A novel image restoration approach based on point location in high-dimensional space geometry

The goal of image restoration is to restore the original clear image from the existing blurred image without distortion as possible. A novel approach based on point location in high-dimensional space geometry method is proposed, which is quite different from the thought ways of existing traditional image restoration approaches. It is based on the high-dimensional space geometry method, which derives from the fact of the Principle of Homology-Continuity (PHC). Begin with the original blurred image, we get two further blurred images. Through the regressive deducing curve fitted by these three images, the first iterative deblured image could be obtained. This iterative "blurring-debluring-blurring" process is performed till reach the deblured image. Experiments have proved the availability of the proposed approach and achieved not only common image restoration but also blind image restoration which represents the majority of real problems.

Non-goal scene analysis for soccer video

The broadcast soccer video is usually recorded by one main camera, which is constantly gazing somewhere of playfield where a highlight event is happening. So the camera parameters and their variety have close relationship with semantic information of soccer video, and much interest has been caught in camera calibration for soccer video. The previous calibration methods either deal with goal scene, or have strict calibration conditions and high complexity. So, it does not properly handle the non-goal scene such as midfield or center-forward scene. In this paper, based on a new soccer field model, a field symbol extraction algorithm is proposed to extract the calibration information. Then a two-stage calibration approach is developed which can calibrate camera not only for goal scene but also for non-goal scene. The preliminary experimental results demonstrate its robustness and accuracy. (c) 2010 Elsevier B.V. All rights reserved.

i-Motif Quadruplex DNA-Based Biosensor for Distinguishing Single- and Multiwalled Carbon Nanotubes

The increasing worldwide demand for carbon nanotubes (CNTs) and increasing concern regarding how to safely develop and use CNTs are requiring a low-cost, simple, and highly sensitive CNT detection assay for toxicological evaluation and environmental monitoring. However, this goal is still far from being achieved. All the current CNT detection techniques are not,applicable for automation and field analysis because they are dependent on highly expensive special instruments and complicated sample preparation. On the basis of the capability of single-walled carbon nanotubes (SWNTs) to specifically induce human telomeric i-motif formation, we design an electrochemical DNA (E-DNA) sensor that can distinguish single- and multiwalled carbon nanotubes both in buffer and in cell extracts. The E-DNA sensor can selectively detect SWNTs; with a direct detection limit of 0.2 ppm and has been demonstrated in cancer cell extracts. To the best of our knowledge, this is the first demonstration of a biosensing technique that can distinguish different types of nanotubes. Our work will provide new insights into how to design a biosensor for detection of carbon nanotubes.

Context-Based Vision System for Place and Object Recognition

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user.

The Definition and Implementation of a Computer Programming Language Based on Constraints

The constraint paradigm is a model of computation in which values are deduced whenever possible, under the limitation that deductions be local in a certain sense. One may visualize a constraint 'program' as a network of devices connected by wires. Data values may flow along the wires, and computation is performed by the devices. A device computes using only locally available information (with a few exceptions), and places newly derived values on other, locally attached wires. In this way computed values are propagated. An advantage of the constraint paradigm (not unique to it) is that a single relationship can be used in more than one direction. The connections to a device are not labelled as inputs and outputs; a device will compute with whatever values are available, and produce as many new values as it can. General theorem provers are capable of such behavior, but tend to suffer from combinatorial explosion; it is not usually useful to derive all the possible consequences of a set of hypotheses. The constraint paradigm places a certain kind of limitation on the deduction process. The limitations imposed by the constraint paradigm are not the only one possible. It is argued, however, that they are restrictive enough to forestall combinatorial explosion in many interesting computational situations, yet permissive enough to allow useful computations in practical situations. Moreover, the paradigm is intuitive: It is easy to visualize the computational effects of these particular limitations, and the paradigm is a natural way of expressing programs for certain applications, in particular relationships arising in computer-aided design. A number of implementations of constraint-based programming languages are presented. A progression of ever more powerful languages is described, complete implementations are presented and design difficulties and alternatives are discussed. The goal approached, though not quite reached, is a complete programming system which will implicitly support the constraint paradigm to the same extent that LISP, say, supports automatic storage management.

Optimization of physical and optical properties of biodegradable edible films based on pea starch and guar gum

The influence of process variables (pea starch, guar gum and glycerol) on the viscosity (V), solubility (SOL), moisture content (MC), transparency (TR), Hunter parameters (L, a, and b), total color difference (ΔE), yellowness index (YI), and whiteness index (WI) of the pea starch based edible films was studied using three factors with three level Box–Behnken response surface design. The individual linear effect of pea starch, guar and glycerol was significant (p < 0.05) on all the responses. However, a value was only significantly (p < 0.05) affected by pea starch and guar gum in a positive and negative linear term, respectively. The effect of interaction of starch × glycerol was also significant (p < 0.05) on TR of edible films. Interaction between independent variables starch × guar gum had a significant impact on the b and YI values. The quadratic regression coefficient of pea starch showed a significant effect (p < 0.05) on V, MC, L, b, ΔE, YI, and WI; glycerol level on ΔE and WI; and guar gum on ΔE and SOL value. The results were analyzed by Pareto analysis of variance (ANOVA) and the second order polynomial models were developed from the experimental design with reliable and satisfactory fit with the corresponding experimental data and high coefficient of determination (R2) values (>0.93). Three-dimensional response surface plots were established to investigate the relationship between process variables and the responses. The optimized conditions with the goal of maximizing TR and minimizing SOL, YI and MC were 2.5 g pea starch, 25% glycerol and 0.3 g guar gum. Results revealed that pea starch/guar gum edible films with appropriate physical and optical characteristics can be effectively produced and successfully applied in the food packaging industry.

Constraint-based Mining of Frequent Arrangements of Temporal Intervals

The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Two efficient methods to find frequent arrangements of temporal intervals are described; the first one is tree-based and uses depth first search to mine the set of frequent arrangements, whereas the second one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints consisting of regular expressions and gap constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

Experimental impulse radio IEEE 802.15.4a UWB based wireless sensor localization technology: Characterization, reliability and ranging

Ultra Wide Band (UWB) transmission has recently been the object of considerable attention in the field of next generation location aware wireless sensor networks. This is due to its fine time resolution, energy efficient and robustness to interference in harsh environments. This paper presents a thorough applied examination of prototype IEEE 802.15.4a impulse UWB transceiver technology to quantify the effect of line of sight (LOS) and non line of sight (NLOS) ranging in real indoor and outdoor environments. Results included draw on an extensive array of experiments that fully characterize the 802.15.4a UWB transceiver technology, its reliability and ranging capabilities for the first time. A new two way (TW) ranging protocol is proposed. The goal of this work is to validate the technology as a dependable wireless communications mechanism for the subset of sensor network localization applications where reliability and precision positions are key concerns.

Study protocol: home-based telehealth stroke care: a randomized trial for veterans.

BACKGROUND: Stroke is one of the most disabling and costly impairments of adulthood in the United States. Stroke patients clearly benefit from intensive inpatient care, but due to the high cost, there is considerable interest in implementing interventions to reduce hospital lengths of stay. Early discharge rehabilitation programs require coordinated, well-organized home-based rehabilitation, yet lack of sufficient information about the home setting impedes successful rehabilitation. This trial examines a multifaceted telerehabilitation (TR) intervention that uses telehealth technology to simultaneously evaluate the home environment, assess the patient's mobility skills, initiate rehabilitative treatment, prescribe exercises tailored for stroke patients and provide periodic goal oriented reassessment, feedback and encouragement. METHODS: We describe an ongoing Phase II, 2-arm, 3-site randomized controlled trial (RCT) that determines primarily the effect of TR on physical function and secondarily the effect on disability, falls-related self-efficacy, and patient satisfaction. Fifty participants with a diagnosis of ischemic or hemorrhagic stroke will be randomly assigned to one of two groups: (a) TR; or (b) Usual Care. The TR intervention uses a combination of three videotaped visits and five telephone calls, an in-home messaging device, and additional telephonic contact as needed over a 3-month study period, to provide a progressive rehabilitative intervention with a treatment goal of safe functional mobility of the individual within an accessible home environment. Dependent variables will be measured at baseline, 3-, and 6-months and analyzed with a linear mixed-effects model across all time points. DISCUSSION: For patients recovering from stroke, the use of TR to provide home assessments and follow-up training in prescribed equipment has the potential to effectively supplement existing home health services, assist transition to home and increase efficiency. This may be particularly relevant when patients live in remote locations, as is the case for many veterans. TRIAL REGISTRATION: Clinical Trials.gov Identifier: NCT00384748.

Institutional and ecological interplay for successful self-governance of community-based fisheries

The goal of this paper is to improve our understanding of the role of institutional arrangements and ecological factors that facilitate the emergence and sustainability of successful collective action in small-scale fishing social-ecological systems. Using a modified logistic growth function, we simulate how ecological factors (i.e. carrying capacity) affect small-scale fishing communities with varying degrees of institutional development (i.e. timeliness to adopt new institutions and the degree to which harvesting effort is reduced), in their ability to avoid overexploitation. Our results show that strong and timely institutions are necessary but not sufficient to maintain sustainable harvests over time. The sooner communities adopt institutions, and the stronger the institutions they adopt, the more likely they are to sustain the resource stock. Exactly how timely the institutions must be adopted, and by what amount harvesting effort must be diminished, depends on the ecological carrying capacity of the species at the particular location. Small differences in the carrying capacity between fishing sites, even under scenarios of similar institutional development, greatly affects the likelihood of effective collective action. © 2009 Elsevier B.V. All rights reserved.

The evolving capabilities of rhodopsin-based genetically encoded voltage indicators.

Protein engineering over the past four years has made rhodopsin-based genetically encoded voltage indicators a leading candidate to achieve the task of reporting action potentials from a population of genetically targeted neurons in vivo. Rational design and large-scale screening efforts have steadily improved the dynamic range and kinetics of the rhodopsin voltage-sensing domain, and coupling these rhodopsins to bright fluorescent proteins has supported bright fluorescence readout of the large and rapid rhodopsin voltage response. The rhodopsin-fluorescent protein fusions have the highest achieved signal-to-noise ratios for detecting action potentials in neuronal cultures to date, and have successfully reported single spike events in vivo. Given the rapid pace of current development, the genetically encoded voltage indicator class is nearing the goal of robust spike imaging during live-animal behavioral experiments.

A comparison of the effectiveness of the team-based learning readiness assessments completed at home to those completed in class.

PURPOSE: The readiness assurance process (RAP) of team-based learning (TBL) is an important element that ensures that students come prepared to learn. However, the RAP can use a significant amount of class time which could otherwise be used for application exercises. The authors administered the TBL-associated RAP in class or individual readiness assurance tests (iRATs) at home to compare medical student performance and learning preference for physiology content. METHODS: Using cross-over study design, the first year medical student TBL teams were divided into two groups. One group was administered iRATs and group readiness assurance tests (gRATs) consisting of physiology questions during scheduled class time. The other group was administered the same iRAT questions at home, and did not complete a gRAT. To compare effectiveness of the two administration methods, both groups completed the same 12-question physiology assessment during dedicated class time. Four weeks later, the entire process was repeated, with each group administered the RAP using the opposite method. RESULTS: The performance on the physiology assessment after at-home administration of the iRAT was equivalent to performance after traditional in-class administration of the RAP. In addition, a majority of students preferred the at-home method of administration and reported that the at-home method was more effective in helping them learn course content. CONCLUSION: The at-home administration of the iRAT proved effective. The at-home administration method is a promising alternative to conventional iRATs and gRATs with the goal of preserving valuable in-class time for TBL application exercises.

Busbar sizing modeling tools: comparing an ANSYS® based 3D model with the versatile 1D model part of MHD-Valdis

The main goal of a cell stability MHD model like MHD-Valdis is to help locate the busbars around the cell in a way which leads to the generation of a magnetic field inside the cell that itself leads to a stable cell operation. Yet as far as the cell stability is concerned, the uniformity of the current density in the metal pad is also extremely important and can only be achieved with a correct busbar network sizing. This work compares the usage of a detailed ANSYS based 3D thermo-electric model with the one of the versatile 1D part of MHD-Valdis to help design a well balanced busbar network.

Towards Cloud-Based Knowledge Capturing Based on Natural Language Processing

Paper presented at the Cloud Forward Conference 2015, October 6th-8th, Pisa