Kim, Tae-Seong

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). Th

c-Met is a receptor tyrosine kinase often deregulated in human cancers, thus making it an attractive drug target. One mechanism by which c-Met deregulation leads to cancer is through gain-of-function mutations. Therefore, small molecules capable of targeting these mutations could offer therapeutic benefits for affected patients. SU11274 was recently described and reported to inhibit the activity of the wild-type and some mutant forms of c-Met, whereas other mutants are resistant to inhibition. W

This paper presents a novel human home activity recognition (HAR) system designed for smart homes that utilize depth silhouettes and ℜ transformation to continuously recognize the daily activities of the elderly and disabled in an indoor environment for better lifecare and e-healthcare services. Previously, ℜ transformation has been applied only on binary silhouettes that provide only the shape information of human activities. In this work, ℜ transformation was utilized on depth silhouettes such

To establish safe and efficient transcranial direct current stimulation (tDCS), it is of particular importance to understand the electrical effects of tDCS in the brain. Since the current density (CD) and electric field (EF) in the brain generated by tDCS depend on various factors including complex head geometries and electrical tissue properties, in this work, we investigated the influence of anisotropic conductivity in the skull and white matter (WM) on tDCS via a 3D anatomically realistic fin

Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cell

Smart home technologies are getting considerable attentions nowadays for better care of the residents, especially the elderly. One of the key technologies is the human activity recognition (HAR) system which automatically recognizes various indoor activities of a resident and reacts upon the needs of the resident, known as a proactive system. In this work, we propose a novel HAR system that utilizes depth imaging. Our HAR system utilizes local binary patterns (LBP) as local activity features fro

We have investigated electroencephalography (EEG) distributed source imaging with a realistic finite-element (FE) head model. The performance of different FE imaging methods was evaluated and compared in two- (2-D) and three-dimensional (3-D) simulation studies. The results demonstrate the feasibility of EEG distributed source imaging with FE head models using FE inverse methods. We also show that incorporating prior knowledge of sources significantly improves the inverse solutions. As an applic

Gait recognition at smart home is considered as a primary function of the smart system nowadays. The significance of gait recognition is high especially for the elderly as gait is one of the basic activities to promote and preserve their health. In this work, a novel method was proposed for human gait recognition by processing depth videos from a depth camera. The gait recognition method utilizes local directional patterns (LDPs) for local feature extraction from depth silhouettes and hidden Mar

The precise knowledge of the electromechanical properties of an ultrasonic transmit-receive system can be used to optimize the excitation waveform in transmission-mode tomographic imaging. Although a linear system hypothesis is often postulated to model the dynamic transformation of the excitation waveform delivered at the transducer of the transmitter (input) into the received waveform at the receiver (output), linearity may not be appropriate in order to account for the actual dynamic characte

In diagnostic ultrasound, tissue differentiation is essential to detect lesions or cancerous tissues from normal tissue. The attenuation characteristics of various tissues will be different at different frequencies, since the propagating ultrasonic pulse undergoes frequency-dependent attenuation, that is characteristic of the material it traverses. These vectors of attenuation values at different frequency bands represent multi-band characteristics of individual pixels (termed “multispectral”) t

This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study proposes a 2D equivalent analysis method that addresses the unique features of outer rotor type BLDC motors, particularly the permanent magnet (PM) overhang structure. This approach