Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

Sla-driven automatic bottleneck detection and resolution for read intensive multi-tier applications hosted on a cloud

Abstract. A Service-Level Agreement (SLA) provides surety for specific quality attributes to the consumers of services. However, the current SLAs offered by cloud providers do not address response time, which, from the user's point of view, is the most important quality attribute for Web applications. Satisfying a maximum average response time guarantee for Web applications is difficult for two main reasons: first, traffic patterns are unpredictable; second, the complex nature of multi-tier Web applications increases the difficulty of identifying bottlenecks and resolving them automatically. This paper presents a working prototype system that automatically detects and resolves bottlenecks in a multi-tier Web application hosted on a EUCALYPTUS-based cloud in order to satisfy specific maximum response time requirements. We demonstrate the feasibility of the approach in an experimental evaluation with a testbed cloud and a synthetic workload. Automatic bottleneck detection and resolution under dynamic resource management has the potential to enable cloud providers to provide SLAs for Web applications that guarantee specific response time requirements

Joint localization of pursuit quadcopters and target using monocular cues

Pursuit robots (autonomous robots tasked with tracking and pursuing a moving target) require accurate tracking of the target's position over time. One possibly effective pursuit platform is a quadcopter equipped with basic sensors and a monocular camera. However, combined noise of the quadcopter's sensors causes large disturbances of target's 3D position estimate. To solve this problem, in this paper, we propose a novel method for joint localization of a quadcopter pursuer with a monocular camera and an arbitrary target. Our method localizes both the pursuer and target with respect to a common reference frame. The joint localization method fuses the quadcopter's kinematics and the target's dynamics in a joint state space model. We show that predicting and correcting pursuer and target trajectories simultaneously produces better results than standard approaches to estimating relative target trajectories in a 3D coordinate system. Our method also comprises a computationally efficient visual tracking method capable of redetecting a temporarily lost target. The efficiency of the proposed method is demonstrated by a series of experiments with a real quadcopter pursuing a human. The results show that the visual tracker can deal effectively with target occlusions and that joint localization outperforms standard localization methods

Magnetic Resonance Imaging of Pulmonary Lesions in Guinea Pigs Infected with Mycobacterium tuberculosis

We utilized magnetic resonance imaging to visualize lesions in the lungs of guinea pigs infected by low-dose aerosol exposure to Mycobacterium tuberculosis. Lesions were prominent in such images, and colorized three-dimensional reconstructions of images revealed a very uniform distribution in the lungs. Lesion numbers after 1 month were approximately similar to the aerosol exposure algorithm, suggesting that each was established by a single bacterium. Numbers of lesions in unprotected and vaccinated animals were similar over the first month but increased thereafter in the control animals, indicating secondary lesion development. Whereas lesion sizes increased progressively in control guinea pigs, lesions remained small in BCG-vaccinated animals. A prominent feature of the disease pathology in unprotected animals was rapid and severe lymphadenopathy of the mediastinal lymph node cluster, which is paradoxical given the strong state of cellular immunity at this time. Further development of this technical approach could be very useful in tracking lesion size, number, and progression in the search for new tuberculosis vaccines

Building a needs-based curriculum in data science and artificial intelligence: case studies in Indonesia, Sri Lanka, and Thailand

Indonesia and Thailand are middle-income countries within the South-East Asia region. They have well-established and growing higher education systems, increasingly focused on quality improvement. However, they fall behind regional leaders in educating people who design, develop, deploy and train data science and artificial intelligence (DS&AI) based technology, as evident from the technological market, regionally dominated by Singapore and Malaysia, while the region as a whole is far behind China. A similar situation holds also for Sri Lanka, in the South Asia region technologically dominated by India. In this paper, we describe the design of a master's level curriculum in data science and artificial intelligence using European experience on building such curricula. The design of such a curriculum is a nontrivial exercise because there is a constant trade-off between having a sufficiently broad academic curriculum and adequately meeting regional needs, including those of industrial stakeholders. In fact, findings from a gap analysis and assessment of needs from three case studies in Indonesia, Sri Lanka, and Thailand comprise the most significant component of our curriculum development process.The authors would like to thank the European Union Erasmus+ programme which provided funding through the Capacity Building Higher Education project on Curriculum Development in Data Science and Artificial Intelligence, registered under the reference number 599600-EPP-1-2018-1-TH-EPPKA2-CBHE-JP

The Changing Epidemiology of Murray Valley Encephalitis in Australia: The 2011 Outbreak and a Review of the Literature

Murray Valley encephalitis virus (MVEV) is the most serious of the endemic arboviruses in Australia. It was responsible for six known large outbreaks of encephalitis in south-eastern Australia in the 1900s, with the last comprising 58 cases in 1974. Since then MVEV clinical cases have been largely confined to the western and central parts of northern Australia. In 2011, high-level MVEV activity occurred in south-eastern Australia for the first time since 1974, accompanied by unusually heavy seasonal MVEV activity in northern Australia. This resulted in 17 confirmed cases of MVEV disease across Australia. Record wet season rainfall was recorded in many areas of Australia in the summer and autumn of 2011. This was associated with significant flooding and increased numbers of the mosquito vector and subsequent MVEV activity. This paper documents the outbreak and adds to our knowledge about disease outcomes, epidemiology of disease and the link between the MVEV activity and environmental factors. Clinical and demographic information from the 17 reported cases was obtained. Cases or family members were interviewed about their activities and location during the incubation period. In contrast to outbreaks prior to 2000, the majority of cases were non-Aboriginal adults, and almost half (40%) of the cases acquired MVEV outside their area of residence. All but two cases occurred in areas of known MVEV activity.This outbreak continues to reflect a change in the demographic pattern of human cases of encephalitic MVEV over the last 20 years. In northern Australia, this is associated with the increasing numbers of non-Aboriginal workers and tourists living and travelling in endemic and epidemic areas, and also identifies an association with activities that lead to high mosquito exposure. This outbreak demonstrates that there is an ongoing risk of MVEV encephalitis to the heavily populated areas of south-eastern Australia

Whole-genome sequencing and annotation of Bacillus safensis RIT372 and Pseudomonas oryzihabitans RIT370 from Capsicum annuum (bird\u27s eye chili) and Capsicum chinense (yellow lantern chili), respectively

Here, we report the genome sequences of Bacillus safensis RIT372 and Pseudomonas oryzihabitans RIT370 from Capsicum spp. Annotation revealed gene clusters for the synthesis of bacilysin, lichensin, and bacillibactin and sporulation killing factor (skfA) in Bacillus safensis RIT372 and turnerbactin and carotenoid in Pseudomonas oryzihabitans RIT370