Fusion of multiple self-diagnostic questionnaires into optimal diagnostic cut-offs and factor analysis for depression characterization of the Korean university student group.

Background: We were interested in developing a methodology for diagnosing the depression status of a focused population group, such as the Korean university student group, with higher accuracy. To this end, we proposed a method of fusing the data collected from multiple depression self-questionnaires aided by a psychiatrist's diagnosis. In particular, we found that the standard diagnostic cut-offs and factor analysis prepared for a general population by depression self-questionnaires are inadequate for a focused population with its unique cultural background.

Breast Lesion Detection for Ultrasound Images Using MaskFormer.

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound's limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance.

Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human-Robot Interaction.

For successful human-robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human-robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI.

The Effect of Training on Localizing HoloLens-Generated 3D Sound Sources.

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function.

Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations.

Liver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of standardized US scans coordinated with reference liver segments to guide examiners.

Effect of Perceptual Training with Sound-Guided and Kinesthetic Feedback on Human 3D Sound Localization Capabilities.

In this paper, we experimentally investigate how the 3D sound localization capabilities of the blind can improve through perceptual training. To this end, we develop a novel perceptual training method with sound-guided feedback and kinesthetic assistance to evaluate its effectiveness compared to conventional training methods. In perceptual training, we exclude visual perception by blindfolding the subjects to apply the proposed method to the visually impaired.

Progressive Deep Learning Framework for Recognizing 3D Orientations and Object Class Based on Point Cloud Representation.

Deep learning approaches to estimating full 3D orientations of objects, in addition to object classes, are limited in their accuracies, due to the difficulty in learning the continuous nature of three-axis orientation variations by regression or classification with sufficient generalization. This paper presents a novel progressive deep learning framework, herein referred to as 3D POCO Net, that offers high accuracy in estimating orientations about three rotational axes yet with efficiency in network complexity. The proposed 3D POCO Net is configured, using four PointNet-based networks for independently representing the object class and three individual axes of rotations.

Micro star tracker with a curved vane for a short baffle length and sharp stray light attenuation.

We present a micro star tracker with curved vanes that offers a short length of the baffle and a sharp cutoff of stray light. The curved vanes are derived mathematically by ray-tracing in such a way that all the stray light from outside of the desired field of view (FOV) is reflected out. The proposed curved vane design allows a smaller number of vanes to completely cut off stray light, leading to a shorter length in baffle design.

Gaze and Eye Tracking: Techniques and Applications in ADAS.

Tracking drivers' eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers' community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories.

A Comprehensive Survey of Driving Monitoring and Assistance Systems.

Improving a vehicle driver's performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience.

Accurate and Cost-Effective Micro Sun Sensor based on CMOS Black Sun Effect.

An accurate and cost-effective micro sun sensor based on the extraction of the sun vector using a phenomenon called the "black sun" is presented. Unlike conventional image-based sun sensors where there is difficulty in accurately detecting the sun center, the black sun effect allows the sun center to be accurately extracted even with the sun image appearing irregular and noisy due to glare. This allows the proposed micro sun sensor to achieve high accuracy even when a 1 mm × 1 mm CMOS image sensor with a resolution of 250 × 250 pixels is used.

A Framework of Covariance Projection on Constraint Manifold for Data Fusion.

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information.

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System.

The quality of the captured point cloud and the scanning speed of a structured light 3D camera system depend upon their capability of handling the object surface of a large reflectance variation in the trade-off of the required number of patterns to be projected. In this paper, we propose and implement a flexible embedded framework that is capable of triggering the camera single or multiple times for capturing single or multiple projections within a single camera exposure setting. This allows the 3D camera system to synchronize the camera and projector even for miss-matched frame rates such that the system is capable of projecting different types of patterns for different scan speed applications.

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency.

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data.

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting.

A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time.

Energy-efficient SVM learning control system for biped walking robots.

An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs.

Wide-baseline stereo matching based on the line intersection context for real-time workspace modeling.

Line matching in widely separated views is challenging because of large perspective distortion and violation of the planarity assumption in local regions. We introduce a novel method of wide-baseline image matching based on the coplanar line intersections for poorly textured and/or nonplanar structured scenes. The local areas of the coplanar line pairs are normalized into canonical frames by rectifying the coplanar line pairs to be orthogonal.

Boundary Inheritance Codec for high-accuracy structured light three-dimensional reconstruction with comparative performance evaluation.

This paper presents a new method of structured light-based 3D reconstruction, referred to here as Boundary Inheritance Codec, that provides high accuracy and low noise in projector-camera correspondence. The proposed method features (1) real-boundary recovery: the exact locations of region boundaries, defined by a coded pattern, are identified in terms of their real coordinates on the image plane. To this end, a radiance independent recovery of accurate boundaries and a disambiguation of true and false boundaries are presented.

Structured-light-based highly dense and robust 3D reconstruction.

In this paper, a structured-light-based highly dense and robust 3D reconstruction method is proposed by combining a Gray code and region-shifting pattern. The region-shifting pattern is transformed to the trapezoidal and triangle wave shifting pattern by combining all frames of the region-shifting pattern, and then the boundary of the trapezoidal wave shifting pattern and the peak and phase of the triangle wave shifting pattern are estimated. Through this technique, the spatial resolution is increased about three times.

Contributions of syntactic awareness to reading in Chinese-speaking adolescent readers with and without dyslexia.

This study investigated the relative contribution of syntactic awareness to Chinese reading among Chinese-speaking adolescent readers with and without dyslexia. A total of 78 junior high school students in Hong Kong, 26 dyslexic adolescent readers, 26 average adolescent readers of the same age (chronological age control group) and 26 younger readers matched with the same reading level (reading-level group) participated and were administered measures of IQ, syntactic awareness, morphological awareness, vocabulary knowledge, working memory, word reading, and reading comprehension. Results showed that dyslexic readers scored significantly lower than chronological age but similarly to reading level control groups in most measures, especially in the areas of syntactic skills.

Probabilistic 3D object recognition and pose estimation using multiple interpretations generation.

This paper presents a probabilistic object recognition and pose estimation method using multiple interpretation generation in cluttered indoor environments. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In order to solve this problem, we approach it in a probabilistic manner.

Cognitive skills and literacy performance of Chinese adolescents with and without dyslexia.

The present study sought to identify cognitive abilities that might distinguish Hong Kong Chinese adolescents with dyslexia and to assess how these abilities were associated with Chinese word reading, word dictation, and reading comprehension. The cognitive skills of interest were morphological awareness, visual-orthographic knowledge, rapid naming, and verbal working memory. A total of 90 junior secondary school students, 30 dyslexic, 30 chronological age controls, and 30 reading level controls was tested on a range of cognitive and literacy tasks.

Accurate estimation of the boundaries of a structured light pattern.

Depth recovery based on structured light using stripe patterns, especially for a region-based codec, demands accurate estimation of the true boundary of a light pattern captured on a camera image. This is because the accuracy of the estimated boundary has a direct impact on the accuracy of the depth recovery. However, recovering the true boundary of a light pattern is considered difficult due to the deformation incurred primarily by the texture-induced variation of the light reflectance at surface locales.

Cognitive profiles of Chinese adolescents with dyslexia.

The present study sought to identify cognitive abilities that might distinguish Hong Kong Chinese adolescents with and without dyslexia and examined the cognitive profile of dyslexic adolescents in order to better understand this important problem. The performance of 27 Chinese adolescents with childhood diagnoses of dyslexia was compared with 27 adolescents of the same chronological age (CA) and 27 of matched reading level (RL) on measures of literacy and cognitive abilities: Chinese word reading, one-minute reading, reading comprehension, dictation, verbal short-term memory, rapid naming, visual-orthographic knowledge, morphological and phonological awareness. The results indicated that the dyslexic group scored lower than the CA group, but similar to the RL group, especially in the areas of rapid naming, visual-orthographic knowledge and morphological awareness, with over half having multiple deficits exhibited 2 or more cognitive areas.

In search of subtypes of Chinese developmental dyslexia.

The dual-route model offers a popular way to classify developmental dyslexia into phonological and surface subtypes. The current study examined whether this dual-route model could provide a framework for understanding the varieties of Chinese developmental dyslexia. Three groups of Chinese children (dyslexics, chronological-age controls, and reading-level controls) were tested on Chinese exception character reading, pseudocharacter reading (analogous to English nonword reading), novel word learning, and some phonological and orthographic skills.