Showing papers on "Filter (video) published in 2010"

Smoothing algorithms for state–space models

Abstract: Two-filter smoothing is a principled approach for performing optimal smoothing in non-linear non-Gaussian state-space models where the smoothing dis- tributions are computed through the combination of 'forward' and 'backward' time filters. The 'forward' filter is the standard Bayesian filter but the 'backward' filter, generally referred to as the backward information filter, is not a probability measure on the space of the hidden Markov process. In cases where the backward information filter can be computed in closed form, this technical point is not important. However, forgeneralstate-spacemodelswherethereisnoclosedformexpression,thisprohibits the use of flexible numerical techniques such as Sequential Monte Carlo (SMC) to approximate the two-filter smoothing formula. We propose here a generalised two- filter smoothing formula which only requires approximating probability distributions and applies to any state-space model, removing the need to make restrictive assump- tions used in previous approaches to this problem. SMC algorithms are developed to implement this generalised recursion and we illustrate their performance on various problems.

Filter Optimization for Grid Interactive Voltage Source Inverters

Abstract: Higher order LCL filters are essential in meeting the interconnection standard requirement for grid-connected voltage source converters. LCL filters offer better harmonic attenuation and better efficiency at a smaller size when compared to the traditional L filters. The focus of this paper is to analyze the LCL filter design procedure from the point of view of power loss and efficiency. The IEEE 1547-2008 specifications for high-frequency current ripple are used as a major constraint early in the design to ensure that all subsequent optimizations are still compliant with the standards. Power loss in each individual filter component is calculated on a per-phase basis. The total inductance per unit of the LCL filter is varied, and LCL parameter values which give the highest efficiency while simultaneously meeting the stringent standard requirements are identified. The power loss and harmonic output spectrum of the grid-connected LCL filter is experimentally verified, and measurements confirm the predicted trends.

Expandable emboli filter and thrombectomy device

Abstract: Expandable emboli filter and thrombectomy devices adapted for use with microcatheters to remove debris from blood vessels. The devices embody expanded profiles that span the entirety of various sized target vessels and thus are particularly effective in the engagement of debris found in vessels.

View-based Maps

Abstract: Robotic systems that can create and use visual maps in real-time have obvious advantages in many applications, from automatic driving to mobile manipulation in the home. In this paper we describe a mapping system based on retaining stereo views of the environment that are collected as the robot moves. Connections among the views are formed by consistent geometric matching of their features. Out-of-sequence matching is the key problem: how to find connections from the current view to other corresponding views in the map. Our approach uses a vocabulary tree to propose candidate views, and a strong geometric filter to eliminate false positives: essentially, the robot continually re-recognizes where it is. We present experiments showing the utility of the approach on video data, including incremental map building in large indoor and outdoor environments, map building without localization, and re-localization when lost.

Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

Abstract: The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.

Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration

Abstract: One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluates the performances of adaptive Kalman filter methods with different adaptations. Innovation and residual based adaptive Kalman filters were employed for adapting R and Q. These methods were implemented in a loose GPS/INS integration system and tested using real data sets. Their performances have been evaluated and compared. Their limitations in real-life engineering applications are discussed.

A Kalman/Particle Filter-Based Position and Orientation Estimation Method Using a Position Sensor/Inertial Measurement Unit Hybrid System

Abstract: This paper presents a novel methodology that estimates position and orientation using one position sensor and one inertial measurement unit. The proposed method estimates orientation using a particle filter and estimates position and velocity using a Kalman filter (KF). In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results show that the orientation errors using the proposed method are significantly reduced compared to the orientation errors obtained from an extended Kalman filter (EKF) approach. The improved orientation estimation using the proposed method leads to better position estimation accuracy. This paper studies the effects of the number of particles of the proposed filter and position sensor noise on the orientation accuracy. Furthermore, the experimental results show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown.

A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments

Abstract: This paper presents a filter that is able to simultaneously estimate the behaviors of traffic participants and anticipate their future trajectories. This is achieved by recognizing the type of situation derived from the local situational context, which subsumes all information relevant for the drivers decision making. By explicitly taking into account the interactions between vehicles, it achieves a comprehensive situational understanding, inevitable for autonomous vehicles and driver assistance systems. This provides the necessary information for safe behavior decision making or motion planning. The filter is modeled as a Dynamic Bayesian Network. The factored state space, modeling the causal dependencies, allows to describe the models in a compact fashion and reduces the computational complexity of the inference process. The filter is evaluated in the context of a highway scenario, showing a good performance even with very noisy measurements. The presented framework is intended to be used in traffic environments but can be easily transferred to other robotic domains.

Linear Stabilization of a DC Bus Supplying a Constant Power Load: A General Design Approach

Abstract: In this paper, an oscillation compensation technique is proposed to improve the stability margin of an electrical system constituted by a dc power supply, an LC filter, and a constant power load. This is realized here by an actuator (inverter-permanent-magnet synchronous motor). To design the compensator, input impedance of the constant power load and output impedance of the filter are required and derived in this paper. To develop the load input impedance expression, small signal approximation is employed and all dynamics are taken into account except by the inverter ones only, which can often be neglected in practical applications. Then, the control structure of the whole system is slightly modified to implement the oscillation compensation block that increases the stability margin, and thus, permits to reduce the dc-link capacitance value. In this paper, the proposed method is applied to an actuator designed for aerospace applications. The influence of the actuator control parameters and the input filter parameters on the stability of the dc-link voltage is discussed. Simulations and experimentations confirm the validity of the proposed approach.

Regular expressions considered harmful in client-side XSS filters

Abstract: Cross-site scripting flaws have now surpassed buffer overflows as the world's most common publicly-reported security vulnerability. In recent years, browser vendors and researchers have tried to develop client-side filters to mitigate these attacks. We analyze the best existing filters and find them to be either unacceptably slow or easily circumvented. Worse, some of these filters could introduce vulnerabilities into sites that were previously bug-free. We propose a new filter design that achieves both high performance and high precision by blocking scripts after HTML parsing but before execution. Compared to previous approaches, our approach is faster, protects against more vulnerabilities, and is harder for attackers to abuse. We have contributed an implementation of our filter design to the WebKit open source rendering engine, and the filter is now enabled by default in the Google Chrome browser.

A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm

Abstract: A novel correlation based memetic framework (MA-C) which is a combination of genetic algorithm (GA) and local search (LS) using correlation based filter ranking is proposed in this paper. The local filter method used here fine-tunes the population of GA solutions by adding or deleting features based on Symmetrical Uncertainty (SU) measure. The focus here is on filter methods that are able to assess the goodness or ranking of the individual features. Empirical study of MA-C on several commonly used datasets from the large-scale Gene expression datasets indicates that it outperforms recent existing methods in the literature in terms of classification accuracy, selected feature size and efficiency. Further, we also investigate the balance between local and genetic search to maximize the search quality and efficiency of MA-C.

Guideline for a Simplified Differential-Mode EMI Filter Design

Abstract: The design of electromagnetic interference (EMI) input filters, needed for switched power converters to fulfill the regulatory standards, is typically associated with high development effort. This paper presents a guideline for a simplified differential-mode (DM) filter design. First, a procedure to estimate the required filter attenuation based on the total input rms current using only a few equations is given. Second, a volume optimization of the needed DM filter based on the previously calculated filter attenuation and volumetric component parameters is introduced. It is shown that a minimal volume can be found for a certain optimal number of filter stages. The considerations are exemplified for two single-phase power factor correction converters operated in continuous and discontinuous conduction modes, respectively. Finally, EMI measurements done with a 300-W power converter prototype prove the proposed filter design method.

The Dynamic Bloom Filters

Abstract: A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.

Embedded EZ-Source Inverters

Abstract: Z-source inverters are recent topological options proposed for buck-boost energy conversion with a number of possible voltage- and current-type circuitries already reported in the literature. Comparing them, a common feature noted is their inclusion of an LC impedance network, placed between the dc input source and inverter bridge. This impedance network allows the output end of a voltage-type Z-source inverter to be shorted for voltage boosting without causing a large current flow and the terminal current of a current-type inverter to be interrupted for current boosting without introducing overvoltage oscillations to the system. Therefore, Z-source inverters are, in effect, safer and less complex and can be implemented using only passive elements with no additional active semiconductor needed. Believing in the prospects of Z-source inverters, this paper contributes by introducing a new family of embedded EZ-source inverters that can produce the same gain as the Z-source inverters but with smoother and smaller current/voltage maintained across the dc input source and within the impedance network. These latter features are attained without using any additional passive filter, which surely is a favorable advantage, since an added filter will raise the system cost and, at times, can complicate the dynamic tuning and resonant consideration of the inverters. The same embedded concept can also be used for designing a full range of voltage- and current-type inverters with each of them tested experimentally using a number of scaled-down laboratory prototypes.

Balanced Bandpass Filters Using Center-Loaded Half-Wavelength Resonators

Abstract: This paper presents a balanced filter that is designed by using the center-loaded half-wavelength resonator. To demonstrate the design ideas, the differential- and common-mode equivalent half circuits are built and studied. The proposed technique is novel in the way that the common-mode suppression can be made independent of the differential-mode response in the balanced filter, making use of a center-loaded half-wavelength resonator. This is very important to simplify the design and tuning processes of the balanced filters. Making use of this feature, the resonator is not only utilized to design the balanced single-band bandpass filter but also the balanced dual-band bandpass one, possibly being the first to be presented. To demonstrate the design ideas, one single-band and two dual-band balanced filters are presented. The experimental results agree well with the theoretical predictions and simulations.

Tunable multimode-interference bandpass fiber filter

Abstract: We report on a wavelength-tunable filter based on multimode interference (MMI) effects. A typical MMI filter consists of a multimode fiber (MMF) spliced between two single-mode fibers (SMF). The peak wavelength response of the filter exhibits a linear dependence when the length of the MMF is modified. Therefore a capillary tube filled with refractive-index-matching liquid is used to effectively increase the length of the MMF, and thus wavelength tuning is achieved. Using this filter a ring-based tunable erbium-doped fiber laser is demonstrated with a tunability of 30 nm, covering the full C-band.

Improved SMC implementation of the PHD filter

Abstract: The paper makes two contributions. First, a new formulation of the PHD filter which distinguishes between persistent and newborn objects is presented. This formulation results in an efficient sequential Monte Carlo (SMC) implementation of the PHD filter, where the placement of newborn object particles is determined by the measurements. The second contribution is a novel method for the state and error estimation from an SMC implementation of the PHD filter. Instead of clustering the particles in an ad-hoc manner after the update step (which is the current approach), we perform state estimation and, if required, particle clustering, within the update step in an exact and principled manner. Numerical simulations indicate a significant improvement in the estimation accuracy of the proposed SMC-PHD filter.

Poisson Point Processes: Imaging, Tracking, and Sensing

Abstract: : Multitarget tracking intensity filters are closely related to imaging problems, especially PET imaging. The intensity filter is obtained by three different methods. One is a Bayesian derivation involving target prediction and information updating. The second approach is a simple, compelling, and insightful intuitive argument. The third is a straightforward application of the Shepp-Vardi algorithm. The intensity filter is developed on an augmented target state space. The PHD filter is obtained from the intensity filter by substituting assumed known target birth and measurement clutter intensities for the intensity filter's predicted target birth and clutter intensities. To accommodate heterogeneous targets and sensor measurement models, a parameterized intensity filter is developed using a marked PPP Gaussian sum model. Particle and Gaussian sum implementations of intensity filters are reviewed. Mean-shift algorithms are discussed as a way to extract target state estimates. Grenander's method of sieves is discussed for regularization of the multitarget intensity filter estimates. Sources of error in the estimated target count are discussed. Finally, the multisensor intensity filter is developed using the same PPP target models as in the single sensor filter. Both homogeneous and heterogeneous multisensor fields are discussed. Multisensor intensity filters reduce the variance of estimated target count by averaging.

Dual-Band and Wide-Stopband Single-Band Balanced Bandpass Filters With High Selectivity and Common-Mode Suppression

Abstract: Novel dual-band and wide-stopband single-band balanced bandpass filters with high selectivity and common-mode suppression are presented in this paper. Stepped-impedance resonators (SIRs) are usually used for designing dual-band bandpass filters; however, they have a strong common-mode response when designing a balanced filter. To suppress the common-mode signal, a half-wavelength SIR loaded by a capacitor or a resistor has been introduced and theoretically analyzed. It is found that the capacitor can minimize the common-mode external quality factor, and the resistor can reduce the common-mode unloaded quality factor. With the use of this property, the common-mode response can be suppressed, whereas the differential-mode response is almost unaffected. This property can be easily verified by comparing the results of the balanced bandpass filters with and without loaded elements. To demonstrate the design idea, one balanced dual-band bandpass filter operating at 2.4 and 5 GHz and another balanced single-band bandpass filter with a wide stopband are designed. It was found that the common-mode suppression level of both filters can be greatly improved, and high selectivity is obtained by giving two differential-mode coupling paths.

Improved adaptive complex diffusion despeckling filter

Abstract: Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.

Novel Balanced Dual-Band Bandpass Filter Using Coupled Stepped-Impedance Resonators

Abstract: A novel balanced dual-band bandpass filter, using coupled stepped-impedance resonators (SIRs), is proposed in this letter. The differential- and common-mode equivalent half circuits are given. By properly designing the resonators, associated with the filter composed of eight SIRs, dual-band differential-mode bandpass response can be obtained. To improve the common-mode suppression level, two open stubs with different lengths are added to the connection point of two resonators. A prototype balanced filter operating at 2.4 and 5 GHz has been realized to validate the proposed concept and theory.

SieveStore: a highly-selective, ensemble-level disk cache for cost-performance

Abstract: Emerging solid-state storage media can significantly improve storage performance and energy. However, the high cost-per-byte of solid-state media has hindered wide-spread adoption in servers. This paper proposes a new, cost-effective architecture - SieveStore - which enables the use of solid-state media to significantly filter access to storage ensembles. Our paper makes three key contributions. First, we make a case for highly-selective, storage-ensemble-level disk-block caching based on the highly-skewed block popularity distribution and based on the dynamic nature of the popular block set. Second, we identify the problem of allocation-writes and show that selective cache allocation to reduce allocation-writes - sieving - is fundamental to enable efficient ensemble-level disk-caching. Third, we propose two practical variants of SieveStore. Based on week-long block access traces from a storage ensemble of 13 servers, we find that the two components (sieving and ensemble-level caching) each contribute to SieveStore's cost-effectiveness. Compared to unsieved, ensemble-level disk-caches, SieveStore achieves significantly higher hit ratios (35%-50% more, on average) while using only 1/7th the number of SSD drives. Further, ensemble-level caching is strictly better in cost-performance compared to per-server caching.

Novel Voltage-Mode All-Pass Filter Based on Using DVCCs

Abstract: In this paper, a novel design for realizing a voltage-mode (VM) all-pass filter utilizing two differential voltage current conveyors (DVCCs) is proposed. Also, the suggested filter uses a canonical number of passive elements (one grounded capacitor and one resistor) without requiring any element matching condition. The proposed filter has high input and low output impedances, which make it suitable for cascading. The effects of the nonidealities of the DVCCs on the proposed design are investigated. As an application, a quadrature oscillator is designed using the proposed VM all-pass filter and an integrator. The proposed filter and oscillator circuits are simulated using the SPICE simulation program to confirm the theory.

Electronic cigarette with atomizer incorporated in the false filter

Abstract: Electronic cigarette provided with an atomizer placed inside the false filter, that filter being realized in soft material.

Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation

Abstract: This paper deals with the problem of state estimation for the integration of an inertial navigation system (INS) and Global Positioning System (GPS). For a nonlinear system that has the model error and white Gaussian noise, a predictive filter (PF) is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) is proposed and is called predictive iterated Kalman filter (PIKF). The basic idea of the PIKF is to compensate the state estimate by the estimated model error. An INS/GPS integration system is implemented using the PIKF and applied to synthetic aperture radar (SAR) motion compensation. Through flight tests, it is shown that the PIKF has an obvious accuracy advantage over the IEKF and unscented Kalman filter (UKF) in velocity.

Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks

Abstract: Indoor positioning is an enabling technology for delivery of location-based services in mobile computing environments. This paper proposes a positioning solution using received signal strength in indoor wireless local area networks. In this application, an explicit measurement equation and the corresponding noise statistics are unknown because of the complexity of the indoor propagation channel. To address these challenges, we introduce a new state-space Bayesian filter: the nonparametric information (NI) filter. This filter effectively tracks motion in situations where the Kalman filter and its variants are inapplicable, while maintaining a computational complexity comparable to that of the Kalman filter. To deal with the noisy nature of the indoor propagation environment, the NI filter is used in the design of an intelligent dynamic WLAN tracking system. The system anticipates future position values and adapts its sensing and estimation parameters accordingly. Our experimental results conducted on measurements from a real office environment indicate that the combination of the intelligent design and the NI filter results in significant improvements over the Kalman and particle filters.

Linear Programming Algorithms for Sparse Filter Design

Abstract: In designing discrete-time filters, the length of the impulse response is often used as an indication of computational cost. In systems where the complexity is dominated by arithmetic operations, the number of nonzero coefficients in the impulse response may be a more appropriate metric to consider instead, and computational savings are realized by omitting arithmetic operations associated with zero-valued coefficients. This metric is particularly relevant to the design of sensor arrays, where a set of array weights with many zero-valued entries allows for the elimination of physical array elements, resulting in a reduction of data acquisition and communication costs. However, designing a filter with the fewest number of nonzero coefficients subject to a set of frequency-domain constraints is a computationally difficult optimization problem. This paper describes several approximate polynomial-time algorithms that use linear programming to design filters having a small number of nonzero coefficients, i.e., filters that are sparse. Specifically, we present two approaches that have different computational complexities in terms of the number of required linear programs. The first technique iteratively thins the impulse response of a non-sparse filter until frequency-domain constraints are violated. The second minimizes the 1-norm of the impulse response of the filter, using the resulting design to determine the coefficients that are constrained to zero in a subsequent re-optimization stage. The algorithms are evaluated within the contexts of array design and acoustic equalization.

Performance Improvement of LCL -Filter-Based Grid-Connected Inverters Using PQR Power Transformation

Abstract: The demand for three-phase pulsewidth modulation (PWM) inverters in applications such as power control or grid connecting has been on the increase in recent years. Such inverters are connected to the grid via an L filter or an LCL filter to reduce the harmonics caused by the switching. An LCL filter can reduce the harmonics induced by low switching frequency and generates a satisfactory level of grid-side current using a relatively low inductance, as compared to an L filter. The additional poles introduced by the LC part induces resonance in the system, leading to stability problems; this paper presents a compensation method using power theory to improve these issues, so that the performance of the designed LCL filter system can be improved. The effectiveness of the proposed algorithm is verified by simulations and experiments.

Robust fault detection for continuous-time switched delay systems: an linear matrix inequality approach

Abstract: This study addresses the robust fault detection problem for continuous-time switched systems with state delays. The fault detection filter is used as the residual generator depending on the system mode. Attention is focused on designing the filter such that, for the modelling errors, the unknown inputs and the control one, the error between the residuals and the faults is minimised. The addressed fault detection filter design is converted into an auxiliary H ? filtering problem. By using the Lyapunov-Krasovskii functional method and average dwell time approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities (LMIs). Two examples are provided to demonstrate the effectiveness of the proposed method.