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Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of −0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE.

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years. [ABSTRACT FROM AUTHOR]

Intelligent Tutoring Systems are promising tools for delivering optimal and personalized learning experiences to students. A key component for their personalization is the student model, which infers the knowledge level of the students to balance the difficulty of the exercises. While important advances have been achieved, several challenges remain. In particular, the models should be able to track in real-time the evolution of the students’ knowledge levels. These evolutions are likely to follow different profiles for each student, while measuring the exact knowledge level remains difficult given the limited and noisy information provided by the interactions. This paper introduces a novel model that addresses these challenges with three contributions: 1) the model relies on Gaussian Processes to track online the evolution of the student's knowledge level over time, 2) it uses collaborative filtering to rapidly provide long-term predictions by leveraging the information from previous users, and 3) it automatically generates abstract representations of knowledge components via automatic relevance determination of covariance matrices. The model is evaluated on three datasets, including real users. The results demonstrate that the model converges to accurate predictions in average four times faster than the compared methods. [ABSTRACT FROM AUTHOR]

Recommendation algorithms, such as Neighborhood-based Collaborative- Filtering (CF), have been widely applied in various emerging machine learning applications. However, under the circumstance of the explosive big data, it poses significant challenges to CF recommendation algorithms as it is becoming quite time and energy-consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. To solve these problems, in this article, we propose WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA. It is able to accommodate three types of CF recommendation algorithms, including User-based CF, Item-based CF, and SlopeOne recommendations algorithms, with five different similarity analysis metrics including Jaccard, Cosine, CosineIR, euclidean, and Pearson. To maintain flexibility for these different CF algorithms and metrics, we adopt custom instruction sets to manipulate the learning and prediction accelerators. We implement a hardware prototype on a real Xilinx Zynq FPGA development board. Experimental results show that the proposed learning and prediction accelerators can achieve 8.0X speedup and 1.7X speedup compared with an Intel i7 processor respectively. The accelerator has the energy benefits of up to 137.4X compared with an NVIDIA Tesla K40C GPU, with the affordable hardware cost. [ABSTRACT FROM AUTHOR]

In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users’ preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items’ attribute information. Specifically, we design useful user selection criteria based on items’ attributes and users’ rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users’ previous ratings and items’ attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods. [ABSTRACT FROM AUTHOR]

Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on the sparse Bayesian learning (SBL) framework. The key idea underlying the algorithm, termed SBL-DF, is the incorporation of a signal prediction generated from a dynamics model and estimates of previous time steps into the hyperpriors of the SBL probability model. The proposed algorithm is online, robust to imperfect dynamics models (due to the propagation of dynamics information through higher-order statistics), robust to certain undesirable dictionary properties such as coherence (due to properties of the SBL framework), allows the use of arbitrary dynamics models, and requires the tuning of fewer parameters than many other dynamic filtering algorithms do. We also extend the fast marginal likelihood SBL inference procedure to the informative hyperprior setting to create a particularly efficient version of the SBL-DF algorithm. Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms. In particular, we show that SBL-DF outperforms state of the art algorithms when the dictionary contains the challenging coherence and column scaling structure found in many practical applications. [ABSTRACT FROM AUTHOR]

This paper considers the problem of distributed ${H} _{\infty }$ filtering for a class of switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching due to practical reasons. The possibility of randomly occurred sensor nonlinearities is described by a Bernoulli stochastic variable, and the asynchronous switching filtering means that the mode of the plant is different from the mode of the designed filter possibly. A distributed filtering network is used to estimate the system state instead of a filter to improve reliability in case of faults of the filter. A distributed mode-dependent filter is designed by constructing a unified mode-dependent Lyapunov function and solving a set of linear matrix inequalities. Some novel sufficient conditions are obtained by using the average dwell time switching mechanism such that the augmented filtering error system is stochastically exponentially stable and achieves a prescribed ${H} _{\infty }$ disturbance attention index. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed designed method. [ABSTRACT FROM AUTHOR]

In this paper, the filtering problem is investigated for a class of discrete-time Markov jump linear parameter varying systems with packet dropouts and channel noises in the network surroundings. The partial accessibility of system modes with respect to the designed filter is described by a hidden Markov model (HMM). A typical behavior characterization mechanism is proposed in the communication channel including data losses and additive noises, which occurs in a probabilistic way based on two mutually independent Bernoulli sequences. With the aid of a class of Lyapunov function subject to parameter-dependent and mode-dependent constraints, sufficient conditions ensuring the existence of HMM-based filters are obtained such that the filtering error system is stochastically stable with a guaranteed ${\mathcal {H}_{\infty }}$ error performance. The influence of monotonicity on the performance index is explored while changing the degree of both additive noise and mode inaccessibility. The effectiveness and applicability of the obtained results are finally verified by two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, the generalized ${\mathcal {H}_{2}}$ filter design problems are addressed for a class of nonlinear discrete-time systems with measurement quantization. The considered nonlinear system is represented by Takagi–Sugeno fuzzy model and the system measurement output is quantized by a dynamic quantizer constituted by a static quantizer and a dynamic parameter before it is transmitted to the filter. The attention is focused on the design of both full- and reduced-order filters and the quantizer dynamic parameter such that the quantized filtering error systems are asymptotically stable with prescribed generalized ${\mathcal {H}_{2}}$ performances. Superior to existing results on the quantized filtering design, the proposed one is given under a unified linear matrix inequality (LMI) characterization, it is shown that the design problem can be solved if the LMIs conditions are feasible. Finally, simulation examples will be exploited to illustrate the effectiveness of the developed quantized generalized ${\mathcal {H}_{2}}$ filtering methods. [ABSTRACT FROM AUTHOR]

In this paper, the distributed ${H_{\infty }}$ state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal ${H_{\infty }}$ disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures. [ABSTRACT FROM PUBLISHER]

Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multiview representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multiview sparse dictionary selection with centroid co-regularization method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. We also introduce a diversity regularizer to favor a selection of diverse representatives. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm. Experiments on synthetic data and benchmark video datasets validate the effectiveness of the proposed approach for video summarization, in comparison with other video summarization methods and representative selection methods such as K-medoids, sparse dictionary selection, and multiview clustering. [ABSTRACT FROM PUBLISHER]

Median filtering is a smoothing technique for noise removal in images. While there are various implementations of median filtering for a single-core CPU, there are few implementations for accelerators and multi-core systems. Many parallel implementations of median filtering use a sorting algorithm for rearranging the values within a filtering window and taking the median of the sorted value. While using sorting algorithms allows for simple parallel implementations, the cost of the sorting becomes prohibitive as the filtering windows grow. This makes such algorithms, sequential and parallel alike, inefficient. In this work, we introduce the first software parallel median filtering that is non-sorting-based. The new algorithm uses efficient histogram-based operations. These reduce the computational requirements of the new algorithm while also accessing the image fewer times. We show an implementation of our algorithm for both the CPU and NVIDIA’s CUDA supported graphics processing unit (GPU). The new algorithm is compared with several other leading CPU and GPU implementations. The CPU implementation has near perfect linear scaling with a $3.7\times $ speedup on a quad-core system. The GPU implementation is several orders of magnitude faster than the other GPU implementations for mid-size median filters. For small kernels, $3 \times 3$ and $5 \times 5$ , comparison-based approaches are preferable as fewer operations are required. Lastly, the new algorithm is open-source and can be found in the OpenCV library. [ABSTRACT FROM AUTHOR]

We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition; and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries. The shift-invariance property is directly modeled into the objective function for learning filters. Extensive experiments show that the proposed methods perform favorably compared with state-of-the-art image separation methods. [ABSTRACT FROM PUBLISHER]

Cloud applications built on service-oriented architectures generally integrate a number of component services to fulfill certain application logic. The changing cloud environment highlights the need for these applications to keep resilient against QoS variations of their component services so that end-to-end quality-of-service (QoS) can be guaranteed. Runtime service adaptation is a key technique to achieve this goal. To support timely and accurate adaptation decisions, effective and efficient QoS prediction is needed to obtain real-time QoS information of component services. However, current research has focused mostly on QoS prediction of working services that are being used by a cloud application, but little on predicting QoS values of candidate services that are equally important in determining optimal adaptation actions. In this paper, we propose an adaptive matrix factorization (namely AMF) approach to perform online QoS prediction for candidate services. AMF is inspired from the widely-used collaborative filtering techniques in recommender systems, but significantly extends the conventional matrix factorization model with new techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments, as well as a case study, have been conducted based on a real-world QoS dataset of Web services (with over 40 million QoS records). The evaluation results demonstrate AMF’s superiority in achieving accuracy, efficiency, and robustness, which are essential to enable optimal runtime service adaptation. [ABSTRACT FROM PUBLISHER]

Lebesgue-sampling-based fault diagnosis and prognosis (LS-FDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the parameters and uncertainties in the diagnostic and prognostic models. To improve performance of LS-FDP, this paper introduces an online model parameter adaptation scheme, which is realized by a recursive least square method with a forgetting factor. In addition, uncertainty of remaining useful life (RUL) prediction is managed by adjusting the model noises through a short-term prediction and correction loop. To verify the proposed parameter adaptation and noise adjustment methods, they are designed and implemented in a particle-filtering-based LS-FDP algorithm with applications to Li-ion batteries. Experimental results show that the proposed approach has significant improvement on both battery capacity estimation and RUL prediction. [ABSTRACT FROM PUBLISHER]

In this paper, a new class of filtering differential phase shifters is proposed and developed, which can provide constant phase shift and self-embedded filtering function at the same time. In contrast to its traditional counterparts, the proposed phase shifter consists of two bandpass filter (BPF) branches and the phase properties of generalized n th-order BPF network are systematically studied at first time. With derived closed-form formula of phase slope, the synthesis method is presented to design the proposed filtering differential phase shifters with prescribed arbitrary phase shift value, passband ripple, and bandwidth. The filter order, which dominates frequency selectivity, is considered in the synthesis design of proposed phase shifter as well. The tradeoff between the frequency selectivity and phase shift is discussed. In addition to the advanced features of multifunction and simple geometry, the proposed filtering differential phase shifters can achieve compact size, low amplitude imbalance, and multiway polyphase capability. To validate the proposed concept and synthesis method, two filtering differential phase shifters with single-phase (90°) and five-way polyphase (45°, 90°, 135°, and 180°) are designed, fabricated, and measured. The simulated and measured results coincide well with the prescribed performances in phase shift and magnitude. [ABSTRACT FROM PUBLISHER]

Frequency-domain multiplexing was proposed to reduce the number of signals to be digitized in vector control using a resolver as position sensor. Signals from resolver and current sensors can be multiplexed through analog adders. This approach has many advantages over using analog switches. On the other hand, the conventional demultiplexing system involves filtering and delay compensation, while the rotor angle is obtained after the signal demultiplexing. However, this conventional approach is difficult to implement because the required filters must have good stopband rejection, without adding a significant phase delay. This paper proposes an improved demultiplexing system whose structure is simpler than the conventional approach. A type-III angle tracking observer estimates the rotor angle and the resolver outputs directly from the multiplexed signals. The estimated resolver outputs and stopband filters are used to get the current signals. Thus, the demultiplexing of resolver signals and the delay compensators used in the conventional approach were eliminated. Experimental results prove that the proposed approach gives accurate estimations of the rotor angle and current signals. Besides, conditions for the resolver excitation frequency, the inverter switching frequency, and for current synchronous sampling were defined for the application of frequency-domain multiplexing. [ABSTRACT FROM AUTHOR]

Much work has focused on automatically constructing conceptual taxonomies or semantic networks from large text corpora. In this paper, we use a state-of-the-art data-driven conceptual taxonomy, Probase, to show that missing links in taxonomies are the chief problem that hinders their adoption by many real life applications, for the missing links break the inferencing that the conceptual taxonomy claims to support. To solve this problem, we devise a collaborative filtering framework to infer missing links in taxonomies derived from text corpora. We implement our method mainly on Probase, creating a denser taxonomy containing 5.1 million (about 30 percent) more isA relationships, with an accuracy of above 90 percent. We conduct comprehensive experiments to demonstrate the quality of the revised conceptual taxonomies. [ABSTRACT FROM PUBLISHER]

This paper discusses how to solve a filtering problem for a class of continuous nonlinear time-varying systems via the Duncan–Mortensen–Zakai (DMZ) equation. In this paper, the original DMZ equation is changed into the Kolmogorov forward equation (KFE) by exponential transformations in each time interval, and then, under some assumptions, the KFE can be transformed into a time-varying Schrödinger equation, which can be solved explicitly. The novelty of this paper lies in how to transform the KFE into the Schrödinger equation. As a direct application, the results of the paper “Nonlinear filtering and time varying Schrodinger equation” are extended for time-varying Yau systems. [ABSTRACT FROM PUBLISHER]

The rapid growth of cloud services dictates increasingly powerful datacenters to maintain the high quality of service (QoS). It's a common practice in virtually all tiers of datacenters to continuously upgrade the datacenters, i.e. replacing outdated and failed servers with more advanced and efficient ones. However, how to upgrade a datacenter in the most cost-efficient strategy remains unclear, and however this problem goes increasingly challenging given the great diversity of applications. In practice, the datacenters’ operators usually resort to expending the scale of servers. The preferred servers are either expensive but high-performance, or, by contrast, cheap but low-power. Whatever sever preferences, how to justify the cost-efficiency is still an open problem. We claim that a cost-efficient upgrading strategy should be fully aware of not only the capacity and cost of various servers, but also the resource demands of target applications. We model this strategy as a recommendation problem: recommending the “best” servers to a datacenter. We propose “EcoUp”, a model-based framework that faithfully rates the cost efficiency of server candidates, relying on which an optimal server portfolio can be derived. The performance prediction on candidate servers is realized by employing a sophisticated latent factor model (LFM). The cost mainly involves the server purchasing cost and energy bill. Given the application distribution, EcoUp can give an optimal server portfolio under a certain capital budget. We use Google trace, a big profiling dataset opened by Google, to validate the performance prediction. Experimental results show that the error rate is below 8 percent on average. Meanwhile, we build a comprehensive upgrading procedure on a local cluster to evaluate the potential of EcoUp. The results show that our approach significantly outperforms two conventional upgrading strategies by 12.3 and 33.6 percent in terms of system throughput, respectively. [ABSTRACT FROM AUTHOR]

The snoopy-based protocol is a widely used cache coherence mechanism for a symmetric multiprocessor (SMP) system. However, this broadcast-based protocol blindly disseminates data sharing information across the system, and introduces many unnecessary data operations. This paper proposes a novel architecture of double-layer counting Bloom filter (DLCBF) to reduce the unnecessary data lookups on the local cache and redundant data transactions on the shared interconnection of an SMP system. By adding an extra filtering layer, the DLCBF effectively exploits the data locality of applications. The two-layer hierarchy reduces the storage size of DLCBF by 18.75%, and achieves 81.99% and 31.36% better filtering rates when compared with a classic Bloom filter (BF) and original counting BF, respectively. When applied on the segmented shared bus of an SMP system, the DLCBF outperforms the previous work by 58% for In-filters and $1.86\times $ for Out-filters. This paper also comprehensively explores the key design parameters of DLCBF, including the sizes of top-layer, bottom-layer, and multilayer design. The results show that enlarging the layer filters enhance the filtering rates of DLCBF, while adding an extra filter layer only provides slight benefit. [ABSTRACT FROM PUBLISHER]

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation. [ABSTRACT FROM PUBLISHER]

A number of integrated broadcast-broadband services have recently been launched which allow the viewers to receive the content via the airwaves together with additional information about the content via the Internet. By providing personal preferences viewers can expect more attractive and personalized services. Viewers would be only willing to share their information with providers that they trust. On the other hand, the viewers would like to distribute their preferences as wide as possible so that they can enjoy more interesting and diverse services. In this paper, a privacy preserving system for integrated broadcast-broadband service is proposed. The system allows viewers to share their personal information with service providers that satisfy certain criteria (possess certain attributes). An implementation of the system using a previously proposed attribute-based encryption scheme is given and the computation cost of the system is examined in practice. Using the system, a viewer can safely receive personalized services from a large number of service providers. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonparametric approach toward delayed-state filtering for cooperative tracking. Standard parametric cooperative localization/tracking approaches are generally aimed at problems that can be easily parameterized and/or are limited to incorporate only real-time measurements. This paper provides a nonparametric yet computationally tractable alternative that is suitable for tracking cases where real-time observations are not always possible, e.g., in a sparse mesh network. The proposed delayed-state cooperative particle filter features forward filtering and backward smoothing to incorporate measurements that are received with time delays. A record of historical marginal states is kept for each mobile node within a sliding time window, instead of the high-dimensional joint state. Essentially, it replaces the importance sampling in traditional particle filters by a Gibbs sampler, which is a Markov chain Monte Carlo method, to fuse all available egocentric and internode relative observations into the global position estimate, thus alleviating the high-dimensionality problems in cooperative tracking. The performance of the proposed approach is evaluated in a multiagent simulation, and experimental results from a large-scale multivehicle industrial operation clearly demonstrate that the proposed approach effectively facilitates the tracking of mobile nodes without position awareness, through the use of relative range, negative detection, and time-delayed measurements. [ABSTRACT FROM PUBLISHER]

For synchronization applications, synchronous reference frame (SRF) phase-locked loop (PLL) is widely deployed. Its performance is excellent when the input voltage consists of only fundamental positive sequence (FPS) component. If the grid voltage is unbalanced or polluted with harmonics and dc offset, its performance degrades. Many modifications were proposed to address this issue. However, the removal of dc offset and fundamental negative sequence (FNS) component without compromising the dynamic performance still remains a challenging task. To this end, this paper presents a rapid Type-1 SRF PLL scheme with preloop filtering stage for tracking the attributes of grid voltage FPS component. Fixed sampling period sliding discrete Fourier transform (SDFT) and instantaneous symmetrical components method are employed in the preloop stage. With this modification, the dc offset, harmonics and the FNS component are rejected and only the FPS component enters the PLL. As a result, transients vanish quickly. However, when the grid frequency drifts, SDFT causes amplitude and phase errors, and Type-1 PLL introduces a steady-state tracking error in phase. These errors are compensated with the help of an error correction criteria. Robustness and the improved transient response of the proposed scheme are demonstrated with an experimental study involving real-time controller board (dSPACE DS1104) and three-phase programmable power source. [ABSTRACT FROM PUBLISHER]

Network reachability is an important characteristic for understanding end-to-end network behavior and helps in detecting violations of security policies across the network. While quantifying network reachability within one administrative domain is a difficult problem in itself, performing the same computation across a network spanning multiple administrative domains presents a novel challenge. The problem of quantifying network reachability across multiple administrative domains is more difficult because the privacy of security policies of individual domains is a serious concern and needs to be protected through this process. In this paper, we propose the first cross-domain privacy-preserving protocol for quantifying network reachability. Our protocol constructs equivalent representations of the Access Control List (ACL) rules and determines network reachability while preserving the privacy of the individual ACLs. This protocol can accurately determine the network reachability along a network path through different administrative domains. We have implemented and evaluated our protocol on both real and synthetic ACLs. The experimental results show that the online processing time of an ACL containing thousands of rules is less than 25 s. Given two ACLs, each containing thousands of rules, the comparison time is less than 6 s, and the total communication cost is less than 2100 kB. [ABSTRACT FROM PUBLISHER]

A sub-threshold design could provide a compelling approach to power critical applications. An exponential relationship exists, however, between the delay and the threshold voltage, that makes this design-time timing closure extremely difficult, if not impossible, to achieve. Several previous studies were focused on the technique of body biasing during post-silicon tuning for delay compensation. But they were mostly for super-threshold designs where spatially correlated L \mathbf {eff} variation dominates. They cannot be applied directly to sub-threshold designs in which purely random threshold voltage variations dominate. These works also assumed multiple body biasing voltage domains and multiple body biasing voltage levels, which involve significant design overhead. The problem of selective body biasing for post-silicon tuning of sub-threshold designs is examined in this paper. The possibility of using only one body bias voltage domain with a single body bias voltage is explored. The problem was formulated first as a linearly constrained statistical optimization model. The adaptive filtering concept from the signal processing community was then adopted so that an efficient, yet novel, solution could be developed. Using several 65 nm industrial designs, experimental results suggest that, compared with a seemingly more intuitive approach, the proposed approach can improve the pass rate by 57% on average with similar standby power and the same number of body biasing gates. This approach can reduce the standby power, on average, by 84%, with a 20% pass rate loss, more than the approach to bias all the gates. [ABSTRACT FROM PUBLISHER]

We propose the computation of a perceptual motion blur in videos. Our technique takes the predicted eye motion into account when watching the video. Compared to traditional motion blur recorded by a video camera our approach results in a perceptual blur that is closer to reality. This postprocess can also be used to simulate different shutter effects or for other artistic purposes. It handles real and artificial video input, is easy to compute and has a low additional cost for rendered content. We illustrate its advantages in a user study using eye tracking. [ABSTRACT FROM AUTHOR]

Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging. [ABSTRACT FROM AUTHOR]

Applying non-linear transfer functions and look-up tables to procedural functions (such as noise), surface attributes, or even surface geometry are common strategies used to enhance visual detail. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient transfer function filtering remains an open problem for several reasons: transfer functions are complex and non-linear, especially when mapped through procedural noise and/or geometry-dependent functions, and the effects of perspective and masking further complicate the filtering over a pixel’s footprint. We accurately solve this problem by computing and sampling from specialized filtering distributions on the fly, yielding very fast performance. We investigate the case where the transfer function to filter is a color map applied to (macroscale) surface textures (like noise), as well as color maps applied according to (microscale) geometric details. We introduce a novel representation of a (potentially modulated) color map’s distribution over pixel footprints using Gaussian statistics and, in the more complex case of high-resolution color mapped microsurface details, our filtering is view- and light-dependent, and capable of correctly handling masking and occlusion effects. Our approach can be generalized to filter other physical-based rendering quantities. We propose an application to shading with irradiance environment maps over large terrains. Our framework is also compatible with the case of transfer functions used to warp surface geometry, as long as the transformations can be represented with Gaussian statistics, leading to proper view- and light-dependent filtering results. Our results match ground truth and our solution is well suited to real-time applications, requires only a few lines of shader code (provided in supplemental material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.102), is high performance, and has a negligible memory footprint. [ABSTRACT FROM AUTHOR]

This paper presents a new bilateral filtering method specially designed for practical stereo vision systems. Parallel algorithms are preferred in these systems due to the real-time performance requirement. Edge-preserving filters like the bilateral filter have been demonstrated to be very effective for high-quality local stereo matching. A hardware-efficient bilateral filter is thus proposed in this paper. When moved to an NVIDIA GeForce GTX 580 GPU, it can process a one megapixel color image at around 417 frames per second. This filter can be directly used for cost aggregation required in any local stereo matching algorithm. Quantitative evaluation shows that it outperforms all the other local stereo methods both in terms of accuracy and speed on Middlebury benchmark. It ranks 12th out of over 120 methods on Middlebury data sets, and the average runtime (including the matching cost computation, occlusion handling, and post processing) is only 15 milliseconds (67 frames per second). [ABSTRACT FROM PUBLISHER]

As the number of cores increases exponentially on a single chip, the design and integration of both the on-chip network facilitating intercore communication, and the cache coherence protocol for enabling shared memory programming have become critical for improved energy-efficiency and overall chip performance. With traditional metal interconnects facing stringent energy constraints, researchers are currently pursuing disruptive solutions such as nanophotonics for improved energy-efficiency. Cache coherence in multicores can be enforced effectively by snoopy protocols; however, broadcasting every cache miss can limit the scalability while consuming excess energy. In this paper, we propose PULSE, a nanophotonic broadcast tree-based network for snoopy cache coherent multicores. To limit the energy-penalty from broadcasting (and thereby splitting) optical signals, we direct the optical signal from the external laser such that only the subset of requesters can receive the optical signal. Furthermore, as cache blocks are shared by a few cores, we propose a multicast version of PULSE called multi-PULSE that predicts the sharers' for each L2 miss and morphing the broadcast to a multicast network. We evaluate the energy and performance using CACTI and SIMICS on 16-core and 64-core versions of PULSE and multi-PULSE for Splash-2, PARSEC, and SPEC CPU2006 benchmarks and compare to electrical networks, optical networks, and another cache filtering techniques. Our results indicate that PULSE outperforms competitive electrical/optical networks by 60 percent in terms of execution time, and multi-PULSE reduces average energy from 10 to 80 percent even with a few mispredictions. [ABSTRACT FROM AUTHOR]

The design of planar sharp-rejection low-pass and bandpass filters (LPFs and BPFs) featuring a very broad stopband is addressed in this paper. For the LPF case, two new classes of bandstop signal-interference transversal filtering section (TFS) consisting of power directional couplers in reflection mode are proposed. In the BPF application, additional short-circuit stubs are inserted at the TFS input/output accesses. Based on these filtering cells, the devised LPF and BPF approaches are then derived from the cascade of multiple TFSs with frequency-contiguous stopbands. Thus, an overall attenuated band showing an ultra-large spectral width and deep rejection levels through the generation of several transmission zeros (TZs) is synthesized. Furthermore, such unprecedented filtering performances are obtained for much lower circuit complexity than available solutions. The operating principle of the conceived LPF and BPF topologies and guidelines for their realization are detailed. This includes formulas for their analytically designable TZs. For practical validation, three LPF prototypes and one wideband BPF circuit are also fabricated in microstrip technology and tested. [ABSTRACT FROM AUTHOR]

Bloom filter based multicast has recently been proposed as a promising methodology for scalable multicasting. The basic idea underpinning this family of multicast mechanisms is to encode the multicast routing information into a Bloom filter carried in the packet. Thus, the routers are relieved from maintaining per-group forwarding states and able to support multicasting with improved scalability, compared to traditional IP multicast. In this article, we first review the evolution path of multicast protocols to shed light on the impetus that had driven the multicasting technology forward. We then discuss several representative Bloom filter based multicast protocols, with revealing their core design issues. While multicast is normally considered the most efficient way for delivering multimedia services, how to fully leverage the advantages of Bloom filter based multicast protocols to facilitate multimedia delivery is still an open issue. In this regard, we present a design that exploits a Bloom-filter based multicast protocol to accelerate the channel zapping in an IPTV system. Moreover, we point out the open research issues hoping to promote new development in the field. [ABSTRACT FROM PUBLISHER]

In this paper, we study the set reconciliation problem, in which each member of a node pair has a set of objects and seeks to deliver its unique objects to the other member. How could each node compute the set difference, however, is challenging in the set reconciliation problem. To address such an issue, we propose a lightweight but efficient method that only requires the pair of nodes to represent objects using a counting Bloom filter (CBF) of size $(O(d))$ and exchange with each other, where $(d)$ denotes the total size of the set differences. A receiving node then subtracts the received CBF from its local one via minus operation proposed in this paper. The resultant CBF can approximately represent the union of the set differences and thus the set difference to each node can be identified after querying the resultant CBF. In this paper, we propose a novel estimator through which each node can accurately estimate not only the value of $(d)$ but also the size of the set difference to each node. Such an estimation result can be used to optimize the parameter setting of the CBF to achieve less false positives and false negatives. Comprehensive analysis and evaluation demonstrates that our method is more efficient than prior BF-based methods in terms of achieving the same accuracy with less communication cost. Moreover, our reconciliating method needs no prior context logs and it is very useful in networking and distributed applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for performing efficient cost aggregation in stereo matching. The cost aggregation problem is reformulated from the perspective of a histogram, giving us the potential to reduce the complexity of the cost aggregation in stereo matching significantly. Differently from previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window, our approach focuses on reducing the computational redundancy that exists among the search range, caused by a repeated filtering for all the hypotheses. Moreover, we also reduce the complexity of the window-based filtering through an efficient sampling scheme inside the matching window. The tradeoff between accuracy and complexity is extensively investigated by varying the parameters used in the proposed method. Experimental results show that the proposed method provides high-quality disparity maps with low complexity and outperforms existing local methods. This paper also provides new insights into complexity-constrained stereo-matching algorithm design. [ABSTRACT FROM AUTHOR]

Mixed impulsive and Gaussian noise reduction from digital color images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient, and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from color images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are preserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters. [ABSTRACT FROM AUTHOR]

This paper discusses practical experiences and mitigation methods of harmonics in wind power plants. Traces obtained from harmonic measurements of actual wind turbines are presented for the type 3 and type 4 turbines, and the harmonic performances of these wind turbines are elaborated on. Simulation case studies obtained from the harmonic analysis of various practical wind power plants are presented. The case studies presented include both resonance and nonresonance conditions. Finally, practical harmonic mitigation techniques including harmonic filtering and harmonic compensation are discussed. [ABSTRACT FROM AUTHOR]

Similarity joins play an important role in many application areas, such as data integration and cleaning, record linkage, and pattern recognition. In this paper, we study efficient algorithms for similarity joins with an edit distance constraint. Currently, the most prevalent approach is based on extracting overlapping grams from strings and considering only strings that share a certain number of grams as candidates. Unlike these existing approaches, we propose a novel approach to edit similarity join based on extracting nonoverlapping substrings, or chunks, from strings. We propose a class of chunking schemes based on the notion of tail-restricted chunk boundary dictionary. A new algorithm, VChunkJoin, is designed by integrating existing filtering methods and several new filters unique to our chunk-based method. We also design a greedy algorithm to automatically select a good chunking scheme for a given data set. We demonstrate experimentally that the new algorithm is faster than alternative methods yet occupies less space. [ABSTRACT FROM AUTHOR]

The recently developed High Efficiency Video Coding (HEVC) international video compression standard uses adaptive deblocking filter for reducing blocking artifacts. Deblocking filters increase both subjective and objective quality. But, they have high computational complexity. Therefore, in this paper, the first HEVC deblocking filter hardware in the literature is proposed. Two parallel datapaths are used in the proposed hardware in order to increase its performance. The proposed hardware is implemented in Verilog HDL. The Verilog RTL code is verified to work correctly on an FPGA board. The proposed HEVC deblocking filter hardware can code 30 full HD (1920x1080) video frames per second. Therefore, it can be used in consumer electronics products that require a real-time HEVC encoder or decoder. [ABSTRACT FROM PUBLISHER]

This paper presents the development of filtering methods for positron emission tomography (PET) signals contaminated by radio frequency (RF) pulses for combined PET and clinical 3-T magnetic resonance imaging (MRI). The filtering methods include software, hardware, and hybrid correction methods. In the software correction method, PET signals are assessed, and valid signals are identified based on the characteristics of a typical PET signal using Field-Programmable Gate Array (FPGA)-based programming. The hardware correction method makes use of differential-to-single-ended and low-pass filter circuits for PET analog signals. The hybrid correction method involves the sequential application of both the hardware and software methods. Both valid and contaminated PET signals are measured with an oscilloscope. An evaluation is then made of the performance (energy resolution, photopeak channel, total counts, and coincidence timing resolution) of the PET detector modules with and without various MR sequences (gradient echo, spin echo T1 sequence). For all correction methods, the energy resolution, photopeak position, and coincidence timing resolution with MR sequences are similar (< 3\%) to those without MR sequences. However, the total count of each module depends greatly on the method applied. The hybrid correction method displays an ability to preserve (< 1\%) the total counts of the modules during various MR sequences. The results show that this filtering method, which can reject noise signals and reduce count loss while preserving the valid analog signals of MR sequences, is reliable and useful for the development of simultaneous PET-MRI. [ABSTRACT FROM AUTHOR]

Many cities in China are seeking to establish cloud computing facilities for public and business services. However, despite large investments in the cloud, there are still several barriers to strategic cloud deployment in China. [ABSTRACT FROM PUBLISHER]

The intensity or gray-level derivatives have been widely used in image segmentation and enhancement. Conventional derivative filters often suffer from an undesired merging of adjacent objects because of their intrinsic usage of an inappropriately broad Gaussian kernel; as a result, neighboring structures cannot be properly resolved. To avoid this problem, we propose to replace the low-level Gaussian kernel with a bi-Gaussian function, which allows independent selection of scales in the foreground and background. By selecting a narrow neighborhood for the background with regard to the foreground, the proposed method will reduce interference from adjacent objects simultaneously preserving the ability of intraregion smoothing. Our idea is inspired by a comparative analysis of existing line filters, in which several traditional methods, including the vesselness, gradient flux, and medialness models, are integrated into a uniform framework. The comparison subsequently aids in understanding the principles of different filtering kernels, which is also a contribution of this paper. Based on some axiomatic scale-space assumptions, the full representation of our bi-Gaussian kernel is deduced. The popular \gamma-normalization scheme for multiscale integration is extended to the bi-Gaussian operators. Finally, combined with a parameter-free shape estimation scheme, a derivative filter is developed for the typical applications of curvilinear structure detection and vasculature image enhancement. It is verified in experiments using synthetic and real data that the proposed method outperforms several conventional filters in separating closely located objects and being robust to noise. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising. [ABSTRACT FROM PUBLISHER]

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details. [ABSTRACT FROM AUTHOR]

This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results. [ABSTRACT FROM AUTHOR]

This paper describes the specific development of a high-data-rate 57–64-GHz point-to-multipoint wireless local-area network communication system and the worldwide first installation of such a system into an aircraft cabin mock-up. All of the system components, from the RF-chip-set, the low-temperature cofired ceramic integrated antennas, and RF modules to the baseband processor, the medium access control, and the application software were purpose-designed for a wireless backbone for in-cabin communication. The integration aspects of these system components into a passenger aircraft cabin mock-up (ceiling and passenger seat-integration) as well as successful system performance tests were carried out and are reported here. [ABSTRACT FROM AUTHOR]