Far-infrared contraband-detection-system development for personnel-search applications

Abstract Forest and land fires are a disaster that occurs almost every year on Sumatra Island. Ogan Komering Ilir is one of the regencies in South Sumatra Province with a high number of hotspots causing forest and land fires every year. Prevention efforts are important to reduce the impact caused by forest and land fires on various aspects of life. One of them is by building a web-based Geographic Information System (WebGIS) for the distribution of hotspots as a form of early warning and detection system by utilizing VIIRS Nightfire (VNF) data from the remote sensing technology of the Suomi-NPP satellite which has Visible Infrared Imaging Radiometer Suite (VIIRS) active sensors which have been processed with the Nightfire algorithm. The Leaflet JavaScript library plays an important role in adding to the functionality of WebGIS with a wide selection of available plugins and easy-to-read source code to make web-based spatial information more interactive. The prototype of WebGIS with the name OKIApi has been successfully developed and has several key features such as displaying information on the distribution of hotspots that have been classified by temperature; the priority level of firefighting areas and the vulnerability level of flammable areas based on the type of land cover; route to the hotspot or the fire department locations; a chart of the estimated burned area from the source footprint of hotspot; and a chart of the number of hotspots per day that have been classified by temperature. The percentage value of the web feasibility for the functionality test to 13 WebGIS features is 100% with a very good predicate, the usability test is 91.5% with a very good predicate, and the portability test on 18 web browsers applications is 100% with a very good predicate.

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SBC-Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3368.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4605-4613

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Learning Algorithm ◽

Detection System ◽

System Development ◽

Deep Convolutional Neural Network ◽

Raspberry Pi ◽

Significant Difference ◽

Cataract Patients

This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through GoogLeNet Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients

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Design and Development of On-Board Earth Resources Detection System Based on FPGA and LabVIEW for Remote Sensing Application

10.29007/4q63 ◽

2018 ◽

Author(s):

Alpesh Vala ◽

Riddhi Goswami ◽

Amit Patel ◽

Keyur Mahant

Keyword(s):

Detection System ◽

System Development ◽

Detection Algorithm ◽

Memory Storage ◽

Satellite Imaging ◽

High Speeds ◽

Ground System ◽

Imaging Sensors ◽

Mass Volume ◽

Water Vegetation

This paper presents system development for the detection of earth resources such as a water, vegetation and land for the satellite application. Satellite imaging sensors generate mass volume of data at very high speeds. On the other hand, storage capacity and communication bandwidth are crucial parameters for satellite resources. Here we have proposed the system that can be used on board to extract relative information from the image and can send out the required (obtained) results to the ground system (Result of pixel information whether it contains water/vegetation/land). The system can be used for the saving of on board satellite resources such as memory storage, power and communication bandwidth. The detection of earth resources are based on their reflectance value. For the analysis of proposed detection algorithm LabVIEW based simulation has been carried out for the detection of Land, Water and vegetation from their reflectance value. Same algorithm has implemented in FPGA for the real time implementation using SPARTAN XC3S500e-4vq100 FPGA board. The results are accurate and matched with the simulation results performed in LabVIEW.

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A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study

JMIR mhealth and uhealth ◽

10.2196/20625 ◽

2020 ◽

Vol 8 (12) ◽

pp. e20625

Author(s):

Mehrab Bin Morshed ◽

Samruddhi Shreeram Kulkarni ◽

Richard Li ◽

Koustuv Saha ◽

Leah Galante Roper ◽

...

Keyword(s):

Eating Behavior ◽

Detection System ◽

System Development ◽

Contextual Factors ◽

Well Being ◽

Survey Study ◽

Dominant Hand ◽

Contextual Data ◽

The Individual ◽

Ecological Momentary

Background Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. Objective The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. Methods The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. Results Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. Conclusions The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

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The Automatic Detection System Development of Aluminum Production Line Quality

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.525 ◽

2013 ◽

Vol 462-463 ◽

pp. 525-528

Author(s):

Guang Yu Li ◽

Liang Hou

Keyword(s):

Production Efficiency ◽

Detection System ◽

System Development ◽

Automatic Detection ◽

Recognition Algorithm ◽

Working Environment ◽

Aluminum Surface ◽

Aluminum Production ◽

Automatic Detection System

Aluminum electrolytic capacitors in the production process, in order to ensure product quality, reduce consumption, should happen quickly detect aluminum surface quality of this paper, digital image processing technology as the core, collecting images in different directions on the computer using a variety of assistance and recognition algorithm to achieve the automatic detection of defects in aluminum. The system will greatly improve production efficiency, reduce labor intensity and improve the working environment of the scene.

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