bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of an Embedded System for Islanding Detection and Control Action in PV Inverter system
Authors: Hake A. G. , Chavan S. B. , Kamat R. K. 
Published: September 2022

Abstract
Many countries utilize hybrid energy systems for power generation. Transmission lines are used to deliver electricity to remote places from a variety of sources. During islanding, power lines are broken or damaged by natural or man-made causes. If not detected, animal life may be endangered. This paper presents anti-islanding technique that can be used to detect and prevent islanding. In this work, a prototype is developed using microcontroller for an anti-islanding PV inverter control system. The system detects islanding and disconnects the load from the source inverter and switches off the inverter. This technique continuously monitors the output power. A sudden drop in output power is indicative of islanding. The system immediately isolates the PV inverter from the output load and switches off all processing devices. A relay is used to isolate the PV inverter from the load. In the system developed here source isolation time is equal to relay switching time.
Multi-Tasking Robot using Microcontroller for Agriculture Application
Authors: Sebastian Vindro Jude P. , Kathirvel C. , VenkateshKumar S. , MohanKumar R. 
Published: September 2022

Abstract
With innovations in every field of our lives, agricultural field also gets impacted by technological innovations on a huge scale. Agriculture is the backbone of India. In order to support this, a multi-functioning robot is required. Smart farming aids in boosting the quality and quantity of the yields. Currently, smart agriculture is implemented to save the manpower and time of work with the help of multitasking robots. This is the advanced booming technology in the agriculture sector. However, certain considerations and approaches should be taken when designing these types of robots, taking into account the agricultural setting in which they will be used. The main aim of this research is to provide smart farming at low cost without compromising on the efficiency of the processes involved. This paper discusses the design procedure and development of an independent multitasking robot that could be applied for agricultural purpose like soil digging, sowing seeds, harvesting as well as irrigation. This robot works on solar energy and will also help in conserving non-renewable energy resources on a large scale thereby contributing towards the sustainability of our planet. With help of technologies, this idea kindles the knowledge in developing more intelligent and smart robots for the agriculture activities.
IoT Based Intelligent Low-Cost Anti-Collision Cum Monitoring System for Fishing Vessel
Authors: Wazarkar Pranav S , Bhurkunde Neel S , Yadav Nitin S , Patil Sajal S , Joshi Poorvi K 
Published: September 2022

Abstract
The collision of fishing vessels is the problem many fishermen face. Due to the lack of a proper system, this problem persists and inevitable danger needs to be solved. Even after detecting the objects that may collide with the ship, the captain may not be able to manoeuvre it to safety. The system proposed here would be able to overtake the control of the motor and the propeller, to slow down the ship. In this paper, an ultrasonic sensor is used to detect objects; and the system alerts the crew of the danger ahead. A DC motor is also used to replicate the operation of the propeller of the ship and emergency braking system. For control and sensing parameters, Arduino Uno is used as the microcontroller. To display the Real-time data on the web, ESP-S01 (8266) module is used. The relative distance of the object is logged on thingspeak through the ESP module. A machine learning model is implemented using Support Vector Machine (SVM) to predict the appropriate direction to avoid collisions, The testing accuracy of the model is 98%. Similarly, the logs and live updates help the port rescue team to help the crew under distress.
Design and implementation of power efficient clock gated dual-port SRAM
Authors: Bhat Mohammad Waqar , Kaartik R , Sowmya K B 
Published: September 2022

Abstract
Synchronous clock gated Dual-port RAM has been designed in this paper. To increase the design’s power, a negative latch-based clock gating approach was used. On Xilinx Vivado ML Edition, the design was implemented on an XC7Z010ICLG225-1L device with a - 1-speed grade and the Zynq-7000 FPGA family. The Verilog HDL was used to create the design. A 2-bit AND gate is used to realize clock gating which takes the clock and enable signal as input thus clock become only high when enable is high which reduces the switching frequency and thus power consumption by the clock is reduced as power is directly proportional to (frequency)2. Apart from clock gating, the SRAM is Dual-Port based, allowing multiple reads or writes to occur simultaneously, or at approximately the same time, unlike single-port RAM, which permits only one access at a given time. The power consumption value of the clock signal with clock gating at 100 GHz is 627.61 mW as compared to power consumption without clock gating which is 1405 mW, at a clock frequency of 100 GHz, a 55 percent reduction in total clock power was achieved. Xilinx Vivado ML Edition’s XPower Analyzer tool was used to calculate the device’s power.
Design and implementation of zeta converter for LED driver applications
Authors: Gnanavadivel J. , Shunmathi M. , Raghul I. , Mani Bharathi R. , Maneesh E. 
Published: September 2022

Abstract
The objective of this study is to analyze the design and operation of low power, stand-alone active led driver applications. The system is powered by solar PV panels with a maximum input power of 125 W. The chosen converter supports continuous conduction mode. The device seems to have a switching frequency of 20 kHz. The duty cycle of a converter is controlled by the MPPT controller, which keeps the dc bus voltage constant. MPPT employs an incremental conductance method. The intention of this converter used to control the voltage at 48 V. The preferred converter is verified in both Matlab/Simulink and its performance is evaluated on hardware prototype of 25 W. While PV module efficiency is low and power output is dependent on solar irradiation and ambient temperature, maximizing power output while increasing efficiency is of key significance. The resulting system is highly efficient, low cost and able to track power faster.
Effective and Efficient Honey Harvest Alert System for Bee Farms
Authors: Albert Raj A. , Kavin  , Jeeva T. , Kiran L.K 
Published: September 2022

Abstract
The technology described in this paper could be used to support the rural people who practice apiculture. The present innovation helps to increase the quality attributes of the honey by automating the process of monitoring the honey broods in order to obtain the first quality honey. The proposed invention continuously monitors the honey brood using the webcam and image processing technique is used to identify the capping level with absolute accuracy. We also monitor the humidity level inside the brood box using the humidity sensor ensuring the quality of honey produced and quality of the environment for the sustaining of bees. Image processing and machine learning is used to identify the level of capping occurred in the beehive and first quality of honey could be extracted if at least 75% of cells of the hive are capped.
Intrusion Detection System using Machine Learning and Microwave Doppler Radar
Authors: Ushasree A , Datta Atluri Shanmukha , Krishna Varikallu Sai , Reddy Pingili Manish , Kumar Rampelly Sanjay 
Published: September 2022

Abstract
A security system is an essential part of a safe home as thefts and intrusions are increasing in recent times; moreover, keeping the home safe is a major concern while being away. This paper proposes an innovative way of security alert system using a microwave doppler radar and integrating it with other peripherals such as camera, alarm, and raspberry pi, implemented using machine learning techniques. The machine learning technique used is the deep neural networks. This structure helps in monitoring the target space while receiving notifications on WhatsApp and Email to improve the security.
Routing Protocols and Security Issues in Vehicular Ad hoc Networks: A Review
Authors: Khanpara Pimal , Bhojak Stuti 
Published: September 2022

Abstract
Vehicular Ad hoc Networks, also known as VANETs, are temporary wireless networks consisting of different types of automobiles as network nodes and the connections among these vehicles as links. VANET is a booming technology that has become quite popular in research, academia, and industry domains. A VANET can be considered as a moving network in which the nodes are automobiles. As VANETs are basically ad hoc networks, they have all the fundamental characteristics of ad hoc networks including computational and storage constraints. A network of VANETs can also be formed to enhance the safety and other services for vehicle drivers. VANETs are proven to be efficient in many real-life scenarios including traffic control and driver assistance. Information communicated through VANETs can definitely help to improve the user driving experience and avoid collisions. This paper discusses various deployment scenarios for VANETs along with the communication requirements and security concerns.
Diabetic Retinopathy Detection Using Automated Segmentation Techniques
Authors: Prabha S. , Sasikumar S. , Leela Manikanta Ch. 
Published: September 2022

Abstract
This paper contains a brief discussion about Diabetic Retinopathy. As the name indicates, it’s a medical complication present in diabetic patients which affects the retina. Diabetic Retinopathy acronymed as DR is a medical circumstance where the high glucose levels in the blood start affecting the blood vessels in the retina. The paper discusses the non-invasive technical method to detect diabetic retinopathy involving various algorithms in every phase of the process. The input fundus images are taken from STARE Database. The methodology conveyed in this paper involves contrast-limited adaptive histogram equalization for noise cancellation purposes and enhancing the base contrast of the image. The Segmentation consists of 2 steps and the first step consists of the Fuzzy C-Means clustering primarily to find the coarse vessels present in the retina. Additionally, the Region-based active contour is used to select the region of interest which is to highlight the blood vessels. As a result, Our proposed segmentation method extracts the blood vessels accurately, resulting in the similarity measure value of 85%. Furthermore, these segmented retinal blood vessels are given as the input to CNN classifiers in order to detect Diabetic Retinopathy. For our proposed method, an overall accuracy to detect DR was 92%. This methodology can be used for mass screening processes in the field of ophthalmology.
Quality of Service Aware Performance Improvement in Wireless Body Area Sensor Networks
Authors: Anoop Ashley , Jariya Nazrin , Ashna Aziz P 
Published: September 2022

Abstract
Modern healthcare quality has been under increasing pressure. Wireless Body Area Networks have enabled better monitoring of health and reduced costs. A wireless body area network comprises miniature sensors that can be worn or implanted to record a person’s health data. The uninterrupted and quick access to a patient’s health status makes it easier for the doctor to diagnose in an emergency. The network uses C.S.M.A/C.A during data transmission. With packet collision and loss being two significant issues, we designed an enhanced algorithm to improve the overall performance and Quality of Service of the Wireless Body Area Networks by introducing aging factors. The algorithm avoids unnecessary long backoff periods and brief backoff periods that lead to performance degradation, improving various Quality of Service parameters like packet reception rate, packets received per node, consumed energy, estimated network lifetime, and packet loss rate.
Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud
Authors: Mugeraya Sanath , Devadkar Kailas 
Published: September 2022

Abstract
With the emergence of new companies and the expansion of the information technology sector, the need for Cloud Computing becomes apparent. Currently, the enterprises are rapidly transitioning from monolithic architecture to microservice-driven architecture. This research study has discovered that all task scheduling algorithms were designed for a specific (set) number of virtual machines, which resulted in the bottleneck problem, where multiple tasks were assigned to the microservice scheduler and the execution time of processing the tasks was significantly increased. Therefore, to address this issue, a novel model was designed based on the number of tasks and accordingly the number of virtual machines were dynamically generated to send the tasks to the microservice scheduler one by one, and the difficulties with execution time were also addressed. The study also discovered that due to the multiple workloads on the microservices, resource allocation becomes extremely difficult. To address this issue, containerized microservices were discovered. Here, the microservices would be distributed in containers. To implement the dynamic work scheduling technique, a cloud microservice translator would be developed, where a user may upload a text file and quickly get it dynamically translated. The main aim of this research work is to improve the task scheduling and resource allocation in microservices.
Detection and Classification of Arrhythmias by Deploying Deep Learning Models
Authors: Padma T , Uday Kiran A , Jahnavi C , Rahul S , Raja Nehaa , Kamal Kumar M 
Published: September 2022

Abstract
Arrhythmias can be detected using an ECG signal, which is an important tool in the healthcare industry. ECG overall variation trends, original variation features, and their relative positions are used to classify arrhythmias according to sphere knowledge and large-scale data analysis. They haven’t been fully explored by being styles. CNN and hybrid CNN-LSTM models are used to address this problem. A LSTM and CNN are used to separate the ECG’s overall variation trends and its unique features. In this project the implemented models are CNN and Hybrid LSTM models to check which model is better in identifying the arrythmias based on the ACC, SEN, and SPE scores. The Accuracy of the CNN model is 74.4 percent, respectively, while the Hybrid-CNN LSTM scores are 83.5 on the MIT-BIH arrhythmias dataset.
Analysis of object detection techniques for bird species identification
Authors: Saxena Saloni , Thorat Sneh , Jain Prachi , Mohanty Rupal , Baraskar Trupti 
Published: September 2022

Abstract
The birdwatching community in India is extensive. Wildlife enthusiasts often face difficulty in identifying a particular bird. There exist, separately, object detection Machine Learning models as well as online directories for manual bird identification, but there is no approach combining the two for easy identification of birds in India. We present a technique that uses object detection algorithms such as Faster R-CNN and YOLOv5 to solve this challenge. Our dataset includes a total of 60 species of birds found in Maharashtra. Furthermore, these methods have been tested on datasets of various sizes to provide a thorough comparison of the two techniques and to better understand their behaviour as the dataset size and classes grow. The YOLOV5 model trained on 100 epochs for 3000 images achieved a mAP score of 0.78 whereas Detectron2 when trained on the same dataset achieved an IOU score of 0.001. These methods have not been tested on a dataset containing birds from peninsular India, particularly Maharashtra. As a result, we intend to extend the dataset and make bird identification easier for birdwatchers.
A Deep Learning based Advanced Gait Detection
Authors: Sumathi S. , Bhuvaneshwari P. , Harikrishnaveni V , Sangami S.A , Shivanee Bhuvaneshwari N.S 
Published: September 2022

Abstract
A gait is a biometric sign that people use to provide visual verification of viewing. Distinctive highlights need more than one movement cycle. In Gait Energy Image (GEI), for example, typical images of a frame with more than one cycle motion are analyzed. The unique stride highlights the required movement of an individual strolling more than one complete walking cycle. For instance, in Gait Energy Image (GEI), normal outline pictures of more than one entire walk cycle are processed. Be that as it may, there may be an incomplete step cycle in which information is accessible due to impediments. This research work has designed and developed a model for implementing stride arrangement and profound learning strategies. A separate walk dataset is used in our trial. The walk marks are removed from the Gait Energy Image (GEI) using the Convolution Neural Network (CNN). The classifier proposed here for grouping human gait postures is Mobile Net. Finally, the proposed methodology is evaluated on the OULP step dataset, affirming that the proposed design effectively reproduces total GEI even from the outrageously deficient walk cycles.
Surface waves propagating along the interface between a parabolic graded-index medium and a self-focusing nonlinear medium: exact analytical solution
Authors: Savotchenko S E 
Published: September 2022

Abstract
The waveguide properties of interface between a parabolic graded-index medium and a self-focusing nonlinear medium are described theoretically. The exact analytical solution to the wave equation with dielectric permittivity dependent on the distance from interface and on the electric field intensity is found. The obtained solution describes the new type of nonlinear surface wave. It is shown that the electric field is localized completely inside the parabolic graded-index layer. The dispersion equation determining explicit dependence of the effective refractive index on the thickness of the parabolic graded-index layer and the change in dielectric constant in it is found in a particular case corresponding to the exotic surface wave propagation. The influence of the optical parameters on the distribution profile of the electric field across is analyzed. The decrease in the field in surface waves with distance from the interface is non-exponential.
Evaluation of computed tomography dose profiler probe for computed tomography dose index and geometric efficiency measurements
Authors: Akhilesh Philomina , Pathan M S , Sharma S D 
Published: September 2022

Abstract
The purpose of this study was to evaluate the performance of solid-state sensor based computed tomography dose profiler (CTDP) probe for measurement of standard computed tomography dose metric CTDI100 and free in air geometric efficiency for various beam widths available in a 128-slice CT scanner and also to estimate the efficiency of CTDI100 metric. The response accuracy of CTDP probe was verified using a standard 100 mm long ionization chamber. The geometric efficiency measurements performed by the CTDP probe were validated using XR-QA2 radiochromic film measurements. The efficiency of CTDI100 metric was assessed by calculating the ratio of CTDI100 measured in the center hole position to CTDI∞ measured in the same position of both head and body phantoms. The weighted CTDI values derived from CTDI100 measured by the CTDP probe showed an average difference of 8% from ionization chamber measured values. The efficiency of CTDI100 metric estimated using CTDP probe and 150 mm long phantoms was in the range of 82% to 86% and 76% to 80% for head and body phantom measurements respectively. The differences in the geometric efficiency values for various beam settings and tube voltages measured by the CTDP probe and films were within 7%. Taken together, the results of this study proved that unlike the 100 mm long ionization chamber, the CTDP probe can be efficiently used to determine CTDI for any length over which dose integration is desired and also measure geometric efficiency of MDCT scanners for various beam widths in helical mode of operation.
IgG and IgM Immunohistochemistry in Primary Biliary Cholangitis (PBC) and Autoimmune Hepatitis (AIH) Liver Explants
Authors: Hsu Maylee , Ju Jennifer Y , Pearson Meredith M , Yu Lei , Swanson Paul E , Yeh Matthew M 
Published: September 2022

Abstract

Objectives
Primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) can be difficult to distinguish in end-stage liver disease. Previous studies have shown that immunoglobulin G (IgG) and immunoglobulin M (IgM) immunostaining can differentiate AIH from PBC in needle core biopsy specimens, and we seek to extend these data to cirrhotic liver explants, in which the histology of AIH or PBC may be indiscernible.

Methods
Clinical data were reviewed for 20 patients with PBC cirrhosis and 16 with AIH cirrhosis. Immunohistochemistry for IgM and IgG was performed on representative blocks of explanted livers. Three high-power fields with the highest concentration of IgG- and IgM-positive plasma cells were counted and compared.

Results
The average number of IgM-positive plasma cells was significantly higher in PBC explants (7.3) than in AIH (1.8) (P = .001). There was no significant difference in the average number of IgG-positive plasma cells in PBC (2.5) and AIH (2.8) (P = .8). The IgG/IgM ratio was more likely to be less than 1.0 in PBC (17/20, 85%) compared with AIH (7/16, 44%) (P = .01).

Conclusions
Our study demonstrates that the absolute number of IgM plasma cells is greater in explants of cirrhotic PBC compared with AIH. These findings may be helpful in the evaluation of cryptogenic cirrhosis.
Systemic Presentation of Somatic TET2 Mutated B-Cell Lymphoma in a Child With Kabuki Syndrome and a Germline KMT2D Variant
Authors: Kumar Jyoti , Alspach Amy E , Zucker Jacob P , Gratzinger Dita 
Published: September 2022

Abstract

Objectives
Kabuki syndrome (KS) is a rare congenital malformation syndrome associated with germline KMT2D mutations. Recurrent somatic mutations in KMT2D have frequently been observed in B-cell lymphoma, but limited studies are available that evaluate the genetic landscape of B-cell lymphomas in the setting of KS.

Methods
We describe a unique case of B-cell lymphoma that illustrates histologic features of pediatric-type follicular lymphoma (FL) in a young patient with KS and autoimmune disease who showed a systemic presentation of widespread lymphadenopathy and clonal lymphocytosis.

Results
We present the first reported case of a young patient with KS harboring a germline KMT2D variant and presenting with a systemic CD10-positive, BCL2-negative B-cell lymphoma of follicle center origin illustrating histologic features of pediatric-type FL. Targeted next-generation sequencing of the B-cell lymphoma showed somatic TET2 and subclonal CXCR4 variants. These findings suggest that abnormal epigenetic regulation caused by alterations in KMT2D and TET2 may have played critical roles in promoting lymphomagenesis in this patient.

Conclusions
This unique case presentation highlights the importance of close clinical monitoring and the value of clinical context in the diagnosis of pediatric FL-like lesions in patients with KS.