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DSCGGV / repository
This is the primary repository for hosting projects under the Android/Web club.
The Android/Web Club is a technical organization of students in the campus of Guru Ghasidas Vishwavidyalaya, that works under the parent community namely Developer Student Clubs
Web development refers to building, creating, and maintaining websites. It includes aspects such as web design, web publishing, web programming, and database management. Web developers blend creative vision with the project management skills to plan, design and build websites and integrated applications.
Android software development is the process by which applications are created for devices running the Android operating system. Different programming languages like Java, Kotlin, Dart and SDK tools like Flutter etc are used to prepare end to end applications that can solve different problems in the society. It aims at building websites and developing android apps, rather than that members here will focus on blending creative vision with the project management skills to plan, design and build websites and integrated applications which solve real world problems.
Hey Guys ❤, we are going to conduct the Hacktober Fest-2020. The entire contribution guidelines can be found here - Hacktober fest contributions
Selected from shared topics, language and repository description—not editorial ratings.
zhitianz /
This android application is used to communicate between a computer (python server) and a DJI drone using DJI mobile SDK. So that the DJI drone can be controlled by code instead of remote controller.
Authentication is a significant issue in system control in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This project describes a method for Student’s Attendance System which will integrate with the face recognition technology using deep learning algorithms. The system will recognize the students present in the classroom and provide the list of present students for the lecture. The primary technique used for the face detection is by using python inbuilt packages of OpenCV. Once the model is trained on different kinds of datasets, the project will help in identifying students present for the class. The front end will be based on an android application. The application uses SQLite database for establishing connection between web app and the model. The backend model mainly comprises of a convolutional neural network which extracts features and trains the model in recognizing those features. The inbuilt OpenCV uses haarcascade classifiers in identifying the faces present in the input image. The list of identified will be displayed as the end result.
iSpeech's open source android SDK for speech recognition (ASR) API and text to speech (TTS) API, enables you to easily create android applications using iSpeech freeform, command or custom statistical language models. The speech recognition API powering this speech recognition SDK supports nearly 30 languages and accents. The acoustic models are based on huge amounts of low and high quality hand labeled audio data (millions of utterances). The text to speech API powering this text to speech SDK supports nearly 30 languages and accents, with many languages having multiple voices. Currently this SDK has been tested and works on Samsung Galaxy, LG G4, Moto X, HTC One, Nexus 6, and supports devices using API level 10 or greater. *you may need to contact iSpeech to create custom models or to create or access custom text to speech voices or celebrity text to speech voices **if you do not have an iSpeech account and need to test, you may request credits *** iOS, JavaScript, Java, .NET, Python and other SDKs are also available
anishomsy /
AutoReVanced is a Python script designed to automate the process of downloading and patching APKs (Android application packages) with ReVanced patches. This tool simplifies the process of fetching the necessary files, downloading the desired APK, and applying patches to create a customized APK.
It's a project combined with hardware and software, the goal is to make a smart watch based on esp8266 chip. The smart watch has so many features such as time display, alarm, brightness adjustment, text scrolling, weather display, voice control and gesture recognition:. The first five features time display, alarm alert, brightness adjustment and text scrolling are based on the local programming. The basic idea for these is to check the document for esp8266, getting to know how to set up the circuit, how to get the real time, how to control the OLED screen, bus, light sensors etc. The last three features mainly depends on the cloud coding, and are all based on the Client/server architecture. For Weather Display, esp8266 severed as a client. A connection was built by Google Geolocation API between the smart watch(client) and Google Weather Service Server(server). Send the JSON string including location information to the server and get the weather information back. For Voice Control, esp8266 was configured as a server by ngrok and an Android application was used as the client. This app aims to transfer voice signal into text commands by Google Speech API. Connected the app(client) to the smart watch(server) by HTTP request, and used translated commands to control the smart watch. For gesture recognition, esp8266 was client and server was the EC2 Linux instance set up in AWS. In order to teach the smart watch to identify gestures, both the training and predicting process needs to be done. Mongodb is used to keep the large amount of data from accelerometer training. SVM model is selected to do the training, the data for the same gesture was labeled. After training, if someone is holding the smart watch and moving it in some preset gestures, the smart watch could map these data from the accelerometer into a specific gesture. The final recognition accuracy is 98%.
RahulDevadiga /
It is often required to send images captured by the mobile camera to a device like laptop or Raspberry pi for further processing of images. Now a days, android mobiles have a very high quality camera at a very low price. Such images can be made use of for processing of images in real time. This project consists of an Android application which captures the image in real time and then converts into a base64 string which is then sent to a device to which connection is established. The receiving device is chosen by the user from a list of devices displayed. It also consist of Python code which receives the base64 string decodes it and displays the image. Once connection is established, the user may send any number of images. Further the transmission time can be reduced by compromising on quality or using a lossless compression techniques.