In recent years, multiple-input multiple-output (MIMO) channel models for crowded areas, such as indoor offices, shops, and outdoor hotspot environments, have become a topic of significant interest. In such crowded environments, propagation paths are frequently shadowed by moving objects, such as pedestrians or vehicles. These shadowing effects can cause time variations in the delay and angle-of-arrival (AoA) characteristics of a channel. In this paper, we propose a method for modeling the shadowing effects of pedestrians in a cluster-based channel model. The proposed method uses cluster power variations to model the time-varying channel properties. We also propose a novel method for estimating the cluster power variation properties from measured data. In order to validate our proposed method, channel sounding in the 3GHz band is conducted in a cafeteria during lunchtime. The results for the K parameter, delay spreads, and AoA azimuth spreads are compared for the measured data and the channel data generated using the proposed method. The results indicate that the time-varying delay-AoA characteristics can be effectively modeled using our proposed method.

Human body segmentation has many applications in a wide variety of image processing tasks, from intelligent vehicles to entertainment. A substantial amount of research has been done in the field of segmentation and it is still one of the active research areas, resulting in introduction of many innovative methods in literature. Still, until today, a method that can overcome the human segmentation problems and adapt itself to different kinds of situations, has not been introduced. Many of methods today try to use the graph-cut framework to solve the segmentation problem. Although powerful, these methods rely on a distance penalty term (intensity difference or RGB color distance). This term does not always lead to a good separation between two regions. For example, if two regions are close in color, even if they belong to two different objects, they will be grouped together, which is not acceptable. Also, if one object has multiple parts with different colors, e.g. humans wear various clothes with different colors and patterns, each part will be segmented separately. Although this can be overcome by multiple inputs from user, the inherent problem would not be solved. In this paper, we have considered solving the problem by making use of a human probability map, super-pixels and Grab-cut framework. Using this map relives us from the need for matching the model to the actual body, thus helps to improve the segmentation accuracy. As a result, not only the accuracy has improved, but also it also became comparable to the state-of-the-art interactive methods.

In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.

Traditional low-rank feature lose the temporal information among action sequence. To obtain the temporal information, we split an action video into multiple action subsequences and concatenate all the low-rank features of subsequences according to their time order. Then we recognize actions by learning a novel dictionary model from concatenated low-rank features. However, traditional dictionary learning models usually neglect the similarity among the coding coefficients and have bad performance in dealing with non-linearly separable data. To overcome these shortcomings, we present a novel similarity constrained discriminative kernel dictionary learning for action recognition. The effectiveness of the proposed method is verified on three benchmarks, and the experimental results show the promising results of our method for action recognition.

This paper proposes the use of two transmit and two receive antennas spaced at roughly the width of a human body to improve communication quality in the presence of shadowing by a human body in the 60GHz band. In the proposed method, the transmit power is divided between the two transmit antennas, and the receive antenna that provides the maximum receive level is then chosen. Although the receive level is reduced by 3dB, the maximum attenuation caused by human body shadowing is totally suppressed. The relationship between the antenna element spacing and the theoretical spacing based on the 1st. Fresnel zone theory is clarified. Experiments confirm that antenna spacing several centimeters wider than that given by the 1st. Fresnel zone theory is enough to attain a significant performance improvement.

Microwave systems have a number of promising applications in surveillance and monitoring systems. The main advantage of microwave systems is their ability to detect targets at distance under adverse conditions such as dim, smoky, and humid environments. Specifically, the wide bandwidth of ultra-wideband radar enables high range resolution. In a previous study, we proposed an accurate shape estimation algorithm for multiple targets using multiple ultra-wideband Doppler interferometers. However, this algorithm produces false image artifacts under conditions with severe interference. The present paper proposes a technique to suppress such false images by detecting inconsistent combinations of the radial velocity and time derivative of image positions. We study the performance of the proposed method through numerical simulations of a two-dimensional section of a moving human body, and demonstrate the remarkable performance of the proposed method in suppressing false image artifacts in many scenarios.

Chen proposed an image quality assessment method to evaluate image quality at a ratio of noise in an image. However, Chen's method had some drawbacks that unnoticeable noise is reflected in the evaluation or noise position is not accurately detected. Therefore, in this paper, we propose a new image quality measurement scheme using the mean-centered WLNI (Weber's Law Noise Identifier) and the saliency map. The experimental results show that the proposed method outperforms Chen's and agrees more consistently with human visual judgment.

To investigate electrostatic discharge (ESD) immunity testing for wearable electronic devices, the worst scenario i.e., an ESD event occurs when the body-mounted device approaches a grounded conductor is focused in this paper. Discharge currents caused by air discharges from a charged human through a hand-held metal bar or through a semi-sphere metal attached to the head, arm or waist in lieu of actual wearable devices are measured. As a result, it is found that at a human charge voltage of 1kV, the peak current from the semi-sphere metal is large in order of the attachment of the waist (15.4A), arm (12.8A) and head (12.2A), whereas the peak current (10.0A) from the hand-held metal bar is the smallest. It is also found that the discharge currents through the semi-sphere metals decrease to zero at around 50ns regardless of the attachment positions, although the current through the hand-held metal bar continues to flow at over 90ns. These discharge currents are further characterized by the discharge resistance, the charge storage capacitance and the discharge time constant newly derived from the waveform energy, which are validated from the body impedance measured through the hand-held and body-mounted metals. The above finding suggests that ESD immunity test methods for wearable devices require test specifications entirely different from the conventional ESD immunity testing.

Recently, wearable wireless devices or terminals have become hot a topic not only in research but also in business. Implantable wireless devices can temporarily be utilized to monitor a patient's condition in an emergency situation or to identify people in highly secured places. Unlike conventional wireless devices, wearable or implantable devices are used on or in the human body. In this sense, body-centric wireless communications (BCWCs) have become a very active area of research. Radio-frequency or microwave medical devices used for cancer treatment systems and surgical operation have completely different functions, but they are used on or in the human body. In terms of research techniques, such medical devices have a lot of similarities to BCWCs. The antennas to be used in the vicinity of the human body should be safe, small and robust. Also, their interaction with the human body should be well considered. This review paper describes some of the wearable antennas as well as implantable antennas that have been studied in our laboratory.

Current research trends in computer vision have tended towards achieving the goal of recognizing human action, due to the potential utility of such recognition in various applications. Among many potential approaches, an approach involving Gaussian Mixture Model (GMM) supervectors with a Support Vector Machine (SVM) and a nonlinear GMM KL kernel has been proven to yield improved performance for recognizing human activities. In this study, based on tensor analysis, we develop and exploit an extended class of action features that we refer to as gradient-flow tensor divergence. The proposed method has shown a best recognition rate of 96.3% for a KTH dataset, and reduced processing time.

We propose part-segment (PS) features for estimating an articulated pose in still images. The PS feature evaluates the image likelihood of each body part (e.g. head, torso, and arms) robustly to background clutter and nuisance textures on the body. While general gradient features (e.g. HOG) might include many nuisance responses, the PS feature represents only the region of the body part by iterative segmentation while updating the shape prior of each part. In contrast to similar segmentation features, part segmentation is improved by part-specific shape priors that are optimized by training images with fully-automatically obtained seeds. The shape priors are modeled efficiently based on clustering for fast extraction of PS features. The PS feature is fused complementarily with gradient features using discriminative training and adaptive weighting for robust and accurate evaluation of part similarity. Comparative experiments with public datasets demonstrate improvement in pose estimation by the PS features.

A method for measuring the magnetic field strength for human exposure assessment closer than 20cm to wireless power transfer (WPT) systems for information household appliances is investigated based on numerical simulations and measurements at 100kHz and 6.78MHz. Four types of magnetic sources are considered: a simple 1-turn coil and three types of coils simulating actual WPT systems. A magnetic sensor whose cross sectional area is 100cm2 as prescribed in International Electrotechnical Commission 62233 is used. Simulation results show that the magnetic field strength detected by the magnetic sensor is affected by its placement angle. The maximum coefficient of variation (CV) is 27.2% when the magnetic source and the sensor are in contact. The reason for this deviation is attributable to the localization of the magnetic field distribution around the magnetic source. The coupling effect between the magnetic source and the sensor is negligible. Therefore, the sensor placement angle is an essential factor in magnetic field measurements. The CV due to the sensor placement angle is reduced from 21% to 4% if the area of the sensor coil is reduced from 100 to 0.75cm2 at 6.78MHz. However, the sensitivity of the sensor coil is decreased by 42.5dB. If measurement uncertainty that considers the deviation in the magnetic field strength due to the sensor placement angle is large, the measured magnetic field strength should be corrected by the uncertainty. If the magnetic field distribution around the magnetic source is known, conservative exposure assessments can be achieved by placing the magnetic sensor in locations at which the spatial averaged magnetic field strengths perpendicular to the magnetic sensor coils become maximum.

Intelligent information systems captivate people's attention. Examples of such systems include driving support vehicles capable of sensing driver state and communication robots capable of interacting with humans. Modeling how people search visual information is indispensable for designing these kinds of systems. In this paper, we focus on human visual attention, which is closely related to visual search behavior. We propose a computational model to estimate human visual attention while carrying out a visual target search task. Existing models estimate visual attention using the ratio between a representative value of visual feature of a target stimulus and that of distractors or background. The models, however, can not often achieve a better performance for difficult search tasks that require a sequentially spotlighting process. For such tasks, the linear separability effect of a visual feature distribution should be considered. Hence, we introduce this effect to spatially localized activation. Concretely, our top-down model estimates target-specific visual attention using Fisher's variance ratio between a visual feature distribution of a local region in the field of view and that of a target stimulus. We confirm the effectiveness of our computational model through a visual search experiment.

Reduced-reference (RR) image quality assessment (IQA) algorithm aims to automatically evaluate the distorted image quality with partial reference data. The goal of RR IQA metric is to achieve higher quality prediction accuracy using less reference information. In this paper, we introduce a new RR IQA metric by quantifying the difference of discrete cosine transform (DCT) entropy features between the reference and distorted images. Neurophysiological evidences indicate that the human visual system presents different sensitivities to different frequency bands. Moreover, distortions on different bands result in individual quality degradations. Therefore, we suggest to calculate the information degradation on each band separately for quality assessment. The information degradations are firstly measured by the entropy difference of reorganized DCT coefficients. Then, the entropy differences on all bands are pooled to obtain the quality score. Experimental results on LIVE, CSIQ, TID2008, Toyama and IVC databases show that the proposed method performs highly consistent with human perception with limited reference data (8 values).

This paper presents a method of imaging a two-dimensional section of a walking person using multiple Doppler radar systems. Although each simple radar system consists of only two receivers, different radial speeds allow target positions to be separated and located. The signal received using each antenna is processed employing time-frequency analysis, which separates targets in the time-range-velocity space. This process is followed by a direction-of-arrival estimation employing interferometry. The data obtained using the multiple radar systems are integrated using a clustering algorithm and a target-tracking algorithm. Through realistic simulations, we demonstrate the remarkable performance of the proposed imaging method in generating a clear outline image of a human target in unknown motion.

Traditional sparse representation-based methods for human action recognition usually pool over the entire video to form the final feature representation, neglecting any spatio-temporal information of features. To employ spatio-temporal information, we present a novel histogram representation obtained by statistics on temporal changes of sparse coding coefficients frame by frame in the spatial pyramids constructed from videos. The histograms are further fed into a support vector machine with a spatial pyramid matching kernel for final action classification. We validate our method on two benchmarks, KTH and UCF Sports, and experiment results show the effectiveness of our method in human action recognition.

This paper deals with the small-scale fading distribution for UWB channels in the absence and presence of human bodies in indoor line-of-sight (LOS) environments and performance analysis of UWB systems considering the small-scale fading distribution. To obtain small-scale fading statistics, the channel measurements are performed in five representative environments that have different structure and size while locating the receiver (Rx) antenna on 49 (7×7 grid) local points with a fixed transmitter (Tx) antenna in each environment. The measured channel data are processed by a vector network analyzer and the target frequency bands range from 3 to 4.6GHz. From the measured data, we find the best fitted channel model among several typical theoretical distribution models such as Lognormal, Nakagami, and Weibull distributions, showing good agreement with the empirical channel data. We analyze the amplitude variation of the small-scale fading distribution in the absence and presence of human bodies. The results show that the small-scale fading statistics are best described by Weibull distribution and the two parameters of the distribution that determine the shape and the scale of the distribution depend on whether or not human bodies exist. We modeled and analyzed two parameters at different excess delays for all environments. Based on the measured small-scale fading distribution, this paper deals with the performance of UWB system using Rake receivers and also compares the performance with the existing channel model. The results suggest that the small-scale fading distribution in the absence and the presence of human bodies in indoor LOS environments should be considered when assessing the performance of UWB systems.

In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for human detection as a modified version of the conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are performed for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, and to the best of our knowledge, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions of NRGSLBP is necessary, and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).

Magnetic resonant coupling between two coils allows effective wireless transfer of power over distances in the range of tens of centimeters to a few meters. The strong resonant magnetic field also extends to the immediate surroundings of the power transfer system. When a user or bystander is exposed to this magnetic field, electric fields are induced in the body. For the purposes of human and product safety, it is necessary to evaluate whether these fields satisfy the human exposure limits specified in international guidelines and standards. This work investigates the effectiveness of the quasistatic approximation for computational modeling human exposure to the magnetic fields of wireless power transfer systems. It is shown that, when valid, this approximation can greatly reduce the computational requirements of the assessment of human exposure. Using the quasistatic modeling approach, we present an example of the assessment of human exposure to the non-uniform magnetic field of a realistic WPT system for wireless charging of an electric vehicle battery, and propose a coupling factor for practical determination of compliance with the international exposure standards.

With the development and diverse use of wireless radio terminals, it is necessary to estimate the specific absorption rate (SAR) of the human body from such devices under various exposure situations. In particular, tablet computers may be used for a long time while placed near the abdomen. There has been insufficient evaluation of the SAR for the human body from tablet computers. Therefore, we investigated the SAR of various configurations of a commercial tablet computer using a numerical model with the anatomical structures of Japanese males and females, respectively. We find that the 10-g-averaged SAR of the tablet computer is strongly altered by the tablet's orientation, i.e., from -7.3dB to -22.6dB. When the tablet computer is moved parallel to the height direction, the relative standard deviations of the 10-g averaged SAR for the male and female models are within 40%. In addition, those for the different tilts of the computer are within 20%. The fluctuations of the 10-g-averaged SAR for the seated human models are within ±1.5dB in all cases.