Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity

2016 ◽  
pp. 64-74 ◽  
Author(s):  
M. Espinilla ◽  
A. Rivera ◽  
M. D. Pérez-Godoy ◽  
J. Medina ◽  
L. Martínez ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Resource Constrained ◽  
Constrained Environments

scholarly journals A low-cost wireless endoscope camera: a preliminary report

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
J. M. Lazarus ◽  
M. Ncube
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Video Transmission ◽  
Low Cost ◽  
Battery Life ◽  
Resource Constrained ◽  
High Definition ◽  
Industry Standard ◽  
Constrained Environments ◽  
The Cost

Abstract Background Technology currently used for surgical endoscopy was developed and is manufactured in high-income economies. The cost of this equipment makes technology transfer to resource constrained environments difficult. We aimed to design an affordable wireless endoscope to aid visualisation during rigid endoscopy and minimally invasive surgery (MIS). The initial prototype aimed to replicate a 4-mm lens used in rigid cystoscopy. Methods Focus was placed on using open-source resources to develop the wireless endoscope to significantly lower the cost and make the device accessible for resource-constrained settings. An off the shelf miniature single-board computer module was used because of its low cost (US$10) and its ability to handle high-definition (720p) video. Open-source Linux software made monitor mode (“hotspot”) wireless video transmission possible. A 1280 × 720 pixel high-definition tube camera was used to generate the video signal. Video is transmitted to a standard laptop computer for display. Bench testing included latency of wireless digital video transmission. Comparison to industry standard wired cameras was made including weight and cost. The battery life was also assessed. Results In comparison with industry standard cystoscope lens, wired camera, video processing unit and light source, the prototype costs substantially less. (US$ 230 vs 28 000). The prototype is light weight (184 g), has no cables tethering and has acceptable battery life (of over 2 h, using a 1200 mAh battery). The camera transmits video wirelessly in near real time with only imperceptible latency of < 200 ms. Image quality is high definition at 30 frames per second. Colour rendering is good, and white balancing is possible. Limitations include the lack of a zoom. Conclusion The novel wireless endoscope camera described here offers equivalent high-definition video at a markedly reduced cost to contemporary industry wired units and could contribute to making minimally invasive surgery possible in resource-constrained environments.

scholarly journals Autonomous monitoring framework for resource-constrained environments

2018 ◽  
Vol 4 (3) ◽  
pp. 137-155 ◽  
Author(s):  
Sajid Nazir ◽  
Hassan Hamdoun ◽  
Fabio Verdicchio ◽  
Gorry Fairhurst
Keyword(s):  
Resource Constrained ◽  
Monitoring Framework ◽  
Constrained Environments

Collaborative caching for efficient dissemination of personalized video streams in resource constrained environments

2013 ◽  
Vol 20 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Suchendra M. Bhandarkar ◽  
Lakshmish Ramaswamy ◽  
Hari K. Devulapally
Keyword(s):  
Video Streams ◽  
Resource Constrained ◽  
Collaborative Caching ◽  
Constrained Environments ◽  
Personalized Video

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

2017 ◽  
Vol 2620 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Yang Carl Lu ◽  
Holly Krambeck ◽  
Liang Tang
Keyword(s):  
Traffic Control ◽  
Time Of Day ◽  
Traffic Signal ◽  
Signal Timing ◽  
Gps Data ◽  
Resource Constrained ◽  
Physical Sensors ◽  
Constrained Environments ◽  
Traffic Signal Timing ◽  
High Level

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

scholarly journals Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Cheng-Jian Lin ◽  
Chun-Hui Lin ◽  
Shyh-Hau Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Model Performance ◽  
Image Sensor ◽  
Model Complexity ◽  
Resource Constrained ◽  
Smart Cameras ◽  
Resource Limited ◽  
Constrained Environments

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.

scholarly journals Reinforcement-Learning-Based IDS for 6LoWPAN

2021 ◽  
Author(s):  
Aryan Mohammadi Pasikhani ◽  
Andrew John Clark ◽  
Prosanta Gope
Keyword(s):  
Reinforcement Learning ◽  
Low Power ◽  
Routing Protocol ◽  
Personal Area Networks ◽  
Wireless Personal Area Networks ◽  
Resource Constrained ◽  
Lossy Networks ◽  
Constrained Environments ◽  
Operational Component

<p>The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.</p><br>

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