Ambient Light Independent Depth Acquisition Using Switchable IR filter

Devices such as smartphones, tablets, etc. are equipped with cameras that have depth-sensing capabilities. The quality of depth acquisition in such devices is often limited by the cost, available physical space for depth-sensing hardware, and by the ambient light conditions in which an image is captured. This disclosure describes a depth-sensing camera system that utilizes a switchable infrared filter. In an IR-cut mode, used when ambient light is of sufficient quality, the filter allows visible light to pass through and cuts off IR. In an IR-pass mode, used in low light conditions, the filter allows IR to pass through while cutting off visible light. The camera system enables high resolution depth acquisition in arbitrary light conditions at low cost and with compact hardware

Comparison of diffusion-weighted imaging and contrast-enhanced T1-weighted imaging on a single baseline MRI for demonstrating dissemination in time in multiple sclerosis

BACKGROUND: The 2010 Revisions to the McDonald Criteria have established that dissemination in time (DIT) of multiple sclerosis (MS) can be demonstrated by simultaneous presence of asymptomatic gadolinium-enhancing and nonenhancing lesions on a single magnetic resonance imaging (MRI). However, gadolinium-based contrast agents (GBCAs) have contraindications. Diffusion-weighted imaging (DWI) can detect diffusion alterations in active inflammatory lesions. The purpose of this study was to investigate if DWI can be an alternative to contrast-enhanced T1-weighted imaging (CE T1WI) for demonstrating DIT in MS. METHODS: We selected patients with clinically definite MS and evaluated their baseline brain MRI. Asymptomatic lesions were identified as either hyperintense or nonhyperintense on DWI and enhancing or nonenhancing on CE T1WI. Fisher’s exact test was performed to determine whether the hyperintensity on DWI was related to the enhancement on CE T1WI (P < 0.05). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the DWI to predict lesion enhancement were calculated. RESULTS: Twenty-two patients with 384 demyelinating lesions that were hyperintense on T2-weighted imaging and more than 3 mm in size were recruited. The diffusion hyperintensity and lesion enhancement were significantly correlated (P <0.001). The sensitivity, specificity, PPV, NPV and accuracy were 100%, 67.9%, 32.3%, 100% and 72.1%, respectively. CONCLUSIONS: A hyperintense DWI finding does not necessarily overlap with contrast enhancement. There are many false positives, possibly representing other stages of lesion development. Although DWI may not replace CE T1WI imaging to demonstrate DIT due to the low PPV, it may serve as a screening MRI sequence where the use of GBCAs is a concern

The use of evidence to construct a learned resourcefulness intervention program for older family caregivers

This study is to develop a learned resourcefulness program for older family caregivers using evidence-based methods and the theory of knowledge acquisition, to help caregivers of older adult patients adapt to the pressures of long-term care

Applying the Resilience to the Community Development in Taiwan

In 1999, the Shishui community in Nantou County, Taiwan was severely damaged by the 921 earthquakes, with many houses collapsing and community facilities suffering unprecedented damage. In late 2001, a community development association was established, and with the efforts of residents, the community was awarded the title of Classic Rural Area by the government in 2007, becoming a nationally recognized leisure agriculture demonstration base. However, with the subsequent reduction of external resources, continuous loss of internal talents, and operational dysfunction of community organizations, the Shishui community eventually stagnated. How can we evaluate the community\u27s resilience in this situation? This study takes the Shishui community as its research field, integrates expert and scholar research on community resilience, constructs the elements of Shishui community development resilience through in-depth interviews with community residents, and analyzes Shishui community resilience indicators using the Fuzzy Delphi Method (FDM), with five main dimensions: sustainable development capacity, organizational leadership capacity, financial management capacity, community cohesion, and network resource capacity. Then, using the Similarity-based Importance-Performance Analysis (SBIPA) method, the study analyzes the evaluations of Shishui community residents on the satisfaction and importance of each resilience indicator. Finally, a total of 18 issues that residents consider important but have not yet reached the expected level of performance are identified, such as community resources and mechanisms to support young people returning to their hometowns and fair distribution of community resources by community organizations, which will become important directions for future community governance improvement

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

EEG-based Brain-computer interfaces (BCI) are facing grant challenges in their real-world applications. The technical difficulties in developing truly wearable multi-modal BCI systems that are capable of making reliable real-time prediction of users’ cognitive states under dynamic real-life situations may appear at times almost insurmountable. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report our attempt to develop a pervasive on-line BCI system by employing state-of-art technologies such as multi-tier fog and cloud computing, semantic Linked Data search and adaptive prediction/classification models. To verify our approach, we implement a pilot system using wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end fog servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end cloud servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch and the UCSD Movement Disorder Center to use our system in real-life personal stress and in-home Parkinson’s disease patient monitoring experiments. We shall proceed to develop a necessary BCI ontology and add automatic semantic annotation and progressive model refinement capability to our system

Effectiveness evaluation of glutaraldehyde protection program conduction for operation room nursing staff

Session presented on Sunday, July 26, 2015: Purpose: To compare the knowledge and exact conduction rate of nursing staff for glutaraldehyde protection before and after our intervention. Methods: We chose 42 operation room nursing staff from one teaching hospital by purposive sampling. Through questionnaires and behavior observation, we evaluated the knowledge and exact protection rate for glutaraldehyde of the nursing staff before and after our intervention. Results: After our intervention, the exact protection rate increased from 62.1% to 94.3%. The protection knowledge was promoted from 56.3% to 95.3%. Both reached significant difference statistically. Before the intervention, the item with lowest exact rate is \u27Staff would wear protective glasses and isolated clothing while contacting chemical agents\u27. After the intervention, the item with highest exact rate is \u27Instruments should be sunk into the disinfectant totally and the lid should be closed tightly\u27. Conclusion: Our results clearly indicated that after the intervention, the exact protection rate of staff was significantly increased. We thus suggest this kind of protective training be listed as routine training programs, thus creating a safer working environment

Image Deraining via Self-supervised Reinforcement Learning

The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer vision applications that rely on those images. The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Derain). We locate rain streak pixels from the input rain image via dictionary learning and use pixel-wise RL agents to take multiple inpainting actions to remove rain progressively. To our knowledge, this work is the first attempt where self-supervised RL is applied to image deraining. Experimental results on several benchmark image-deraining datasets show that the proposed SRL-Derain performs favorably against state-of-the-art few-shot and self-supervised deraining and denoising methods