A Fast Cattle Recognition System using Smart devices

2016 ◽  
Author(s):  
Santosh Kumar ◽  
Sanjay Kumar Singh ◽  
Tanima Dutta ◽  
Hari Prabhat Gupta
Keyword(s):  
Recognition System ◽  
Smart Devices ◽  
Cattle Recognition

Recent Trends in Pervasive and Ubiquitous Computing

2011 ◽  
pp. 1-43
Author(s):  
Ramesh Singh
Keyword(s):  
Pervasive Computing ◽  
Logic Gates ◽  
Recognition System ◽  
Smart Devices ◽  
Reconfigurable Processors ◽  
The Future ◽  
Field Programmable ◽  
Recent Trends ◽  
Application Specific ◽  
Integrated Circuitry

Pervasive computing is the trend towards increasingly ubiquitous connected computing devices in the environment, a trend being brought about by a convergence of advanced electronic – and particularly, wireless - technologies and the Internet. Pervasive computing devices are not personal computers but very tiny - even invisible - devices, either mobile or embedded in almost any type of object imaginable, including cars, tools, appliances, clothing and various consumer goods – all communicating through increasingly interconnected networks. In the future these smart devices will maintain current information about their locations, the contexts in which they are being used, and relevant data about the users. The goal of researchers is to create a system that is pervasively and unobtrusively embedded in the environment, completely connected, intuitive, effortlessly portable, and constantly available. Among the emerging technologies expected to prevail in the pervasive computing environment of the future are wearable computers, smart homes and smart buildings. Among the myriad of tools expected to support these are: application-specific integrated circuitry (ASIC); speech recognition; gesture recognition; system on a chip (SoC); perceptive interfaces; smart matter; flexible transistors; reconfigurable processors; field programmable logic gates (FPLG); and micro electromechanical systems (MEMS).

scholarly journals An Acoustic-Based Smart Home System for People Suffering from Dementia

Technologies ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 29 ◽  
Author(s):  
Eleni Boumpa ◽  
Anargyros Gkogkidis ◽  
Ioanna Charalampou ◽  
Argyro Ntaliani ◽  
Athanasios Kakarountas ◽  
...  
Keyword(s):  
Smart Home ◽  
Aging In Place ◽  
Recognition System ◽  
Smart Devices ◽  
Home Support ◽  
Person Recognition ◽  
Dementia Syndrome ◽  
Neurological Research ◽  
Familiar Person

Aging-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. Taking into consideration the fact that numerous neurological research results suggest the use of sound as a stimulus for empowering the memory of the sufferer, an innovative information home support system for people suffering from dementia is proposed. The innovation of the proposed system is found in its application, that is to integrate a home system for assisting with person recognition via a sound-based memory aid service. Furthermore, the system addresses the needs of people suffering from dementia to recognize their familiars and have better interaction and collaboration, without the need for training. The system offers a ubiquitous recognition system, using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound is reproduced on the smart speakers, in order to stimulate the sufferer’s memory. The system identified all users and reproduced the appropriate sound in 100% of the cases. To the best of the authors’ knowledge, this is the first system of its kind for assisting person recognition via sound ever reported in the literature.

scholarly journals MyOcrTool: Visualization System for Generating Associative Images of Chinese Characters in Smart Devices

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Laxmisha Rai ◽  
Hong Li
Keyword(s):  
Real Time ◽  
Chinese Text ◽  
Character Recognition ◽  
Recognition System ◽  
Practical Significance ◽  
Smart Devices ◽  
Automatic Identification ◽  
Chinese Characters ◽  
Visualization System ◽  
Writing Chinese

Majority of Chinese characters are pictographic characters with strong associative ability and when a character appears for Chinese readers, they usually associate with the objects, or actions related to the character immediately. Having this background, we propose a system to visualize the simplified Chinese characters, so that developing any skills of either reading or writing Chinese characters is not necessary. Considering the extensive use and application of mobile devices, automatic identification of Chinese characters and display of associative images are made possible in smart devices to facilitate quick overview of a Chinese text. This work is of practical significance considering the research and development of real-time Chinese text recognition, display of associative images and for such users who would like to visualize the text with only images. The proposed Chinese character recognition system and visualization tool is named as MyOcrTool and developed for Android platform. The application recognizes the Chinese characters through OCR engine, and uses the internal voice playback interface to realize the audio functions and display the visual images of Chinese characters in real-time.

scholarly journals A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

2018 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
Abir Alharbi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Character Recognition ◽  
Recognition System ◽  
Smart Devices ◽  
Binary Matrix ◽  
Recognition Systems ◽  
Handwritten Recognition ◽  
Handwritten Arabic

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

A 260µW infrared gesture recognition system-on-chip for smart devices

2016 ◽  
Author(s):  
Sechang Oh ◽  
Ngoc Le Ba ◽  
Suyoung Bang ◽  
Junwon Jeong ◽  
David Blaauw ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
System On Chip ◽  
Smart Devices ◽  
On Chip

3D Gesture Recognition Based on Handheld Smart Terminals

2018 ◽  
Vol 9 (4) ◽  
pp. 96-111 ◽  
Author(s):  
Yunhe Li ◽  
Yi Xie ◽  
Qinyu Zhang
Keyword(s):  
Gesture Recognition ◽  
Template Matching ◽  
Three Dimensional ◽  
Recognition System ◽  
Smart Devices ◽  
Motion Sensors ◽  
Human Computer Interactions ◽  
Computational Load ◽  
Dynamic Template ◽  
Computer Interactions

With the popularity of smart devices, it has become impossible for traditional human-computer interaction techniques to accommodate people's needs. This article proposes an iOS-based three dimensional (3D) gesture recognition system, gathering users' specific gestures from their handheld smart terminals to judge implications of these gestures, so to control other smart terminals with more natural human-computer interactions. In this article, gestures were recognized by reading data about corresponding 3D gesture data with motion sensors of smart terminals using optimized dynamic time warping (DTW) algorithm. As to this algorithm, curve paths were delimited via slope based on features of mobile devices and dynamic programming. Meanwhile, this algorithm reduced computational load for template matching and costs of gesture recognition by preliminarily storing upper and lower boundaries of delimited areas with linked lists or setting distortion thresholds. In this article, efficiency and precision of recognition schemes were tested and verified on cellphones. The results suggested that the improved algorithm was less time-consuming than classical algorithms, and required less time for computational load for template matching. Furthermore, it was demonstrated that the gesture recognition based on dynamic template matching algorithms, with higher recognition efficiency and precision, could bring better experiences of human-computer interactions.

scholarly journals Comparative Study of Speaker Recognition Techniques in IoT Devices for Text Independent Negative Recognition

2020 ◽  
Vol 21 (3) ◽  
pp. 359-368
Author(s):  
Pooja Jain ◽  
Neha R Kasture ◽  
Tapan Kumar
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Recognition System ◽  
Smart Devices ◽  
Smart Environments ◽  
Iot Devices ◽  
Human Hearing ◽  
Commercial Applications ◽  
Home Industries

Speaker recognition (SR) or identification is the subset of broad area of Pattern recognition. Given the features of the voice print, the recognition system identifies the speaker from the knowledge of the speaker models stored in the database. In today’s world when many of our works are done through voice, recognition of the speaker is necessary.Recently, SR has also gained importance in Internet of Things (IoT) like setting up of smart environments for home, industries or educational and commercial applications. The race for high accuracy needs making the devices used in these smart environments as close to human hearing capacity as possible. Speaker identification is mostly used to establish negative recognition. Negative recognition is when thesystem decides whether a person is who he disagrees to be thus preventing a person from exploiting multiple identities. Only biometrics will be suitable to establish such identification. The feature extraction of voice sample along with comparative analysis of its methods is of fundamental interest in this paper. We try to compare the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices.

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