A low-cost architecture design with efficient data arrangement and memory configuration for MPEG-2 audio decoder

2002 ◽  
Author(s):  
Tsung-Han Tsai ◽  
Liang-Gee Chen ◽  
Sheng-Chieh Huang ◽  
Hao-Chieh Chang
Keyword(s):  
Low Cost ◽  
Architecture Design ◽  
Efficient Data ◽  
Data Arrangement ◽  
Audio Decoder

A novel MPEG-2 audio decoder with efficient data arrangement and memory configuration

1997 ◽  
Vol 43 (3) ◽  
pp. 598-604 ◽  
Author(s):  
Tsung-Han Tsai ◽  
Liang-Gee Chen ◽  
Yuan-Chen Liu
Keyword(s):  
Efficient Data ◽  
Data Arrangement ◽  
Audio Decoder

A Novel MPEG-2 Audio Decoder With Efficient Data Arrangement And Memory Configuration

1997 ◽  
Author(s):  
Tsung-Han Tsai ◽  
Liang-Gee Chen ◽  
Yuan-Chen Liu ◽  
Yeong-Kang Lai ◽  
Po-Cheng Wu
Keyword(s):  
Efficient Data ◽  
Data Arrangement ◽  
Audio Decoder

IoT based e-Healthcare System to Enable Remote Patient Care based on Cloud Server

2017 ◽  
Vol 7 (7) ◽  
pp. 36
Author(s):  
P.Venu Gopala Rao ◽  
Eslavath Raja ◽  
Ramakrishna Gandi ◽  
G. Ravi Kumar
Keyword(s):  
Low Cost ◽  
Vital Signs ◽  
Smart Phone ◽  
Medical Professional ◽  
Design Parameters ◽  
Cloud Server ◽  
Significant Area ◽  
Efficient Data ◽  
System Data ◽  
Remote Patient

IoT (Internet of Things) has become most significant area of research to design an efficient data enabled services with the help of sensors. In this paper, a low-cost system design for e-healthcare service to process the sensitive health data is presented. Vital signs of the human body are measured from the patient location and shared with a registered medical professional for consultation. Temperature and heart rate are the major signals obtained from a patient for the initial build of the system. Data is sent to a cloud server where processing and analysis is provided for the medical professional to analyze. Secure transmission and dissemination of data through the cloud server is provided with an authentication system and the patient could be able to track his data through a smart phone on connecting to the cloud server. A prototype of the system along with its design parameters has been discussed.

Building Sensor Grid Architecture for Large-Scale Air Pollution Data Management

2013 ◽  
Vol 831 ◽  
pp. 276-281
Author(s):  
Ya Jie Ma ◽  
Zhi Jian Mei ◽  
Xiang Chuan Tian
Keyword(s):  
Air Pollution ◽  
Data Management ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Physical World ◽  
Sensor Nodes ◽  
Grid Architecture ◽  
Autonomous Sensor ◽  
Efficient Data

Large-scale sensor networks are systems that a large number of high-throughput autonomous sensor nodes are distributed over wide areas. Much attention has paid to provide efficient data management in such systems. Sensor grid provides low cost and high performance computing to physical world data perceived through sensors. This article analyses the real-time sensor grid challenges on large-scale air pollution data management. A sensor grid architecture for pollution data management is proposed. The processing of the service-oriented grid management is described in psuedocode. A simulation experiment investigates the performance of the data management for such a system.

scholarly journals Baby’s Online Live Database: An Open Platform for Developmental Science

2021 ◽  
Vol 12 ◽  
Author(s):  
Masaharu Kato ◽  
Hirokazu Doi ◽  
Xianwei Meng ◽  
Taro Murakami ◽  
Sachiyo Kajikawa ◽  
...  
Keyword(s):  
Low Cost ◽  
Science Research ◽  
Developmental Studies ◽  
Participant Recruitment ◽  
Open Platform ◽  
Developmental Science ◽  
Gender And Age ◽  
Efficient Data ◽  
Near Future ◽  
Access Data

Efficient data collection in developmental studies is facing challenges due to the decreased birth rates in many regions, reproducibility problems in psychology research, and the COVID-19 pandemic. Here, we propose a novel platform for online developmental science research, the Baby’s Online Live Database (BOLD), which extends the scope of the accessible participant pool, simplifies its management, and enables participant recruitment for longitudinal studies. Through BOLD, researchers can conduct online recruitment of participants preregistered to BOLD simply by specifying their attributes, such as gender and age, and direct the participants to dedicated webpages for each study. Moreover, BOLD handles participant recruitment and reward payment, thereby freeing researchers from the labor of participant management. BOLD also allows researchers the opportunity to access data that were collected from participants in previous research studies. This enables researchers to carry out longitudinal analyses at a relatively low cost. To make BOLD widely accessible, a consortium was formed within the Japan Society of Baby Science, where members from diverse research groups discussed the blueprint of this system. Once in full-scaled operation, BOLD is expected to serve as a platform for various types of online studies and facilitate international collaboration among developmental scientists in the near future.

scholarly journals A Survey on Intelligent Agricultural Information Handling Methodologies

2019 ◽  
Vol 11 (12) ◽  
pp. 3278 ◽  
Author(s):  
Yorghos Voutos ◽  
Phivos Mylonas ◽  
John Katheniotis ◽  
Anastasia Sofou
Keyword(s):  
Information Technologies ◽  
Production Systems ◽  
Low Cost ◽  
Small Scale ◽  
Research Knowledge ◽  
Current State ◽  
Smart Farming ◽  
Efficient Data ◽  
Art Research ◽  
The Internet Of Things

The term intelligent agriculture, or smart farming, typically involves the incorporation of computer science and information technologies into the traditional notion of farming. The latter utilizes plain machinery and equipment used for many decades and the only significant improvement made over the years has been the introduction of automation in the process. Still, at the beginning of the new century, there are ways and room for further vast improvements. More specifically, the low cost of rather advanced sensors and small-scale devices, now even connected to the Internet of Things (IoT), allowed them to be introduced in the process and used within agricultural production systems. New and emerging technologies and methodologies, like the utilization of cheap network storage, are expected to advance this development. In this sense, the main goals of this paper may be summarized as follows: (a) To identify, group, and acknowledge the current state-of-the-art research knowledge about intelligent agriculture approaches, (b) to categorize them according to meaningful data sources categories, and (c) to describe current efficient data processing and utilization aspects from the perspective of the main trends in the field.

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