• Biomedical Science Letters
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• v.17 no.3
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• pp.191-196
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• 2011

Noroviruses (noroV) are the major cause of nonbacterial gastroenteritis in humans worldwide. Since noroV cannot yet be cultured in vitro and their diagnosis by electron microscopy requires at least $10^6$ viral particles/g of stool a variety of molecular detection techniques represent an important step towards the detection of noroV. In the present study, we have applied real-time nucleic acid sequence-based amplification (real-time NASBA) for simultaneous detection of NoroV genogroup I (GI) and genogroup II (GII) using standard viral RNA. For real-time NASBA assay which can detected noroV GI and GII, a selective region of the genes encoding the capsid protein was used to design primers and genotype-specific molecular beacon probes. The specificity of the real-time NASBA using newly designed primers and probes were confirmed using standard viral RNA of noroV GI and GII. To determine the sensitivity of this assay, serial 10-fold dilutions of standard viral RNA of noroV GI and GII were used for reverse transcription polymerase chain reaction (RT-PCR) and real-time NASBA. The results showed that while agarose gel electrophoresis could detect RT-PCR products with 10 pg of standard viral RNA, the real-time NASBA assay could detect 100 fg of standard viral RNA. These results suggested that the real-time NASBA assay has much higher sensitivity than conventional RT-PCR assay. This assay was expected that might detect the viral RNA in the specimens which could have been false negative by RT-PCR. There were needed to perform real-time NASBA with clinical specimens for evaluating accurate sensitivity and specificity of this assay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• Korean Journal of Microbiology
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• v.47 no.1
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• pp.87-91
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• 2011

The aim of this study was to compare the performance of conventional culture and real-time PCR for detection of Cronobacter spp. in powdered foods. Infant formula, baby food and Misugaru inoculated with Cronobacter were enriched in distilled water as first enrichment step, followed by incubating in Enterobacteriaceae enrichment (EE) broth as second enrichment step. A loopful of enriched sample was streaked onto Druggan-Forsythe-Iversen agar, followed by incubating at $37^{\circ}C$ for 24 h. One milliliter of the enriched distilled water and EE broth were used in real-time PCR assay. No statistical differences were observed in the number of positive samples between culture method and real-time PCR (p>0.05) in all types of food samples. The number of positives of real-time PCR was higher in the first enrichment media (distilled water) than the second enrichment media (EE broth), though there was no significant difference (p>0.05). It appears that some components of the second enrichment broth, EE broth, inhibit the reaction of real-time PCR. These results show that real-time PCR using a single enrichment with distilled water could be useful as an effective screening method for detection of Cronobacter while saving much time and labor compared to conventional culture method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.113-121
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• 2012

In this study, a real-time fall detection system based on a smartphone equipped with three-axis accelerometer and magnetometer was proposed and evaluated. The proposed system provides a service that detects falls in real time, triggers alarm sound, and sends emergency SMS(Short Message Service) if the alarm is not deactivated within a predefined time. When both of the acceleration magnitude and angle displacement of the smartphone attached to waist belt are greater than predefined thresholds, it is detected as a fall. The proposed system was evaluated against activities of daily living(walking, jogging, sitting down, standing up, ascending stairs, and descending stairs) and unintended falls induced by a proprietary pneumatic-powered mattress. With the thresholds of acceleration magnitude 1.7g and angle displacement $80^{\circ}$, it showed 96.5% accuracy to detect the falls while all the activities of daily living were not detected as fall.

• Information and Communications Magazine
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• v.10 no.10
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• pp.22-29
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• 1993

This paper describes development of a real-time voice recognition dialing system which can recognize around one hundred word vocabularies in speaker independent mode. The voice recognition algorithm is implemented on a DSP board with a telephone interface plugged in an IBM PC AT/486. In the DSP board, procedures for feature extraction, vector quantization(VQ), and end-point detection are performed simultaneously in every 10msec frame interval to satisfy real-time constraints after the word starting point detection. In addition, we optimize the VQ codebook size and the end-point detection procedure to reduce recognition time and memory requirement. The demonstration system is being displayed in MOBILAB of Korea Mobile Telecom at the Taejon EXPO '93.

• Korean Journal of Veterinary Research
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• v.53 no.1
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• pp.25-28
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• 2013

The aim of this study was to applicate and evaluate a SYBR Green real-time PCR for the specific detection of Salmonella spp. Specificity of the PCR method was confirmed with 48 Salmonella spp. and 5 non-Salmonella strains using invA gene primer. The average threshold cycle ($C_T$) of Salmonella spp. was $11.83{\pm}0.78$ while non-Salmonella spp. was $30.86{\pm}1.19$. Correlation coefficients of standard curves constructed using $C_T$ versus copy number of Salmonella Enteritidis ATCC 13076 showed good linearity ($R^2=0.993$; slope = 3.563). Minimum level of detection with the method was > $10^2$ colony forming units (CFU)/mL. These results suggested that the SYBR Green real-time PCR might be applicable for the specific detection of Salmonella spp. isolates.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4120-4132
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• 2017

In this research, we implement a deformable object simulation system using OpenGL's shader language, GLSL4.3. Deformable object simulation is implemented by using volumetric mass-spring system suitable for real-time simulation among the methods of deformable object simulation. The compute shader in GLSL 4.3 which helps to access the GPU resources, is used to parallelize the operations of existing deformable object simulation systems. The proposed system is implemented using a compute shader for parallel processing and it includes a bounding box-based collision detection solution. In general, the collision detection is one of severe computing bottlenecks in simulation of multiple deformable objects. In order to validate an efficiency of the system, we performed the experiments using the 3D volumetric objects. We compared the performance of multiple deformable object simulations between CPU and GPU to analyze the effectiveness of parallel processing using GLSL. Moreover, we measured the computation time of bounding box-based collision detection to show that collision detection can be processed in real-time. The experiments using 3D volumetric models with 10K faces showed the GPU-based parallel simulation improves performance by 98% over the CPU-based simulation, and the overall steps including collision detection and rendering could be processed in real-time frame rate of 218.11 FPS.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.