Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field

Full HD JPEG XR Encoder Design for Digital Photography Applications

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Novel Codon-optimization Genes Encoded in Chlorella for Triacylglycerol Accumulation

AbstractMicroalgae have been recognized as one of the potential resources for biodiesel production based on its fast growth or its high total lipid content depending on species. Expression of Kennedy pathway genes, which encodes GPAT, LPAAT, PAP, and DGAT for increasing the metabolic flux towards the TAG storage in Chlorella sp. from 20 to 46 wt% and total lipid accumulation from 35 to 60wt.% corresponding to each specific gene combination under autotrophy, compare to the wild type (vector only). The highest TAG content was found in cells expressing a quadruple-gene construct (GPAT-LPAAT-PAP-DGAT) in the Kennedy pathway, corresponding to 46wt.% of TAG and 60wt.% of total lipid content. This work provides the optimization of TAG production in Chlorella sp. can be achieved by manipulating the selected genes, in turns making commercially producing biodiesel practical

Monitoring Apnea in the Elderly by an Electromechanical System with a Carbon Nanotube-based Sensor

SummaryBackgroundBreathing, a part of respiration, is one of the vital functions. Breathing disorders are common in the elderly. An effective breathing sensor for real-time detection of apnea is important in clinical critical care. We aimed to construct a real-time warning platform with a combination of carbon nanotubes (CNTs) and related nano-electromechanical system (NEMS) for elderly care.MethodsThrough a specific acid-treated procedure, multiwalled carbon nanotubes (MWCNTs) were immobilized on a thin silicon dioxide (SiO2) film, coated on a heated silicon wafer. Techniques of photolithography and sputtering with chromium and gold were then implemented on the MWCNT film to develop micro-interdigitated electrodes as a base for the breathing sensor. The sensor was equipped with a programmed microchip processor to become a warning detector for abnormal human breathing, namely less than six breaths per minute. Elderly volunteers were enrolled for examining the effective sensitivity of this novel electromechanical device.ResultsThere were 15 elderly volunteers (9 males and 6 females) tested in this experiment. The dynamic analyses of the MWCNT sensor to exhaled breath showed that it had characteristics of rapid response, high aspect ratio, small tip ratio, and high electrical conductivity. Responses of the MWCNT sensor to exhaled breath was recorded according to different performance parameters, i.e., strength, frequency, flow rate, and breath components. In this study, variable pattern-simulated tests showed that a MWCNT sensor combined with a processor could accurately evoke warning signals (100% of sensitivity rate), indicating its effectiveness and usefulness for detecting abnormal breathing rates, especially apnea.ConclusionOur results showed that a new device composed of an NEMS by combining an MWCNT sensor and complementary metal-oxide semiconductor (CMOS) circuits could be integrated to effectively detect apnea in the elderly. This novel device may improve the pattern of safe respiratory care for the elderly in the future

Lighter U-net for segmenting white matter hyperintensities in MR images

White matter hyperintensities (WMH) is one of main consequences of small vessel diseases. Automated WMH segmentation techniques play an important role in clinical research and practice. U-Net has been demonstrated to yield the best precise segmentation results so far. However, sometimes it losses more detailed information as network goes deeper. In addition, it usually depends on data augmentation or a large number of filters. Large filters increase the complexity of model, which may be an obstacle for real-time segmentation on cloud computing. To solve these two issues, a new architecture, Lighter U-Net is proposed to reinforce feature use, to reduce the number of parameters as well as to retain sufficient receptive fields without losing resolution. The extensive experiments suggest that the proposed network achieves comparable performance as the state-of-the-art methods by only using 17% parameters of standard U-Net

Mitigating Routing Misbehavior Using Ant-Tabu-Based Routing Algorithm for Wireless Ad-Hoc

Summary Routing is a key factor in the design of modern communication networks, especially in wireless ad-hoc networks (WANs). In WANs, both selfish and malicious nodes are misbehaving nodes and cause severely routing and security problems. Selfish nodes may drop routing and data packets and malicious nodes may redirect the packets to another routing path or launch denial-of-service (DoS) attacks. In this paper, an efficient routing algorithm is proposed, Ant-Tabu-Based Routing Algorithm (ATBRA), to mitigate selfish problem and reduce routing overheads. In ATBRA, both the concepts of ant-based routing algorithm and Tabu search are applied. We compare the performance of the proposed scheme with that of DSR in terms of two performance metrics: successful delivery rate (SDR) and routing overhead (RO). By comparisons, we notice that the proposed algorithm outperforms DSR in all two categories. The simulation results also indicate that the proposed algorithm is more efficient than DSR

SPGNet: Semantic Prediction Guidance for Scene Parsing

Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi-stage encoder-decoder networks have been widely used in human pose estimation and show superior performance than their single-stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. We find that by carefully re-weighting features across stages, a two-stage encoder-decoder network coupled with our proposed SPG module can significantly outperform its one-stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only 'fine' annotations.Comment: ICCV 201

An Outbreak of Coxsackievirus A16 Infection: Comparison With Other Enteroviruses in a Preschool in Taipei

Background/PurposeThe transmission rate of enteroviruses in young children remains unclear. Therefore, we carried out active surveillance in preschool children to investigate the transmission rate and clinical manifestation of enteroviruses.MethodsFrom September 2006 to December 2008, we monitored infectious diseases in children 2(–3 years of age) in a preschool in Taipei. If any child had a febrile illness or symptoms/signs of enteroviral infection [e.g. herpangina or hand-foot-and-mouth disease (HFMD)], we performed viral isolation and enterovirus polymerase chain reaction. VP1 sequencing was performed to define their serotypes. We also collected clinical data and analyzed transmission rates.ResultsThere were eight episodes of enterovirus infection during the study period. The serotypes included coxsackievirus A4 (CA4), CA2 and CA16. The transmission rates of CA4 and CA2 among children in same class were 26% and 35%, respectively. Between November 28 and December 12, 2008, 13/21 (61.9%) children contracted herpangina and/or HFMD. The average age was 2.82 (range, 2.43–3.39) years. CA16 was detected in 10/13 (76.9%) of the throat swabs by polymerase chain reaction VP1 genotyping. Compared with previous CA2 and CA4 outbreaks, CA16 had a significantly higher transmission rate (p = 0.035) and resulted in more cases of HFMD (p < 0.001). The transmission duration of coxsackie A viruses within the same class ranged from 12 to 40 days.ConclusionCompared with CA2 and CA4, CA16 infections resulted in more cases of HFMD and had significantly higher transmission rates in preschoolers