Software for full-color 3D reconstruction of the biological tissues internal structure

A software for processing sets of full-color images of biological tissue histological sections is developed. We used histological sections obtained by the method of high-precision layer-by-layer grinding of frozen biological tissues. The software allows restoring the image of the tissue for an arbitrary cross-section of the tissue sample. Thus, our method is designed to create a full-color 3D reconstruction of the biological tissue structure. The resolution of 3D reconstruction is determined by the quality of the initial histological sections. The newly developed technology available to us provides a resolution of up to 5 - 10 {\mu}m in three dimensions.Comment: 11 pages, 8 figure

A study to assess the effectiveness of ginger extract intake for reduction of nausea and vomiting of pregnancy among primigravida mothers in a selected clinic, Chennai

INTRODUCTION: Its estimated that around 80% of gravida experiences NVP. Though there are antiemetic drugs are available in the markets but it's had been established that it has some side effects. To develop and identity safe natural and traditional management of NVP by taking ginger extract, this research was carried out. AIMS & OBJECTIVES: AIM OF THE STUDY: A study to assess the effectiveness of ginger extract intake for reduction of nausea and vomiting of pregnancy among primigravida mothers in a selected clinic, Chennai OBJECTIVES: 1. To assess the level of NVP among primigravida mothers in experimental and control group. 2. To assess the effectiveness of ginger extract on NVP among primigravida mothers in experimental group. 3. To compare the pre-test & post-test level of NVP among primigravida mothers between the experimental and control group. 4. To find out the association between the effectiveness of ginger extract on NVP among primigravida mothers with selected demographic variables. RESEARCH METHODOLOGY: SETTING: A primary metropolitan clinic, data collection period from 21st November 2016 to 31st December 2016. PARTICIPANTS: The participants included 60 primigravida mothers of less than 16 weeks pregnancy, who had experienced morning sickness daily for at least a week or more. INTERVENTION: Random allocation of 3gms of ginger extract given twice a day for 07 days / 01 week continually. MAIN OUTCOME MEASURES: Nausea and vomiting as measured by the customised Rhodes Index of Nausea and Vomiting. RESULTS: The nausea experience score was significantly less for the ginger extract administered experimental group relative to the control group at P < 0.01. There is also significant association of demographic variables to the post-test level of N/V in the experimental group among primigravida mothers at P < 0.01. CONCLUSION: Ginger extract is now scientifically proven to be effective, safe and useful treatment option for women suffering from nausea and vomiting of pregnancy

EFFICIENT AND SECURE AUTHENTICATION BY USING 3-PATH TRANSMISSION IN AD HOC NETWORKS

Ad hoc networks are created dynamically and maintained by the individual nodes comprising the network. They do not require a pre-existing architecture for communication purposes and do not rely on any form of wired infrastructure; in an ad hoc network all communication occurs through a wireless median. The design and management of ad-hoc networks is significantly a challenging one when compared to contemporary networks. Authenticating the multicast session is an important one. To authenticate several factors should be considered, major issue are resource constraints and the wireless links. In addition to being resource efficient and robust, security solution must be provides to large group of receivers and to long multi-hop paths. The authentication must be done without much delay and should independent of the other packets. In existing TAM Tired Authentication scheme for Multicast traffic is proposed for ad-hoc networks. It exposed network clustering to reduce the overhead and to improve the scalability. Its two tired hierarchy combines the time and secret- information asymmetry to achieve the resource efficiency and scalability. In the proposed system, a Asynchronous authentication scheme as using shared key management is proposed to resolve the foremost conflicting security requirements like group authentication and conditional privacy. The proposed batch verification scheme as a part of the protocol poses a significant reduction within the message delay, then by using shared key process so requirement of the storage management is extremely less

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%

Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis

Artificial intelligence (AI) has evolved into a powerful tool that has wide-spread application in computer vision such as computer-aided inspection, industrial control systems, and navigation of robots. Monitoring the condition of machineries and mechanical components for the presence of faults with the aid of image-based automated analysis is one major application of computer vision. Diagnosing machinery faults from images can be made feasible with the adoption of deep learning and machine learning techniques. The primary objective of this study is to detect malfunctions in photovoltaic (PV) modules by utilizing a combination of deep learning and machine learning methodologies, with the assistance of RGB images captured via unmanned aerial vehicles. Six test conditions of PV modules such as good panel, snail trail, delamination, glass breakage, discoloration, and burn marks were considered in the study. The overall experimentation was carried out in two phases: (i) deep learning phase and (ii) machine learning phase. In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. Post selection of features, three families of classifiers such as tree, Bayes, and lazy were applied to determine the best feature extractor-classifier pair. The combination of DenseNet-201 features with k-nearest neighbour (IBK) classifier produced the overall classification accuracy of 100.00% among all other pretrained network features and classifiers considered

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.Validerad;2024;Nivå 1;2024-05-16 (hanlid);Full text license: CC BY</p