• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.55-60
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• 2020

In this paper, we propose a fast block matching algorithm of motion estimation using early detection of optimal candidate with high priority and a threshold. Even though so many fast algorithms for motion estimation have been published to reduce computational reduction full search algorithm, still so many works to improve performance of motion estimation are being reported. The proposed algorithm calculates block matching error for each candidate with high priority from previous partial matching error. The proposed algorithm can be applied additionally to most of conventional fast block matching algorithms for more speed up. By doing that, we can find the minimum error point early and get speed up by reducing unnecessary computations of impossible candidates. The proposed algorithm uses smaller computation than conventional fast full search algorithms with the same prediction quality as the full search algorithm. Experimental results shows that the proposed algorithm reduces 30~70% compared with the computation of the PDE and full search algorithms without any degradation of prediction quality and further reduces it with other fast lossy algorithms.

• Journal of Communications and Networks
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• v.3 no.4
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• pp.334-341
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• 2001

The provision of QoS control is a key of the successful deployment of multimedia networks. Buffer management plays an important role in QoS control. Therefore, this paper proposes a novel QoS buffer management algorithm named GREEN (Global Random Early Estimation for Nipping), which extends the concepts of ERD (early random drop) and RED (random early detection). Specifically, GREEN enhances the concept of "Random" to "Global Random" by globally considering the random probability function. It also enhances the concept of "Early" to "Early Esti mation" by early estimating the network status. For performance evaluation, except compared with RED, extensive simulation cases are performed to probe the characteristics of GREEN.

• Journal of Sensor Science and Technology
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• v.23 no.5
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• pp.342-348
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• 2014

This paper introduced a novel multi-gas sensor detector with simple signal processing algorithm. This device was evaluated by investigating the characteristics of combustible materials using fire-generated smell and smoke. Plural sensors including TGS821, TGS2442, and TGS260X were equipped to detect carbon monoxide, hydrogen gas, and gaseous air contaminants which exist in cigarette smoke, respectively. Signal processing algorithm based on the difference of response times in fire-generated gases was implemented with early and accurately fire detection from multiple gas sensing signals. All fire experiments were performed in a virtual fire chamber. The cigarette, cotton fiber, hair, polyester fiber, nylon fiber, paper, and bread were used as a combustible material. This analyzing software and sensor controlling algorithm were embedded into 8-bit micro-controller. Also the detected multiple gas sensor signals were simultaneously transferred to the personnel computer. The results showed that the air pollution detecting sensor could be used as an efficient sensor for a fire detector which showed high sensitivity in volatile organic compounds. The proposed detecting algorithm may give more information to us compared to the conventional method for determining a threshold value. A fire detecting device with a multi-sensor is likely to be a practical and commercial technology, which can be used for domestic and office environment as well as has a comparatively low cost and high efficiency compared to the conventional device.

• Journal of Sensor Science and Technology
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• v.22 no.5
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• pp.338-345
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• 2013

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and the prevention of possible life threatening cardiac diseases. Most methods for detecting arrhythmia require pp interval, or the diversity of P wave morphology, but they are difficult to detect the p wave signal because of various noise types. Thus, it is necessary to use noise-free R wave. So, the new approach for the detection of PVC is presented based on the rhythm analysis and the beat matching in this paper. For this purpose, we removed baseline wandering of low frequency band and made summed signals that are composed of two high frequency bands including the frequency component of QRS complex using the wavelet filter. And then we designed R wave detection algorithm using the adaptive threshold and window through RR interval. Also, we developed algorithm to classify PVC using RR interval. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate average detection rate of 99.76%, sensitivity of 99.30% and specificity of 98.66%; accuracy respectively for R wave and PVC detection.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.275-277
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• 2018

Recently, the importance of an early response has been emphasized due to the large fire. The most efficient method of extinguishing a large fire is early response to a small flame. To implement this solution, we propose a fire detection mechanism based on a deep learning artificial intelligence. In this study, a small amount of data sets is manipulated by an image augmentation technique using rotating, tilting, blurring, and distorting effects in order to increase the number of the data sets by 5 times, and we study the flame detection algorithm using faster R-CNN.

• ETRI Journal
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• v.27 no.5
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• pp.639-642
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• 2005

This paper proposes two kinds of complexity-reduced algorithms for a low density parity check (LDPC) decoder. First, sequential decoding using a partial group is proposed. It has the same hardware complexity and requires a fewer number of iterations with little performance loss. The amount of performance loss can be determined by the designer, based on a tradeoff with the desired reduction in complexity. Second, an early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Once the edges are detected, no further iteration is required; thus early detection reduces the computational complexity.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3651-3658
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• 2016

Computer-aided diagnosis of breast cancer is an important medical approach. In this research paper, we focus on combining two major methodologies, namely fuzzy base systems and the evolutionary genetic algorithms and on applying them to the Saudi Arabian breast cancer diagnosis database, to aid physicians in obtaining an early-computerized diagnosis and hence prevent the development of cancer through identification and removal or treatment of premalignant abnormalities; early detection can also improve survival and decrease mortality by detecting cancer at an early stage when treatment is more effective. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized systems that attain high classification performance, with simple and readily interpreted rules and with a good degree of confidence.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4C
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• pp.308-315
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• 2002

Guaranteed frame rate (GFR) service has been defied to provide minimum cell rate (MCR) guarantees for virtual connections (VCs) carrying Internet traffic in ATM networks and allow them to fairly share residual bandwidth. The simplest switch implementation mechanism to support the GFR service in ATM networks consists of the frame-based generic cell rate algorithm (F-GCRA) frame classifier and the early packet discard (EPD)-like buffer acceptance algorithm in a single FIFO buffer. This mechanism is simple, but has foiled to guarantee the same bandwidth as an MCR to a VC that has reserved a relatively large MCR. This paper applies the packet spacing scheme to TCP traffic to alleviate its burstness, so as to guarantee a larger MCR to a VC. In addition, the random early detection (RED) scheme is added to the buffer acceptance algorithm in order to improve fairness in use of residual bandwidth. Simulation results show that the applied two schemes improve a quality of service (QoS) in the GFR service for the TCP traffic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4342-4366
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• 2016

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur'e type system. In the context of delayed Lur'e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.791-797
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• 2016

Background: Punjab state is particularly reporting a rising burden of cancer. A 'door to door cancer awareness and early detection campaign' was therefore launched in the Punjab covering about 2.67 million population, wherein after initial training accredited social health activists (ASHAs) and other health staff conducted a survey for early detection of cancer cases based on a twelve point clinical algorithm. Objective: To ascertain unit cost for undertaking a population-based cancer awareness and early detection campaign. Materials and Methods: Data were collected using bottom-up costing methods. Full economic costs of implementing the campaign from the health system perspective were calculated. Options to meet the likely demand for project activities were further evaluated to examine their worth from the point of view of long-term sustainability. Results: The campaign covered 97% of the state population. A total of 24,659 cases were suspected to have cancer and were referred to health facilities. At the state level, incidence and prevalence of cancer were found to be 90 and 216 per 100,000, respectively. Full economic cost of implementing the campaign in pilot district was USD 117,524. However, the financial cost was approximately USD 6,301. Start-up phase of campaign was more resource intensive (63% of total) than the implementation phase. The economic cost per person contacted and suspected by clinical algorithm was found to be USD 0.20 and USD 40 respectively. Cost per confirmed case under the campaign was 7,043 USD. Conclusions: The campaign was able to screen a reasonably large population. High to high economic cost points towards the fact that the opportunity cost of campaign put a significant burden on health system and other programs. However, generating awareness and early detection strategy adopted in this campaign seems promising in light of fact that organized screening is not in place in India and in many developing countries.