Query: Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition

Relevant passages:
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications <=SEP=> In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low

FRACTIONAL DIFFERENTIAL EQUATIONS IN BANACH SPACES <=SEP=> This paper deals with some impulsive fractional differential equations in Banach spaces. Utilizing the Leray-Schauder fixed point theorem and the impulsive nonlinear singular version of the Gronwall inequality, the existence of PC-mild solutions for some fractional differential equations with impulses are obtained under some easily checked conditions. At last, an example is given for demonstration.

Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines <=SEP=> This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.

Histograms of oriented gradients for human detection <=SEP=> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

A vehicle license plate recognition system based on analysis of maximally stable extremal regions <=SEP=> Vehicle License Plate Recognition (VLPR) system is a core module in Intelligent Transportation Systems (ITS). In this paper, a VLPR system is proposed. Considering that license plate localization is the most important and difficult part in VLPR system, we present an effective license plate localization method based on analysis of Maximally Stable Extremal Region (MSER) features. Firstly, MSER detector is utilized to extract candidate character regions. Secondly, the exact locations of license plates are inferred according to the arrangement of characters in standard license plates. The advantage of this license plate localization method is that less assumption of environmental illumination, weather and other conditions is made. After license plate localization, we continue to recognize the license plate characters and color to complete the whole VLPR system. Finally, the proposed VLPR system is tested on our own collected dataset. The experimental results show the availability and effectiveness of our VLPR system in locating and recognizing all the explicit license plates in an image.

UPTIME: Ubiquitous pedestrian tracking using mobile phones <=SEP=> The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

A wideband unity-gain buffer in 0.13-μm CMOS <=SEP=> In this paper, an ultra wideband analog voltage-mode buffer is presented which can drive a load impedance of 50 Ω. The presented feedback-based buffer uses a compound amplifier which is a parallel combination of a high-DC gain operational amplifier and a operation transconductance amplifier to achieve a high unity gain bandwidth. A proof-of-concept prototype is designed and fabricated in a 0.13 μm CMOS process. The simulation and measurement results of the proposed buffer are in good agreement. The prototype buffer circuit consumes 7.34 mW from a 1.3-V supply, while buffering a 2 GHz sinusoidal input signal with a 0.4 V peak-to-peak (Vpp) amplitude and driving an AC-coupled 50-Ω load.

How can human motion prediction increase transparency? <=SEP=> A major issue in the field of human-robot interaction for assistance to manipulation is transparency. This basic feature qualifies the capacity for a robot to follow human movements without any human-perceptible resistive forces. In this paper we address the issue of human motion prediction in order to increase the transparency of a robotic manipulator. Our aim is not to predict the motion itself, but to study how this prediction can be used to improve the robot transparency. For this purpose, we have designed a setup for performing basic planar manipulation tasks involving movements that are demanded to the subject and thus easily predictable. Moreover, we have developed a general controller which takes a predicted trajectory (recorded from offline free motion experiments) as an input and feeds the robot motors with a weighted sum of three controllers: torque feedforward, variable stiffness control and force feedback control. Subjects were then asked to perform the same task but with or without the robot assistance (which was not visible to the subject), and with several sets of gains for the controller tuning. First results seems to indicate that when a predictive controller with open loop torque feedforward is used, in conjunction with force- feedback control, the interaction forces are minimized. Therefore, the transparency is increased.

Brain development and ADHD. <=SEP=> Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by excessive inattention, hyperactivity, and impulsivity, either alone or in combination. Neuropsychological findings suggest that these behaviors result from underlying deficits in response inhibition, delay aversion, and executive functioning which, in turn, are presumed to be linked to dysfunction of frontal-striatal-cerebellar circuits. Over the past decade, magnetic resonance imaging (MRI) has been used to examine anatomic differences in these regions between ADHD and control children. In addition to quantifying differences in total cerebral volume, specific areas of interest have been prefrontal regions, basal ganglia, the corpus callosum, and cerebellum. Differences in gray and white matter have also been examined. The ultimate goal of this research is to determine the underlying neurophysiology of ADHD and how specific phenotypes may be related to alterations in brain structure.

Media Framing and Public Attitudes Toward Biofuels <=SEP=> This study investigates the relationship between media framing and public opinion on the issue of biofuels—transportation fuels made from plants, animal products, or organic waste. First, the paper investigates how media framing of biofuels has changed since the issue regained national prominence in the early 2000s. Through a detailed content analysis of newspaper coverage, the paper documents an increase in negative frames between 1999 and 2008, especially frames focusing on the negative economic effects of biofuels on consumers. Second, using data from a 2010 Internet survey of a random sample of the U.S. public, the paper analyzes the relative influence of these new media frames on public attitudes toward biofuels compared with other common predictors of public opinion, such as party ID, regional economic interests, and personal identity as an environmentalist. In general, the results confirm that public attitudes toward biofuels appear to be shaped by these new media frames, especially among those who indicate a high degree of attention to the media, suggesting the relative importance of framing effects on policy attitudes for environmental and energy policies in general.

Looking Beyond the Visible Scene <=SEP=> A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.

Single motor actuated peristaltic wave generator for a soft bodied worm robot <=SEP=> This paper presents the design and development of a single motor actuated peristaltic worm robot with three segments using a bio-inspired method of locomotion with one actuator that achieves optimised worm like peristaltic motion. Each segment consists of two solid circular disks that have a tendon connected through to the drive mechanism using a Bowden cable and a soft rubber skin that deforms under the compression of the tendon. Our hypothesis that a tuned peristaltic waveform can achieve improved performance of locomotion distance and clamping strength is proven using an initial test platform capable of demonstrating varying waveform types with multiple actuators. Three experiments were undertaken: (i) moving along a flat surface, (ii) moving through a confined tunnel (iii) moving through a confined tunnel whilst pulling a payload. Results from these experiments have identified the optimal parameters for a smart gearbox capable of achieving the same optimal peristaltic waveform signal as the more complex test platform but with only one actuator driving all three worm segments. Unlike other examples of peristaltic worm robots, this example uses a control method embedded within the mechanics of the design removing excessive number of actuators which contributes to miniaturisation, reduces power consumption and simplifies the overall design.

A Powerful Generative Model Using Random Weights for the Deep Image Representation <=SEP=> To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.

Segmentation using Eigenvectors: A Unifying View <=SEP=> Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unied treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from di erent eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images. Human perceiving a scene can often easily segment it into coherent segments or groups. There has been a tremendous amount of e ort devoted to achieving the same level of performance in computer vision. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes the data were generated by multiple processes and estimates the parameters of the processes and the number of components in the mixture. The assignment of points to clusters can then be easily performed by calculating the posterior probability of a point belonging to a cluster. Despite the elegance of this approach, the estimation process leads to a notoriously di cult optimization. The frequently used EM algorithm [3] often converges to a local maximum that depends on the initial conditions.

Machine Learning with World Knowledge: The Position and Survey <=SEP=> Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

Analysis of Flux Leakage in a Segmented Core Brushless Permanent Magnet Motor <=SEP=> This paper presents an analytical model to analyze the leakage flux associated with a brushless permanent magnet motor that utilizes segmented stator core. Due to the presence of a segmented stator structure, the leakage of flux through the tooth tips becomes worthy of analysis and consideration in such a motor. A leakage parameter alpha is developed to show the effects of various design parameters on tooth-tip flux leakage that is essential in accurately predicting the air gap flux density and other output parameters in the motor. Finite-element analysis and experimental verifications are also done to validate the analytical model.

Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer <=SEP=> In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts <=SEP=> The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away. <=SEP=> In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.

Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics <=SEP=> Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database.

OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets <=SEP=> This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.

Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model <=SEP=> As a result of the recent trend towards digitization -- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis -- growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charité University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naïve Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.

A survey of video datasets for anomaly detection in automated surveillance <=SEP=> Automated anomaly detection in surveillance system demands to address various problems of real-time monitoring. Due to these challenges, the researchers from computer vision community focused on automation of the surveillance system. The research carried out in this domain has been proposed many datasets which are suitable for different applications. Though many prominent datasets are available, there is lack of description and categorization of these datasets according to the application demand. A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain. This paper presents various datasets description briefly.

Logic and Artificial Intelligence <=SEP=> Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.

Complete mesocolic excision with central vascular ligation produces an oncologically superior specimen compared with standard surgery for carcinoma of the colon. <=SEP=> PURPOSE
The plane of surgery in colonic cancer has been linked to patient outcome although the optimal extent of mesenteric resection is still unclear. Surgeons in Erlangen, Germany, routinely perform complete mesocolic excision (CME) with central vascular ligation (CVL) and report 5-year survivals of higher than 89%. We aimed to further investigate the importance of CME and CVL surgery for colonic cancer by comparison with a series of standard specimens.


METHODS
The fresh photographs of 49 CME and CVL specimens from Erlangen and 40 standard specimens from Leeds, United Kingdom, for primary colonic adenocarcinoma were collected. Precise tissue morphometry and grading of the plane of surgery were performed before comparison to histopathologic variables.


RESULTS
CME and CVL surgery removed more tissue compared with standard surgery in terms of the distance between the tumor and the high vascular tie (median, 131 v 90 mm; P < .0001), the length of large bowel (median, 314 v 206 mm; P < .0001), and ileum removed (median, 83 v 63 mm; P = .003), and the area of mesentery (19,657 v 11,829 mm(2); P < .0001). In addition, CME and CVL surgery was associated with more mesocolic plane resections (92% v 40%; P < .0001) and a greater lymph node yield (median, 30 v 18; P < .0001).


CONCLUSION
Surgeons in Erlangen routinely practicing CME and CVL surgery remove more mesocolon and are more likely to resect in the mesocolic plane when compared with standard excisions. This, along with the associated greater lymph node yield, may partially explain the high 5-year survival rates reported in Erlangen.

Simple broadband Gysel combiner with a single coupled line <=SEP=> A coupled-Gysel broadband combiner/divider is proposed and demonstrated. The new concept relies on using a single coupled line segment in the design. Significant improvement in bandwidth are realized while maintaining low-loss, ease of design, and flexibility. The coupled-Gysel is demonstrated with a 2.5 - 8 GHz (105% fractional bandwidth) divider with 0.1 dB divider loss, and a 3.4 - 10.2 GHz (100% fractional bandwidth) with 0.2 dB divider loss.

A Deep Learning Approach for Population Estimation from Satellite Imagery <=SEP=> Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the population landscape of a country, they are expensive to realize, are infrequently performed, and only provide population counts over broad areas. Population disaggregation techniques and population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict population in the USA at a 0.01°x0.01° resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level population projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.

High Voltage Pulsed Power Supply Using IGBT Stacks <=SEP=> High voltage pulsed power supply using IGBT (insulated gate bipolar transistor) stacks and pulse transformer for plasma source ion implantation is proposed. To increase voltage rating, twelve IGBTs are used in series at each IGBT stack and a step-up pulse transformer is utilized. To increase the current rating, the proposed system makes use of synchronized three pulse generator modules composed of diodes, capacitors and IGBT stacks. The proposed pulsed power supply uses semiconductor switches as main switches. Hence, the system is compact, and has semi-infinite lifetime. In addition, it has high flexibility in parameters such as voltage magnitude (10-60 kV), pulse repetition rate (PRR) (10-2000 pps), and pulse width (2-5 muS).

Super-resolution from image sequences-a review <=SEP=> Growing interest in super-resolution (SR) restoration of video sequences and the closely related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identify areas which promise performance improvements.

AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor <=SEP=> We present an interactive material acquisition system for average users to capture the spatially-varying appearance of daily objects. While an object is being scanned, our system computes its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially-varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture