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Design of Computationally Efficient IFIR based Filter Structure for Digital Channelizer'
A low computational complexity digital channelizer is essential for a wide band system. FRM is a widely used method to generate a sharp transition width sub-bands or channels in a digital channelizer. The aim of this work is to design a uniform and non-uniform sharp transition width FIR filter bank with low computational complexity compared to FRM based digital channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on IFIR filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is demonstrated with the help of an example. Results show that the number of multipliers of the proposed structure is less compared to other existing methods. 2022 IEEE. -
Design of pseudo stator generator
In today's energy generation scenario, the extensive use of conventional sources are causing lot of environmental issues. It is necessary that humankind should come up with a strategy to produce clean energy. Even though we cannot completely stop relying on non-renewable sources of energy, lot of research is happening to find the ideal substitute for conventional sources of energy and also for migrating towards renewable energy sources from conventional sources. Its gaining popularity because of the fact that availability of fossil fuels are reducing at an alarming rate. Thus, these research works will aid in producing clean energy and also make the existing systems more efficient. A better substitution would be to design a machine which would use less conventional sources of energy and gives required output. Thus it is necessary to come up with a new technology which would suffice the above stated requirements. The proposed project is an novel idea aimed at designing an alternator which has higher power output at lower RPM when compared to conventional alternators. This model finds application in automobiles, WECS, Aerospace, hybrid vehicles in the near future. 2016 IEEE. -
Design of Reconfigurable Filter Structure Based on FRM for Wideband Channelizer?
A reconfigurable FIR bank of filters are essential for digital channelizer in wideband system. FRM is a extensively used method to generate a sharp transition width sub-bands or channels for digital channelizer. The aim of this work is to design multiple non-uniform sharp transition width FIR bank of filters with reduced number of multipliers and group delay for wideband channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on FRM filters and exponential modulation (EM) technique. The performance of the proposed structure is illustrated with the help of an example. Result shows that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
Design, analysis and fabrication of EV with level-1 autonomous vehicle capability
The fact to this day remains true and the same for over a hundred years the Automobile industry and vehicles, in general, have become the pivoting point in our day to day lives. We might as well call it a necessary evil. Although it is very true that they have made our lives more convenient when we speak in terms of transportation; the pollution that conventional IC engine vehicles produce hasn't done much to create a cleaner environment especially with Global warming on the rise as we speak. The simplest remedy would be is to replace IC engine vehicles with Electric one, EV. A Problem common to both conventional IC engine vehicles and EV's alike is the accidents occurring due to collision caused by human error on-road. While safety measures have greatly been taken in order to reduce the damage done to the driver and passengers in the event of a collision it would be far better to avoid the collision altogether. Thus having at least, a Level-1 Autonomous Vehicles capability where the system alerts the driver in the event of a crash or collision and deploy full braking capability. Thanks to increasing urbanization and the advent of modern technology the need of the hour of the 21st century has given rise to high demands for employment in the motorized transport sectors. The authors were successfully able to design, analyze and fabricate an EV with Level-1 Autonomous Vehicles capability. The successful implementation of this project will help in reducing not only pollution and accidents occurring on-road due to vehicle collision but also pave paths in alimenting Level-1 Autonomous Vehicles capability in EV's inexpensively. 2020 Author(s). -
Design, development and characterization of polyelectrolyte multilayer membranes for potential filtration applications
The present work projects to modify the surface of commercially available microfiltration membranes using polyelectrolyte multilayer assembly. This polyelectrolytic modification changes the nature and properties of the membrane such as porosity, absorption capacity, charge etc. which can be used for potential environmental application such as herbicide removal from water. Chitosan (CHI) and polystyrene sulfonate (PSS) were incorporated into nanostructured polymeric films on polyamide membrane using layer-by-layer (LbL) technique. The developed polyelectrolyte multilayer assembly were characterized by using UV-vis spectroscopy, attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy, thermo gravimetric analysis (TGA) and atomic force microscopy (AFM). 2020 Elsevier Ltd. All rights reserved. -
Design, development, and analysis of segment support system for TMT primary mirror
The Thirty Meter Telescope (TMT) adopts a recently developed technology known as Stressed Mirror Polishing for the polishing of its 492 mirror segments. In this process, first the meniscus type spherical shape glass blanks are converted in to a desired aspheric shape by the application of forces around the edges using warping arms followed by spherical polishing in the stressed condition. After that, the blank edges will be cut in to its final hexagonal shape. These warping as well as the hex cutting process generate significant stress within the glass which in later stage, will cause the propagation of micro cracks and results in blank breakage. So prior and after the hex cutting process, it is essential to ensure that the glass blanks are free from stress accumulation. Hence the glass blanks need to be stress relieved before the hex cutting process. To achieve this stress relaxation, the glass blanks need to be kept over a platform or a support system which will provide a zero gravity condition for a time period of at least 48 hours. As a part of this, we designed, developed and analyzed a whiffletree based support system which will equally distribute the entire mirror blank mass into three points which are equally separated by 120 from each other and thus balance itself as if it is in a floating condition. This support system which additionally gives optimized support for the glass blank which in turn minimizes the surface deformation due to its self weight sagging. This paper also discusses the positional sensitivity, reaction force sensitivity and alignment sensitivity analyses which are essential to obtain the tolerance values in the fabrication point of view. 2020 SPIE. -
Designing a Precision Seed Sowing Machine for Enhanced Crop Productivity
A seed sowing machine is a valuable agricultural device that facilitates the precise and efficient sowing of seeds in fields. When designing and optimizing such a machine, several crucial factors need consideration including seed size, seed rate, soil type, and field conditions. The primary objective is to achieve uniform seed distribution and optimal seed-to-soil contact, which can be accomplished by incorporating a seed metering mechanism to control the seed rate accurately. Versatility is another important aspect of the machine's design, as it should be able to handle different seed sizes, types, soil conditions, and field variations. To achieve this, utilizing advanced technologies such as sensors, automation, and precision farming techniques can significantly enhance the machine's performance and efficiency while also reducing costs and minimizing environmental impact. The optimization of a seed sowing machine plays a crucial role in ensuring successful crop production. By implementing cutting-edge technologies and precision farming techniques, farmers can increase their yields and decrease the amount of seed and fertilizer needed for a specific area. Ultimately, this leads to improved productivity, increased profitability, and a more sustainable approach to agriculture. 2024 E3S Web of Conferences -
Despeckling of Ultra sound Images using spatial filters - A Fusion Approach
Ultra sound images are normally affected by speckle noise which is typically multiplicative in nature. This study proposes different fusion based despeckling methods for ultra sound images. The output of existing spatial domain despeckling methods viz. Lee filter, Bayesian Non Local Means (BNLM) filter and Frost filter are fused pairwise. Fusion is implemented in two steps, first an inter-scale stationary wavelet coefficient fusion followed by an intra-scale wavelet coefficient fusion. Analysis of these projected despeckling strategies are conducted using metrics like Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI). The results show that the performance of fusion based methods is better than the respective individual filters for despeckling ultra sound images. 2019 IEEE. -
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE. -
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
Detection and Classification of Potholes in Indian Roads Using Wavelet Based Energy Modules
Maintenance of roads is one the major challenge in the developed countries. The well maintained roads always indicates the economy of the whole country. The heavy use of roads, environmental conditions and maintenance is not performed regularly that leads the formation of potholes which causes the accidents and unwanted traffics. The paper discuss about the detection of potholes based on wavelet energy field. The proposed method mainly includes three phases (A)Wavelet energy filed is constructed in order to detect the image by using geometric criteria and morphological processing (B)Extracting Region of intersect by edge based segmentation technique (C)Classifying the potholes using Neural Network. 2019 IEEE. -
Detection and Classification of Thoracic Diseases in Medical Images using Artificial Intelligence Techniques: A Systematic Review
Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in Computer-Aided Diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence Techniques to identify Thoracic Diseases in Medical Images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria. The Electrochemical Society -
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE. -
Detection of colorectal cancer using dilated convolutional network via Raman spectra
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection plays a crucial role in improving patient outcomes and reducing mortality rates. In recent years, Raman spectroscopy has emerged as a promising tool for non-invasive cancer detection. This research introduces a new method for identifying colorectal cancer (CRC). It combines Raman spectroscopy, a technique that analyzes the molecular fingerprint of tissues, with a powerful deep learning algorithm called a dilated convolutional network (DCN). By combining these two tools, the researchers aim to improve the accuracy and reliability of diagnosing CRC. Intraoperative diagnostics and pathology need to distinguish tumors from normal tissues. This proposal explores Raman spectroscopy as a new surgical tool for identifying colorectal cancer during surgery. Raman spectroscopy offers a way to directly analyze the makeup of tissue, potentially revealing the presence of cancer. However, surrounding tissue can create background interference, making it difficult to detect the key signal. The authors suggest that high-quality data from Raman spectroscopy combined with advanced deep learning algorithms could be a solution to overcome this challenge. We collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with Raman shifts from 385 to 1545 cm-1. Second, dilated convolutional networks classify colorectal cancer tumour tissues. Following the deep learning model's output, we proceed by visualizing and analyzing the identified fingerprint peaks. Our deep learning algorithm exceeds previous colorectal cancer detection methods with 99.1% accuracy. Colorectal cancer detection using Raman spectra is unique. Our ensemble DCN could classify colorectal tumour and normal tissue Raman spectra. 2024 Author(s). -
Detection of DoS Attacks Using Machine Learning Based Intrusion Detection System
Conventional intrusion detection systems are not always sufficient due to the increasing sophistication and frequency of Denial-of-Service (DoS) attacks. This work presents a novel solution to this problem by leveraging machine learning techniques to increase the precision and efficacy of real-time intrusion detection. The system keeps a careful eye on network traffic patterns, looking for any irregularities that would point to a denial-of-service attack. An Intrusion Detection System (IDS) that utilizes machine learning technologies - specifically, neural networks and support vector machines - allows for real-time adaptation to new attack patterns. A combination of rigorous simulations and real-world testing provides empirical support for the IDS's quick detection and mitigation of DoS threats. This initiative makes a major contribution to the development of cybersecurity defenses. 2024 IEEE. -
Detection of faces from video files with different file formats
Face detection is the primary approach of all fundamental problems of human computer interaction system (HCIS). This paper evaluates the performance of detection system on single face from stored videos that are stored in different file formats. Stored videos contain raw homemade datasets as well as ready-made datasets. This proposed work concludes detection percentage of face detection system in different video formats. The implementation is done in two phases. The raw homemade dataset is tested on.3gp,.avi,.mov,.mp4 and a ready-made dataset is tested on.wmv,.m4v,.asf,.mpg file formats. The coding part for face detection has been done in MATLAB R2013a. The detection of faces from video file was 72.79 % for homemade dataset and 82.78% for ready-made dataset. 2016 IEEE. -
Detection of fake opinions on online products using decision tree and information gain
Online reviews are one of the major factors for the customers to purchase any product or to get service from many sources of information that can be used to determine the public opinion on the products. Fake reviews will be published intentionally to drive the web traffic towards the particular products. These fake reviewers mislead the customers to distract the purchasers mind. Reviewers behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. In this work the reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using decision tree classifier and Information Gain.Significance of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficacy of the proposed approach. 2019 IEEE -
Detection of Fraudulent Alteration of Bank Cheques Using Image Processing Techniques
In todays world illegal alteration and illegal modifications of authenticated financial documents is increasing rapidly as a fastest growing crimes around the world. The result of this kind of crimes may result in a huge financial loss. In this paper image processing and document image analysis techniques are used to examine such cases in order to identify the fraudulent bank cheques. However, it is very difficult to detect an alteration made on documents once the printing ink of alike color is employed. In this paper, alterations and modifications caused with handwritten ball point pen strokes are considered and proposed a technique for recognition of such types of corrections by employing standard techniques under Digital image processing and pattern recognition. The results are quite promising during the experiments conducted. 2021, Springer Nature Singapore Pte Ltd. -
Detection of Lung Cancer with a Deep Learning Hybrid Classifier
This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE.