• BMB Reports
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• v.48 no.1
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• pp.6-12
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• 2015

Various biological molecules naturally existing in diversified species including fungi, bacteria, and bacteriophage have functionalities for DNA binding and processing. The biological molecules have been recently actively engineered for use in customized genome editing of mammalian cells as the molecule-encoding DNA sequence information and the underlying mechanisms how the molecules work are unveiled. Excitingly, multiple novel methods based on the newly constructed artificial molecular tools have enabled modifications of specific endogenous genetic elements in the genome context at efficiencies that are much higher than that of the conventional homologous recombination based methods. This minireview introduces the most recently spotlighted molecular genome engineering tools with their key features and ongoing modifications for better performance. Such ongoing efforts have mainly focused on the removal of the inherent DNA sequence recognition rigidity from the original molecular platforms, the addition of newly tailored targeting functions into the engineered molecules, and the enhancement of their targeting specificity. Effective targeted genome engineering of mammalian cells will enable not only sophisticated genetic studies in the context of the genome, but also widely-applicable universal therapeutics based on the pinpointing and correction of the disease-causing genetic elements within the genome in the near future.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.4
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• pp.311-321
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• 2022

The report reviews recent research efforts in demonstrating a computing system whose operation principle mimics the dynamics of biological neurons. The temporal variation of the membrane potential of neurons is one of the key features that contribute to the information processing in the brain. We first summarize the neuron models that explain the experimentally observed change in the membrane potential. The function of ion channels is briefly introduced to understand such change from the molecular viewpoint. Dedicated circuits that can simulate the neuronal dynamics have been developed to reproduce the charging and discharging dynamics of neurons depending on the input ionic current from presynaptic neurons. Key elements include volatile memristors that can undergo volatile resistance switching depending on the voltage bias. This behavior called the threshold switching has been utilized to reproduce the spikes observed in the biological neurons. Various types of threshold switch have been applied in a different configuration in the hardware demonstration of neurons. Recent studies revealed that the memristor-based circuits could provide energy and space efficient options for the demonstration of neurons using the innate physical properties of materials compared to the options demonstrated with the conventional complementary metal-oxide-semiconductors (CMOS).

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.62-74
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• 2023

In response to diverse external stimuli, sensory receptors generate spiking nerve signals. These generated signals are transmitted to the brain along the neural pathway to advance to the stage of recognition or perception, and then they reach the area of discrimination or judgment for remembering, assessing, and processing incoming information. We review research trends in neuromorphic sensory perception technology inspired by biological sensory perception functions. Among the various senses, we consider sensory nerve decoding technology based on sensory nerve pathways focusing on touch and smell, neuromorphic synapse elements that mimic biological neurons and synapses, and neuromorphic processors. Neuromorphic sensory devices, neuromorphic synapses, and artificial sensory memory devices that integrate storage components are being actively studied. However, various problems remain to be solved, such as learning methods to implement cognitive functions beyond simple detection. Considering applications such as virtual reality, medical welfare, neuroscience, and cranial nerve interfaces, neuromorphic sensory recognition technology is expected to be actively developed based on new technologies, including combinatorial neurocognitive cell technology.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.373-382
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• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.333-349
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• 2005

In this paper, we propose a real tine moving object tracking system based on block-based image processing technique and human visual processing. The system has two nun features. First, to take advantage of the merit of the biological mechanism of human retina, the system has two cameras, a CCD(Charge-Coupled Device) camera equipped with wide angle lens for more wide scope vision and a Pan-Tilt-Zoon tamers. Second, the system divides the input image into a numbers of blocks and processes coarsely to reduce the rate of tracking error and the processing time. Tn an experiment, the system showed satisfactory performances coping with almost every noisy image, detecting moving objects very int and controlling the Pan-Tilt-Zoom camera precisely.

• Journal of Biomedical Engineering Research
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• v.19 no.4
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• pp.369-377
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• 1998

This paper describes the results of investigations comparing the relative in vitro responses of different signal processing algorithms for laser Doppler flowmetry(LDF) using self-mixing effect of laser diode(LD). A versatile laser Doppler system is described which enabled complex signal processing to be implemented relatively simply using digital analysis. The flexibility of the system allowed a variety of processing algorithms to be studied by simply characterising the algorithm of interest under software control using a personal computer. Two in-vitro physical models are also presented which was used to maintain reproducible fluid flows Flows of particles were studied in two physical models using a 780nm laser diode source. The results show that frequency weighted algorithms(first and second moments, rate to zero moment) are responsive to particle velocity more than concentration, whereas non-weighted algorithm (zero moment responds to concentration and velocity.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.61-71
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• 2022

Soil color is used to determine soil classification and its physical, chemical, and biological properties. Visual determination is the most commonly used method for identifying soil color. However, it is subjective and, in many cases, non-repeatable. Digital image processing obtains useful information from digital images, accelerates soil classification, and enables the rapid identification of soil types in a field. This study develops a digital image processing-based soil color analysis technology that can consider irregular light conditions in the field. The digital image studio was designed to simulate the characteristics of natural light (illuminance and color temperature). Also, digital images of two soil samples (Jumoonjin sand and Anseong weathered soil) were captured under 12 different light conditions. For the RGB and CIELAB color systems, soil color intensities of 24 images were obtained using digital image processing. CIELAB was suitable for dealing with irregular light conditions in the field.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.623-631
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• 2000

Advanced Life Support Systems (ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses have been delivered through the Internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. They include object-oriented approach for top-level models, procedural approach for process-level models, and application of commercially available modeling tools such as MATLAB$\^$(R)//Simulink$\^$(R)/. Every paradigm has its particular applicability for the purpose of modeling work. An overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Agricultural and Biosystems Engineering
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• v.2 no.2
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• pp.41-49
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• 2001

Advanced Life Support Systems(ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses heave been delivered through the internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. they include objected-oriented approach for top-level models, procedureal approach for process-level models, and application of commercially available modeling tools such as $MATLAB^{R}$/$Simulink^{R}$. Every paradigm has its particular applicability for the purpose of modeling work. an overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.425-430
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• 1995

The correlation technique has been widely used in ctRl data processing. The proposed CLT (clus- tering threshold) technique is a modified CCT (correlation coefficient threshold) technique and has many advantages compared with the conventional CCT technique. The CLT technique is explained by the following two steps. First, once the correlation coefficient map above the proper TH value is obtained using the CCT technique which is discrete and includes splash noise data, then the spurious pixels are rejected and the real neural activity pixels extracted using an nxn matrix box. Second, a clustering operation is performed by the two correction rules. The real neuronal activated pixels can be clustered and the false spurious pixels can be suppressed by the proposed CLT technique. The proposed CLT technique used in the post processing in ctRl has advantages over other existing techniques. It is especially proved to be robust in noisy environment.