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On a clinical dataset obtained from 25 people with MS monitored over eight weeks during free-living conditions, 54 falls were observed and our system achieved a sensitivity of 92.14%, and false-positive rate of 0.65 false alarms per day.Assessing blood flow, respiration patterns, and body composition with wearable and noninvasive bio-impedance (BioZ) sensors has distinctive advantages over the conventional clinical practice. The merits of BioZ sensors derive from having long-term monitoring capability and improved user friendliness. These open up the way to build medical grade wearable devices for chronic conditions. Low power, high precision BioZ sensor interface IC is the heart of such devices, it also determines the signal integrity of the overall system. Nevertheless, electrical design challenges from both circuit and system perspective still need to be addressed. This paper reviews the pioneering BioZ interface ICs and systems, and proposes major electrical specifications for wearable BioZ sensors. System design methodologies and circuit optimization techniques are summarized as guidelines to develop the next generation BioZ sensors.Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG signals is often highly dependent on the filter band selection. At the same time, optimizing the EEG channel combinations is another key issue that substantially affects the SMR feature representations. Although numerous algorithms have been developed to find channels that record important characteristics of MI, most of them select channels in a cumbersome way with low computational efficiency, thereby limiting the practicality of MI-based BCI systems. In this study, we propose the multi-scale optimization (MSO) of spatial patterns, optimizing filter bands over multiple channel sets within CSPs to further improve the performance of MI-based BCI. Specifically, several channel subsets are first heuristically predefined, and then raw EEG data specific to each of these subsets bandpass-filtered at the overlap between a set of filter bands. Further, instead of solving learning problems for each channel subset independently, we propose a multi-view learning based sparse optimization to jointly extract robust CSP features with L2,1 -norm regularization, aiming to capture the shared salient information across multiple related spatial patterns for enhanced classification performance. A support vector machine (SVM) classifier is then trained on these optimized EEG features for accurate recognition of MI tasks. Experimental results on three public EEG datasets validate the effectiveness of MSO compared to several other competing methods and their variants. These superior experimental results demonstrate that the proposed MSO method has promising potential in MI-based BCIs.Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this paper proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average F_measure. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR.Contrast agents are routinely used in ultrasound examinations. Nonlinear ultrasound imaging techniques have been developed over decades to enhance the contrast between the tissue and the blood pool after the injection of ultrasound contrast agents. In this study, we introduce a new contrast pulse sequence, CPS4. The CPS4 combines pulse inversion, sub-harmonic, and ultra-harmonic techniques to remove propagation distortion while capturing the unique sub-harmonic, and ultra-harmonic responses from ultrasound contrast agents. learn more The novel CPS4 and conventional pulse inversion, sub-harmonic, and ultra-harmonic techniques were used to detect the presence of a research-grade, thick shell, polymer microbubble in a tissue-mimicking flow phantom. The contrast-to-tissue ratio (CTR) obtained from the applications of all techniques were compared. The results show that the highest CTR of approximately 16 dB was obtained using CPS4, which was superior to the individual reference techniques pulse inversion, sub-harmonic, and ultra-harmonic techniques, at all scenarios considered in this study.Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.