTE) mismatch between the silicon chip and substrate. Underfill is done with epoxy resins containing silica fillers (SiO2). As a result, the removal of components is no longer possible. Moreover, this solution is not suitable for devices integrating ultra-thin silicon components $(< 100\mu \mathrm{m})$ hybridized on flexible substrates that may be subject to deformation. This is the case, for example, of medical “patches” worn on the person and continuously solicited. Indeed, the rigid contact points are likely to break. To address this issue, we are developing a thin anisotropic conductive and stretchable adhesive film inspired by the adhesion of the gecko. Thanks to the microstructuration of its toes involving about 1 million setae, the gecko can develop a large contact surface and thus a large force of attraction by the multiplication of van der Waals interactions. In this work, this “dry adhesion” based on the principle of “contact splitting”, was implemented in order to improve the adhesion of a flexible interconnection. For this purpose, the surface of a polydimethylsiloxane (PDMS) film was structured with micrometric mushroom-shaped patterns known to be the most efficient form of contact. To this end, silicon molds with varying mushroom geometries were used to shape the PDMS (with different cap and pillar diameters) and the adhesion force microstructured films were assessed (shear and pull-off experiments). To make these films locally conductive through the thickness, a conductive composite was prepared and locally deposited in the mold. One approach we investigated, was using a screen-printing mask. This approach has been implemented and characterized using electrical tests (I-V measurements) in order to select the most suitable films to make a flexible interconnection.
Machine Learning – Based Crop Management: Towards Intelligent Recommendations for Crop, Fertilizer and Pesticide Optimization

India heavily relies on agriculture as a key income source, but frequent natural disasters lead to crop failures and farmer suicides. Accurate crop production predictions can aid farmers and government in planning storage, pricing, and trade. Data science, with its analysis of variables like soil quality, weather, and more, offers a suitable approach for precise crop yield forecasting. Research focuses on data science methods for this purpose. The machine learning model achieved an impressive accuracy score of 96.44%, surpassing the target accuracy of 90%. This accomplishment is particularly notable considering that the fertilizer recommendation component relies solely on a dictionary-based solution. The last module of the system provides pesticide advice, which is determined through the implementation of a deep learning model used to identify pests.
The complete chloroplast genome sequence of Lavandula angustifolia (Lamiaceae), an introduced aromatic plant growing in China

Abstract The chloroplast genome of ‘Jingxun 2,’ a new cultivar of Lavandula angustifolia, was sequenced in view of its role as an economically important aromatic plant with wide applications. De novo assembly with whole genome sequencing data completed the chloroplast genome (cp genome) of 151,459 bp in length, which included two inverted repeats (IRs) blocks of 25,467 bp, separated by the large single-copy (LSC) block of 82,945 bp and small single-copy (SSC) block of 17,580 bp. The genome encoded 123 genes consisting of 86 protein-coding genes, 29 tRNA genes, and 8 rRNA genes. The overall AT content of L. angustifolia cp genome is 62.04%. The AT contents of the LSC, SSC, and IR regions were 64.05, 68.14, and 56.66%, respectively. Further, a maximum-likelihood (ML) phylogenetic tree was reconstructed to indicate relationships among taxa Lamiaceae. The complete chloroplast genome of L. angustifolia provides utility information for aromatic plants evolutionary and genomic studies.
Spatiotemporal Motion Profiles for Cost-Based Optimal Approaching Pose Estimation

Recent public perceptions indicate a positive shift towards a society with human and robot co-existing, especially aged populations. The ability to socially navigate become crucial for mobile robots by enabling them to guarantee not only human physical safety but also psychological comfort, and enhance robots contextual awareness in human-robot interactions (HRI). In this study, we introduce an extended navigation scheme to approach moving target based on the tracking of human spatiotemporal motion, social studies on proxemics, and kino-dynamics of the mobile robot. The strategy utilizes existing multi-layer cost-based navigation mapping for complete integration with plannings and introduce soft social constraints by extending the costmap value range. The primary contributions include (i) spatio-temporal motion profiles (SMPs) of all humans under tracking, (ii) a social navigation cost function (SNCF) for filtering socially-optimal goal poses. The results drawn from simulated testings across three normative social situations, and statistical analysis demonstrate the SMPs effectiveness through measured spatial and temporal coefficients. The driving factors safety and appropriate social construct are determined to be either statistically or practically significant, while also introducing a complete navigation scheme taking into account of socially acceptable behaviours for the robot.
State-of-Art Facial Monitoring: A Machine Learning Perspective

This study proposes the use of real-time human facial tracking for interacting with robots. One of the key elements that enable humanoid robots to do difficult tasks is their visual system. As a result, there have been many investigations on gaze stability artificially, of which a large number are based on reflexes in the biological visual system. The gaze-stabilization approach incorporates Using processing of images, kinematic inverse, and control via feedback, the required gaze behavior is produced. The system is divided into two components. The human face is recognized, and positioned in respect to the original image, and its proportions are created for use in the second part in the first section. The second component determines the location and dimensions of the face by moving the camera by the offset between the intervals while monitoring the face. In contrast to prior work, which employed the Haar cascade method to recognize human faces, in plenty of situations, the Kanade-Lucas-Tomasi (KLT) technique is applied for face tracking. The offset between image frames, which was obtained from the prior stage, is used to offset the camera. Optimized deep neural networks like ResNet can raise the accuracy and performance of face recognition and real-time tracking techniques like Kalman filters are used to keep face tracking constant between frames. The findings demonstrate that real-time monitoring of human faces was still successful even when individuals were using face-side postures, glasses, or hats. This article describes the most frequent problems with face tracking, position changes, variations in face resolution, variations in lighting, and facial distortion.
Analysis and Implementation of Graphene Nanoribbon Field Effect Transistor based SRAM Cell

Graphene Nanoribbon Field Effect Transistor (GNRFET) these transistors can be considered as one of the alternatives for the silicon elements in the recent and future trends of designing integrated circuits. This paper introduces the design, analysis and implementation of a conventional 6T SRAM Cell for Graphene Nano Ribbon Field Effect Transistor (GNRFET) based transistor cell as well as CMOS based transistor cell for the 32nm Technology node. The Circuit analysis and simulation is done using Synopsys HSPICE simulator at the 32nm technology. The SNM is calculated for the different cell ratios on which GNRFET shows the notable performance compared to CMOS based technology. The better valued SNM is choosen for the future implementation.
Development of an Agricultural Robot for Strawberry Fruit Harvesting and Truss Pruning

This paper describes an agricultural robot for strawberry fruit harvesting and truss pruning. Although various agricultural robots were studied, most of them had single-function. The target fields for these were also a well-maintained environment in which they are relatively easy to introduce the agricultural robots, such as a plant factory. The field in which the developed robot in this study operates is a small-scale greenhouse, and this robot is a multifunctional rather than a single-function agricultural robot. This robot consists of the following: a crawler-type moving mechanism, a manipulator with 6 degrees of freedom, an end-effector capable of cutting and gripping, and an RGBD camera. In addition, to realize the functions of harvesting and pruning, a method of recognizing strawberry plants and detecting cutting points for both harvesting and pruning is implemented. Harvesting and pruning experiments was conducted at a strawberry greenhouse. This paper reports the results. For the results of the harvesting experiment, the harvesting time per fruit was approximately 20 seconds, and the success rate was 80%. For the results of the pruning experiment, the pruning time per truss was approximately 23 seconds, and the success rate was 80%. These experimental results demonstrate the effectiveness of the multifunctional agricultural robots.
Design of Intelligent Elderly Care System under the Background of Internet of Things and Big data

Elderly care services can improve the level of old-age care, happiness, and the quality of life in old age. However, in the process of elderly care services, there are problems of complex life data processing and inconsistent data on cultural differences, which in turn affects the quality of elderly care and reduces the level of services of elderly care institutions. In order to improve the quality of elderly care services, this paper designs an intelligent elderly care system, which analyzes the data of elderly care institutions with Internet of Things and big data. Then, health monitoring and early warning, life assistance and convenience functions and social interaction and loneliness alleviation are comprehensively evaluated, and the evaluation results are systematically analyzed, and finally personalized pension plans are made. The results show that the intelligent elderly care system can accurately analyze the elderly care service and improve the social pension level, and its accuracy is greater than 95% for the artificial elderly care service. Therefore, the intelligent pension system can meet the requirements of the elderly and provide auxiliary support for the social pension.
Implementation And Performance Comparison of CNN-Based Semantic Segmentation Methods for Biomedical Application

Skin lesion segmentation from thermoscopic images is crucial for better quantitative melanoma analysis. However, it is still demanding because of the skin lesions' large-scale variances and uneven shapes. Additionally, the likelihood of inaccurate segmentation may rise due to the blurred lesion borders between skin lesions and the surrounding tissues. There are different methods for skin lesion segmentation. Deep Learning has produced impressive skin lesion segmentation performance in the last few years. In this regard, U-Net-based deep learning architectures are prevalent for medical image segmentation. The paper focuses on the implementation training and methodology of U-Net and MultiResUNet architecture.
Temperature-Gradient-Based Partial Activation of Wet SMA Actuators for Displacement Output Linearity

Shape Memory Alloy (SMA) actuators have output non-linearities, posing challenges for control in robotic applications. As an alternative to model- and controller-based compensation, in this paper, with the objective of enhancing the linearity of the SMA output to make it possible to use conventional control algorithms, we present a novel physical compensation method. This method applies a temperature gradient to activate and deactivate specific longitudinal sections of the SMA. Adjusting the gradient's inflection point position allows control over the active/inactive length ratio, ideally linearly tied to the total actuator output. By using this activation technique, improving the output linearity of the SMA is possible. An actuator based on this concept was built and tested. The results of the experiment showed improved open-loop output linearity, time constant, and dead time compared to a typical SMA temperature controlled wet activation, which allowed us to combine the approach with a PID controller and displacement feedback to achieve a resolution of 2.6%, comparable to a typical pneumatic cylinder position control system. The concept presented brings about position control of SMA actuators without characterization experiments or temperature feedback and simplifies its control strategies.
Mango Disease Classification using CNN

Mango is a profitable agricultural product and widely consumed fruit. Mango production is significantly reduced when disease attacks the plant, which makes it difficult for farmers to sell their harvest. This issue drove us to create innovative methods for identifying diseases that damage mango plants. In order to maximise output and quality, it is imperative to stop these dangerous diseases early on. in our nation most many farmers lack literacy. They are unable to find correct information about illnesses as a result. It needs a knowledgeable advisor or agriculture officer. But reaching every farmer can be difficult for a professional counsellor or agricultural authority. So, in this project we are suggesting a trained model that will identify the disease of any mango plant by just providing its picture to the model. We have used Convolutional Neural Networks (CNN) to classify the images and we have trained the model on 4000 images and 8 different diseases. Accuracy of the model is around 98% and is giving accurate results.
Sensor Anomaly Detection for Biped Robot Using the Dynamic Equation of a Robotic System

Falling down is a critical event for biped robots. A robot can fall down easily when one of the sensors is out of order. This paper aims to develop an anomaly detection method for biped robots that can detect anomalies in sensor outputs. A large literature on anomaly detection exists: machine learning algorithms, statistical techniques, and various methods have been developed to detect anomalies. However, each technique performs well under certain conditions, and unfortunately none of these has proven to be efficient in accurately detecting anomalies in real-world situations for robotic systems. In this paper, we introduce a novel approach to detecting anomalies in a biped robot, based on the dynamic equation of a robotic system coupled with a robust z-score. Our method achieved unsupervised and real-time anomaly detection with great accuracy.
Research on Artificial Intelligence Machine Translation Based on Fuzzy Algorithm

In the technical field, achieving semantics for AI machine translation is a challenge that must be overcome. The model uses rule reasoning in natural language processing for deep understanding, and combines machine learning theory to optimize model parameters, so as to achieve the purpose of improving translation accuracy. After experimental verification, the model proposed in this paper shows a high accuracy rate of more than 90% when translating different translations; in addition, compared with DocNMT and SENTNMT, two common artificial intelligence machine translation models, the model proposed in this paper shows higher accuracy in machine translation, up to 97%, far exceeding them. At the same time, the model also has a certain degree of fault tolerance, which can ensure the stability and reliability of the machine translation system. Therefore, using the method proposed in this paper, the quality level of machine translation can be significantly improved.
Deep Distributed Learning and Spatial-Temporal Features for Facial Depression Detection

One of the most frequent and debilitating mental illnesses is major depression. Video-based identification of depression using facial expressions has been proposed by a number of deep learning architectures. These structures are frequently modeled for regression with Euclidean loss to foretell the degree of depression. The ordinal correlation between depressive symptoms and exposure to facial depictions is not explored, and the models' resilience to noisy and ambiguous labeling is constrained as a result. Famous deep architectures have shown guarantee in facial depression acknowledgment, but they still lack proper discriminative power because of problems with a lack of labeled depression information for deep transfer learning, large variations in facial expression between people who have identical symptom severity, and subtle variations in facial expression between varying depression severities. This study models the challenge of facial depression identification as one of the learnings to distribute labels, and this study offers a deep combined label distribution and metric learning approach to this problem. Using distribution learning, this research study provides a deep learning architecture for predicting depressive symptoms. The proposed deep learning on spatiotemporal feature improves the process of depression prediction because the two learning modules work together to improve the feature's representation ability and discriminatory strength. The results of empirical evaluation of the proposed method on publicly available datasets prove the system efficacy.
High-Q Ku-band Microstrip Spiral Resonator in Fan-out Wafer-Level Packaging (FoWLP) Technology for VCO Applications

In this paper, a planar spiral resonator operating at 13 GHz is investigated. The resonator was fabricated using fan-out wafer level packaging (FoWLP) technology. The field analysis highlights the importance of via fence. Two thick film resistors suppress an undesired resonance in proximity of targeted one. The measurements demonstrate a very good correlation in comparison with full-wave simulations. The measured Q-factor defined based on analysis of feedback oscillatory system is about 48. The proposed configuration is suitable for voltage-controlled oscillators (VCOs) with a resonator realized in package.