Atiqul Islam
Cloud Research: Dapr on Azure with Bicep
Dapr (Distributed Application Runtime) is an open-source, event-driven, portable runtime for building distributed applications. It provides a set of building blocks that can be used to build cloud-native, microservices-based applications. By using Dapr, developers can focus on writing business logic and let Dapr handle the complexities of building distributed systems. Github Source code of each Dapr Building Block describes the process of deploying Dapr application with particular building block to Azure Container Apps using Bicep, including the use of the Azure Developer CLI and the provisioning of necessary Azure resources, with support for multiple environments.
Github Source codes will be available Soon
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SecretsList Item 1
Manages secrets and sensitive information securely. Provides an abstraction on top of a set of secrets stores, allowing developers to handle secrets without storing them in the application code or system environment variables.
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State ManagementList Item 2
Enables your application to save, read, and query key/value pairs in supported state stores. Provides stateful, long-running applications with the ability to build stateful applications that save and retrieve data.
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Pub/SubList Item 3
Facilitates communication between applications using a publish-subscribe pattern. Supports Azure Event Hubs as a pub-sub component, allowing for event-driven architecture and asynchronous messaging.
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Actors
Encapsulates state and behavior in a single unit. Provides a way to model and implement concurrent, distributed, and event-driven systems.
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Service Invocation
Allows your application to reliably communicate with other applications using HTTP or gRPC. Acts as a reverse proxy that comes with service discovery, access control, metrics, retries, and more.
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Bindings
Integrates your application with various external services, such as Azure Functions, using bindings. Supports Dapr trigger, Dapr service invocation trigger, Dapr topic trigger, and more.
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Observability
Enhances the visibility and understanding of your application's behavior. Provides metrics, logs, and traces to help you monitor and troubleshoot your application.
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Cron Job
Cron jobs are a powerful feature provided by the Dapr framework that allow you to run scheduled tasks at regular intervals. Dapr's Cron binding makes it easy to set up and manage these scheduled jobs in your applications.
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Middleware
Allows you to extend the functionality of Dapr by adding custom middleware components, such as authentication, authorization, and rate limiting.
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Workflows
Supports the execution of long-running, multi-step processes. Enables the orchestration of multiple services and components to achieve complex business logic.
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Csv Parser
Parses CSV files and provides a data structure that can be used in your application. Simplifies the process of reading and manipulating CSV data.
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Image Processor
Processes images and provides image-related functionality. Can be used to manipulate, resize, or transform images in your application.
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Site Scraping
Extracts data from websites. Allows your application to scrape websites and retrieve information for further processing.
Computer Vision and Artificial Neural Networks: Automated Textile Detect Recognition
List of Services
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Automated Textile Detect Recognition System using Computer Vision and Artificial Neural Networks
Md. Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin. “Automated Textile Detect Recognition System using Computer Vision and Artificial Neural Networks''. Enformatika, Transactions on Engineering, Computing and Technology, ISSN 1305-5313, ISBN: 975-00803-2-7, Budapest, Hungary, May 26-28, 2006
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A Suitable Neural Network to Detect Textile Defects.
Md. Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin, M. Ashraful Amin. “A Suitable Neural Network to Detect Textile Defects.” ICONIP 2006, The 13th International Conference on Neural Information Processing, Hong Kong Convention and Exhibition Centre, Hong Kong, October 3-6, 2006
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Automated System to Detect Textile Defects.List Item 1
Md. Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin, M. Ashraful Amin. “Automated System to Detect Textile Defects.” American Journal of Science and Engineering (AJSE), Volume 5, No. 1, August 2006, pp.71-75