The document describes a proposed extension to a visual information narrator application using neural networks. The researchers developed an Android application that can generate image captions on inexpensive mobile hardware in real-time for visually impaired users. They trained an image captioning model using TensorFlow on the FLICKR dataset and implemented the model in an Android app to demonstrate real-world applicability and optimizations for low-cost hardware performance. The system uses an encoder-decoder neural network architecture that encodes an input image into a fixed-length vector using a convolutional neural network, then decodes the vector into a natural language description.
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to make use of several generators for particular classes, which limits its application to datasets that have a just a few classes available. Instead, I suggest a new Group Decrease Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting natural images with semantic marks and can produce high quality outcomes that are feasible for containing a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in task. Ashish Jobson | Dr. Kamlraj R "Fashion AI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41256.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/41256/fashion-ai/ashish-jobson
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
ieee projects 2012 for cse in networking trichy, ieee projects 2012 for networking chennai, ieee projects 2012 for cse bangalore, ieee projects 2012 for it hyderabad, ieee projects 2012 for me pune, ieee projects 2012 for mca nagpur, ieee projects 2012 for me cse tirupati, ieee projects for cse 2012 titles cochin, ieee projects for cse 2012 free download mysore, ieee projects for cse 2012 hubli, ieee mini projects for cse 2012 vijayawada
The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to make use of several generators for particular classes, which limits its application to datasets that have a just a few classes available. Instead, I suggest a new Group Decrease Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting natural images with semantic marks and can produce high quality outcomes that are feasible for containing a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in task. Ashish Jobson | Dr. Kamlraj R "Fashion AI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41256.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/41256/fashion-ai/ashish-jobson
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
ieee projects 2012 for cse in networking trichy, ieee projects 2012 for networking chennai, ieee projects 2012 for cse bangalore, ieee projects 2012 for it hyderabad, ieee projects 2012 for me pune, ieee projects 2012 for mca nagpur, ieee projects 2012 for me cse tirupati, ieee projects for cse 2012 titles cochin, ieee projects for cse 2012 free download mysore, ieee projects for cse 2012 hubli, ieee mini projects for cse 2012 vijayawada
The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to use several class specific generators, which limits its application to datasets with a limited number of classes. Instead, we suggest a new Group Decreasing Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting semantic labels to natural images and can produce plausible high quality results for datasets with a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in the SMIS mission. We also demonstrate that GroupDNet can perform a variety of interesting synthesis tasks. Ashish Jobson | Dr. Kamalraj R "Fashion AI Literature" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42378.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42378/fashion-ai-literature/ashish-jobson
Aggregate Computing Platforms: Bridging the GapsRoberto Casadei
This presentation, held in the context of the CS & Eng M.D. course "Pervasive Computing" (Unibo, Cesena), drafts some analysis for an Aggregate Computing platform and suggests areas of investigation.
A Novel Algorithm on Wavelet Based Robust Invisible Digital Image Watermarkin...IJERA Editor
This paper presents a new algorithm on waveletbased robust and invisible digital image watermarking for multimedia security. The proposed algorithm has been designed, implemented and verified using MATLAB R2014a simulation for both embedding and extraction of the watermark and the results of which shows significant improvement in performance metrics like PSNR, SSIM, Mean Correlation, MSE than the other existing algorithms in the current literature. The cover image considered here in our algorithm is of the size (256x256) and the binary watermark image size is taken as (16x16).
A novel cryptographic technique that emphasis visual quality and efficieny by...eSAT Journals
Abstract Visual cryptography is a cryptographic technique which allows visual information to be encrypted in such a way that decryption becomes a mechanical operation that does not require a computer. The original image can be split into shares, where unauthorized person cannot get the data which we hide within that share images. By stacking the two shares, the secret data can be revealed. The highlighted issue in VC is, the size and quality of the reconstructed image should be same as the original image. In this paper, a novel k out of k extended visual cryptography scheme (EVCS) is used, to improve security and to produce meaningful shares. Halftone visual cryptography (VC) encodes a secret image into k halftone meaningful image shares through Floyd Steinberg error diffusion algorithm. The algorithm achieves dithering using error diffusion, meaning it pushes (adds) the residual quantization error of a pixel onto its neighboring pixels, to be dealt with later. This algorithm takes a substantial time for encryption and decryption in a considerably calmer manner. Comparisons with previous approaches show the superior performance of the new method.
Keywords: k out of k, extended visual cryptography, halftone visual cryptography, Floyd Steinberg error diffusion algorithm.
Toward Transparent Coexistence for Multihop Secondary Cognitive Radio Networkskitechsolutions
Ki-Tech Solutions IEEE PROJECTS DEVELOPMENTS WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING IN RAJAPALAYAM, VIRUDHUNAGAR DISTRICTS, AND TAMILNADU. Mail to: kitechsolutions.in@gmail.com
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
A survey on power- efficient Forward Error Correction scheme for Wireless Sen...dbpublications
In this recent year, wireless sensor network are widely being used due to its attractive features. Reliable network in wireless sensor network is very important for a broad range of network. Since wireless sensor network consist of a large number of small sensors which has limited battery, power consumption is an issue. The network lifetime depends on the usage of power resources. In this paper, we proposed forward error correction (FEC) which is error controlling method that adds redundancy to the transmitted data so that the receiver can get errorless data. The results provides high reliability in wireless sensor network.
Recent articles published in VLSI design & Communication SystemsVLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Neural Network Algorithm for Radar Signal RecognitionIJERA Editor
Nowadays, the traditional recognition method could not match the development of radar signals. In this paper, based on fractal theory and Neural Network, a new radar signal recognition algorithm is presented. The relevant point is extracted as the input of neutral network, and then it will recognize and classify the signals. Simulation results show that, this algorithm has a distinguish effect on classification under the condition of low SNR.
Hierarchical deep learning architecture for 10 k objects classificationcsandit
Evolution of visual object recognition architectures based on Convolutional Neural Networks &
Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science.
These architectures extract & learn the real world hierarchical visual features utilizing
supervised & unsupervised learning approaches respectively. Both the approaches yet cannot
scale up realistically to provide recognition for a very large number of objects as high as 10K.
We propose a two level hierarchical deep learning architecture inspired by divide & conquer
principle that decomposes the large scale recognition architecture into root & leaf level model
architectures. Each of the root & leaf level models is trained exclusively to provide superior
results than possible by any 1-level deep learning architecture prevalent today. The proposed
architecture classifies objects in two steps. In the first step the root level model classifies the
object in a high level category. In the second step, the leaf level recognition model for the
recognized high level category is selected among all the leaf models. This leaf level model is
presented with the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or unsupervised learning
approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific
leaf level models. Currently the training of leaf level models is in progress; where we have
trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models.
Also we demonstrate that the validation error of the leaf level models saturates towards the
above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5
error rate for the entire two-level architecture needs to be computed in conjunction with the
error rates of root & all the leaf models. The realization of this two level visual recognition
architecture will greatly enhance the accuracy of the large scale object recognition scenarios
demanded by the use cases as diverse as drone vision, augmented reality, retail, image search
& retrieval, robotic navigation, targeted advertisements etc.
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to use several class specific generators, which limits its application to datasets with a limited number of classes. Instead, we suggest a new Group Decreasing Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting semantic labels to natural images and can produce plausible high quality results for datasets with a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in the SMIS mission. We also demonstrate that GroupDNet can perform a variety of interesting synthesis tasks. Ashish Jobson | Dr. Kamalraj R "Fashion AI Literature" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42378.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42378/fashion-ai-literature/ashish-jobson
Aggregate Computing Platforms: Bridging the GapsRoberto Casadei
This presentation, held in the context of the CS & Eng M.D. course "Pervasive Computing" (Unibo, Cesena), drafts some analysis for an Aggregate Computing platform and suggests areas of investigation.
A Novel Algorithm on Wavelet Based Robust Invisible Digital Image Watermarkin...IJERA Editor
This paper presents a new algorithm on waveletbased robust and invisible digital image watermarking for multimedia security. The proposed algorithm has been designed, implemented and verified using MATLAB R2014a simulation for both embedding and extraction of the watermark and the results of which shows significant improvement in performance metrics like PSNR, SSIM, Mean Correlation, MSE than the other existing algorithms in the current literature. The cover image considered here in our algorithm is of the size (256x256) and the binary watermark image size is taken as (16x16).
A novel cryptographic technique that emphasis visual quality and efficieny by...eSAT Journals
Abstract Visual cryptography is a cryptographic technique which allows visual information to be encrypted in such a way that decryption becomes a mechanical operation that does not require a computer. The original image can be split into shares, where unauthorized person cannot get the data which we hide within that share images. By stacking the two shares, the secret data can be revealed. The highlighted issue in VC is, the size and quality of the reconstructed image should be same as the original image. In this paper, a novel k out of k extended visual cryptography scheme (EVCS) is used, to improve security and to produce meaningful shares. Halftone visual cryptography (VC) encodes a secret image into k halftone meaningful image shares through Floyd Steinberg error diffusion algorithm. The algorithm achieves dithering using error diffusion, meaning it pushes (adds) the residual quantization error of a pixel onto its neighboring pixels, to be dealt with later. This algorithm takes a substantial time for encryption and decryption in a considerably calmer manner. Comparisons with previous approaches show the superior performance of the new method.
Keywords: k out of k, extended visual cryptography, halftone visual cryptography, Floyd Steinberg error diffusion algorithm.
Toward Transparent Coexistence for Multihop Secondary Cognitive Radio Networkskitechsolutions
Ki-Tech Solutions IEEE PROJECTS DEVELOPMENTS WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING IN RAJAPALAYAM, VIRUDHUNAGAR DISTRICTS, AND TAMILNADU. Mail to: kitechsolutions.in@gmail.com
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
A survey on power- efficient Forward Error Correction scheme for Wireless Sen...dbpublications
In this recent year, wireless sensor network are widely being used due to its attractive features. Reliable network in wireless sensor network is very important for a broad range of network. Since wireless sensor network consist of a large number of small sensors which has limited battery, power consumption is an issue. The network lifetime depends on the usage of power resources. In this paper, we proposed forward error correction (FEC) which is error controlling method that adds redundancy to the transmitted data so that the receiver can get errorless data. The results provides high reliability in wireless sensor network.
Recent articles published in VLSI design & Communication SystemsVLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Recognition and Detection of Real-Time Objects Using Unified Network of Faste...dbpublications
Region based proposals regularly depend on the features which are economical prudent derivation schemes. The proposed network includesa Region Proposal Network (RPN) which accepts a picture of any size as input and yields an arrangement of rectangular object recommendations, which includes an objectness score. The RPN is prepared end-to-end to produce great quality object recommendations, which are then utilized by Faster R-CNN for object recognition. Further the trained RPN is additionally converged with Faster R-CNN into a solitary system by sharing their convolutional highlights utilizing the as of late famous wording of neural systems with "attention" techniques and the RPN segment advises the brought together system where to look for the object in input. This strategy empowers a unified, profound learning region based proposals for object detection system. The scholarly RPN additionally enhances area proposition quality and accordingly increases the accuracy in object recognition.
A VNF modeling approach for verification purposesIJECEIAES
Network Function Virtualization (NFV) architectures are emerging to increase networks flexibility. However, this renewed scenario poses new challenges, because virtualized networks, need to be carefully verified before being actually deployed in production environments in order to preserve network coherency (e.g., absence of forwarding loops, preservation of security on network traffic, etc.). Nowadays, model checking tools, SAT solvers, and Theorem Provers are available for formal verification of such properties in virtualized networks. Unfortunately, most of those verification tools accept input descriptions written in specification languages that are difficult to use for people not experienced in formal methods. Also, in order to enable the use of formal verification tools in real scenarios, vendors of Virtual Network Functions (VNFs) should provide abstract mathematical models of their functions, coded in the specific input languages of the verification tools. This process is error-prone, time-consuming, and often outside the VNF developers’ expertise. This paper presents a framework that we designed for automatically extracting verification models starting from a Java-based representation of a given VNF. It comprises a Java library of classes to define VNFs in a more developer-friendly way, and a tool to translate VNF definitions into formal verification models of different verification tools.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.