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The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) are fundamental inequalities concerning the differential entropies of linear transformations of random vectors. The EPI provides lower bounds for the differential entropy of linear transformations of random vectors with independent components. The BLI, on the other hand, provides upper bounds on the differential entropy of a random vector in terms of the differential entropies of some of its linear transformations. In this paper, we define a family of entropy functionals, which we show are subadditive. We then establish that Gaussians are extremal for these functionals by adapting a proof technique from Geng and Nair (2014). As a consequence, we obtain a new entropy inequality that generalizes both the BLI and EPI. By considering a variety of independence relations among the components of the random vectors appearing in these functionals, we also obtain families of inequalities that lie between the EPI and the BLI. [ABSTRACT FROM AUTHOR]

With the power system operating close to its stability margin, the increased penetration of renewable resources and loads with diverse characteristics, appropriate stability studies, and control decisions have become crucial. Due to the importance of accurate representation of aggregate loads in such studies, research interests for load modeling have increased. This paper puts forward a novel measurement-based load modeling approach that adapts itself based on the complex dynamics exhibited by the installed aggregate loads. The proposed approach primarily utilizes the collected measurements to gain insight regarding the dynamic trend in the measurements and subsequently determines the optimal order of an equivalent system or load model capable of effectively representing the aggregate load dynamics. An advanced signal pre-processing technique is employed, which effectively suppresses the noise and also preserves the transients present in the measurements. Afterward, an adaptive dynamic load model (ADLM) accurately represents the installed aggregate load. Lastly, the Refined Instrumental Variable (RIV) approach estimates the equivalent dynamic load model parameters. The results on a 2 bus and the IEEE 118 bus networks in DIgSILENT Powerfactory and a 7 bus network in Real-Time Digital Simulator (RTDS) reveal the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

High-level autonomous decision making system is one of the key technologies in intelligent transportation networks, it requires the traffic information within a certain range of vehicles in real time. When the traffic roads become congested or the roadside units (RSUs) are unaccessed beyond the communication range, the unmanned aerial vehicle (UAV)-assisted vehicular edge computing network (VECN) is considered as a potential solution. In this paper, we propose a learning based channel allocation and task offloading strategy in temporary UAV-assisted VECNs from a user perspective, in which the UAV passing temporarily can serve as the relay and edge computing node to support the decision making system. However, the limited available computation resources and time-varying communication channel states make it critical to process the received computing tasks. To address the above mentioned challenges, we design an efficient data transmission strategy combined the long-term evolution vehicle-to-everything (LTE-V2X) and time-division multiple access (TDMA) technologies firstly, then, we propose a multi-option task processing scheme, a service cost minimization problem is proposed where the integral decisions of channel allocation and task processing mode selection are jointly optimized. Under dynamic computing resources and the current data transmission conditions, the UAV selects an optimal task processing service model based on deep reinforcement learning (DRL) algorithm. Simulation results show the proposed strategy greatly improves the data transmission efficiency. [ABSTRACT FROM AUTHOR]

Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integral data rectification strategy for phasor measurement units (PMUs) in multi-area power systems, comprising local data processing and central data recovery modules. The local data processing is designed for the purpose of detecting and eliminating false PMU measurements, which is performed in a decentralized manner, based on our previously developed dynamic state estimation technique; whereas the data recovery is performed in a centralized manner at the control center, based on a newly proposed adaptive-phasor approach. The recovered PMU measurements are then utilized in a low-frequency oscillation damping enhancement scheme, which is achieved by a modified proportional-integral (PI) power system stabilizer (PSS) mechanism embedded in the automatic voltage regulator structure of a synchronous generator. Control parameters of the PI-PSS are optimized by maximizing the critical damping ratio of the power system. This paper is intended to make a contribution to the need of high-quality data transmission in modern power grids with contemporary measuring technologies. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for electricity, the advent of microgrids and the increasing penetration of distributed generators has renewed the interest in dynamic equivalencing, due to to its importance on power system analysis and control applications. This paper introduces a robust measurement-based framework for the online derivation of dynamic equivalent models. First, events/disturbances suitable for the derivation of dynamic equivalent models are automatically detected. Next, signal processing techniques are applied to recover missing samples and to remove noisy components from measured data. To exclude unnecessary post-disturbance data, a fine-tuning technique of the signal window length is also proposed as a supplementary offline process. Finally, model parameters are estimated using nonlinear least-squares optimization. The performance of the proposed methodology is tested using artificially created signals, simulation results obtained from a modified benchmark distribution grid and measurements acquired from a laboratory-scale active distribution network. [ABSTRACT FROM AUTHOR]

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, Firework, which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for Firework to allow developers to program on Firework. This paper describes the system design, implementation, and programming interface of Firework. The experimental results of a video analytics application demonstrate that Firework reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution. [ABSTRACT FROM AUTHOR]

There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance. [ABSTRACT FROM AUTHOR]

Objective: Clostridium difficile (C. diff) infection leads to hundreds of nosocomial infections, and early diagnosis of this toxin-mediated disease is important. This paper aims to develop a microrobotic system and related methods that enable the automated and rapid detection of toxins secreted by C. diff that exist in patient's stool. Methods: We utilize the fluorescent magnetic spore-based microrobot (FMSM), a microscale mobile sensing tool, to efficiently detect C. diff toxins by utilizing its property of selective fluorescence responses to C. diff toxins. A plug-and-play (PnP) electromagnetic coil system integrated with fluorescence microscopy is developed for actuation, control and observation of FMSMs. In order to track in real time and accurately obtain the fluorescence parameters of a FMSM under varied background noise in fluorescence signal, an image gradient-based method is proposed. For accelerating the FMSM-toxin interaction in different samples, an automated navigation control scheme for the FMSM is proposed and implemented. Moreover, data post-processing methods that can optimally extract the fluorescence decay trend from the dense and fluctuated fluorescence data are developed. Results: This automated mobile detection process finishes within only 20 minutes, and the toxin detection result is immediately given by adopting the proposed system and methods. Experimental results on different biological samples confirm the qualitative detection capability. And, C. diff toxins are automatically detected from the clinical stool of infectious patients and the relationship between the fluorescence decay and the toxin concentration is calibrated for semi-quantitative detection purpose. Significance: The proposed automated microrobotic platform provides a rapid and low-cost detection technique for C. diff toxins, and it has good competency for future clinical use. [ABSTRACT FROM AUTHOR]

In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing. [ABSTRACT FROM AUTHOR]

NASA’s Multiple Altimeter Beam Experimental LiDAR (MABEL) is an aircraft-based photon-counting laser altimeter designed as a simulator to test measurement techniques and algorithms for Advanced Topographic Laser Altimeter System (ATLAS), the sole instrument on NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. By measuring the time of flight, pointing angle, and absolute position for individual photons, ICESat-2 provides detailed elevation measurements of earth’s surface. Calculating accurate and precise elevations requires an understanding of how photons interact with surfaces, and characterization of the photon distribution after returning from surfaces. Neither MABEL nor ATLAS records the transmitted laser pulse shape, relying instead on aggregating several pulses worth of photons, often using histograms, to characterize the pulse shape. In this paper, we assess the limitations of using histograms and propose a more robust method to describe MABEL’s system impulse-response function using an exponentially modified Gaussian distribution. We also provide standard error estimates for the arithmetic mean and standard deviation calculations, and for exponentially modified Gaussian parameters using a Monte Carlo sensitivity analysis. We apply this method to photon returns from a sea ice lead and from a dry salt lake bed as case studies for estimating the standard error associated with sample size for the arithmetic mean and standard deviation, and for the exponentially modified Gaussian parameters. We use these standard errors to calculate the minimum number of photons required to find both Gaussian and exponentially modified Gaussian distribution parameters within 3 cm of their parent population values. [ABSTRACT FROM AUTHOR]

Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environment and cannot achieve long-term performance. Moreover, the existing deep reinforcement learning-based offloading approaches suffer from the slow convergence caused by high-dimensional action space. In this paper, we propose a new model-free deep reinforcement learning-based online computation offloading approach for blockchain-empowered mobile edge computing in which both mining tasks and data processing tasks are considered. First, we formulate the online offloading problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Then, to maximize long-term offloading performance, we leverage deep reinforcement learning to accommodate highly dynamic environments and address the computational complexity. Furthermore, we introduce an adaptive genetic algorithm into the exploration of deep reinforcement learning to effectively avoid useless exploration and speed up the convergence without reducing performance. Finally, our experimental results demonstrate that our algorithm can converge quickly and outperform three benchmark policies. [ABSTRACT FROM AUTHOR]

In-memory techniques keep data into faster and more expensive storage media for improving performance of big data processing. However, existing mechanisms do not consider how to expedite the data processing applications that access the input datasets only once. Another problem is how to reclaim memory without affecting other running applications. In this paper, we provide scheduling-aware data prefetching and eviction mechanisms based on Spark, Alluxio, and Hadoop. The mechanisms prefetch data and release memory resources based on the scheduling information. A mathematical method is proposed for maximizing the reduction of data access time. To make the mechanisms applicable in large-scale environments, we propose a heuristic algorithm to reduce the computational time. Furthermore, an enhanced version of the heuristic algorithm is also proposed to increase the amount of prefetched data. Finally, we perform real-testbed and simulation experiments to show the effectiveness of the proposed mechanisms. [ABSTRACT FROM AUTHOR]

MapReduce, originally developed by Google for search applications, has recently become a popular programming framework for parallel and distributed environments. This paper presents an energy-efficient architecture design for MapReduce on Field Programmable Gate Arrays (FPGAs). The major goal is to enable users to program FPGAs with simple MapReduce interfaces, and meanwhile to embrace automatic performance optimizations within the MapReduce framework. Compared to other processors like CPUs and GPUs, FPGAs are (re-)programmable hardware and have very low energy consumption. However, the design and implementation of MapReduce on FPGAs can be challenging: firstly, FPGAs are usually programmed with hardware description languages, which hurts the programmability of the MapReduce design to its users; secondly, since MapReduce has irregular access patterns (especially in the reduce phase) and needs to support user-defined functions, careful designs and optimizations are required for efficiency. In this paper, we design, implement and evaluate Melia, a MapReduce framework on FPGAs. Melia takes advantage of the recent OpenCL programming framework developed for Altera FPGAs, and abstracts FPGAs behind the simple and familiar MapReduce interfaces in C. We further develop a series of FPGA-centric optimization techniques to improve the efficiency of Melia, and a cost- and resource-based approach to automate the parameter settings for those optimizations. We evaluate Melia on a recent Altera Stratix V GX FPGA with a number of commonly used MapReduce benchmarks. Our results demonstrate that 1) the efficiency and effectiveness of our optimizations and automated parameter setting approach, 2) Melia can achieve promising energy efficiency in comparison with its counterparts on CPUs/GPUs on both single-FPGA and cluster settings. [ABSTRACT FROM AUTHOR]

When two input discrete distributions pass through a memoryless channel, the data processing inequality expresses the fact that the $f$ divergence decreases. As is well known, in the case of the Kullback Leibler divergence, the chain rule makes it possible to obtain an exact expression for the gap in the data processing inequality, as a conditional divergence. In this paper, we provide a general expression for the gap valid for any convex function $f$ , which generalizes the well-known formula for the Kullback-Leibler case. The result is a sum of Bregman divergences, which shows non-negativity. We then show how this identity may be used to give simple proofs of two classical results in linear algebra. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results. [ABSTRACT FROM AUTHOR]

Significant advances in spaceborne imaging payloads have resulted in new big data problems in the Earth Observation (EO) field. These challenges are compounded onboard satellites due to a lack of equivalent advancement in onboard data processing and downlink technologies. We have previously proposed a new GPU accelerated onboard data processing architecture and developed parallelised image processing software to demonstrate the achievable data processing throughput and compression performance. However, the environmental characteristics are distinctly different to those on Earth, such as available power and the probability of adverse single event radiation effects. In this paper, we analyse new performance results for a low power embedded GPU platform, investigate the error resilience of our GPU image processing application and offer two new error resilient versions of the application. We utilise software based error injection testing to evaluate data corruption and functional interrupts. These results inform the new error resilient methods that also leverages GPU characteristics to minimise time and memory overheads. The key results show that our targeted redundancy techniques reduce the data corruption from a probability of up to 46 percent to now less than 2 percent for all test cases, with a typical execution time overhead of 130 percent. [ABSTRACT FROM AUTHOR]

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. In this paper, we present some extensions of the existing adaptive filtering algorithms enabling data selection, which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data are expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection algorithms. Similar discussions lead to the proposal of the data-selective least mean square and data-selective recursive least squares algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacrificing the estimation accuracy, while reducing the computational cost. [ABSTRACT FROM AUTHOR]

Erasure coding has received considerable attentions due to the better tradeoff between the space efficiency and reliability. The frequent update of the stored data in the distributed storage systems has posed a new challenge for erasure codes: how to update the erasure-coded data in a general, efficient and adaptive way. However, existing update schemes of erasure codes are inadequate to meet these requirements, since their code-related update manners lead to a low generality, their star-structured data transmission manners lead to a low update efficiency, and their redo manners when encountering the node failure lead to a low adaptivity. In this paper, we propose an adaptive update scheme with the tree-structured transmission, called TA-Update, which consists of a code-independent update framework and three algorithms: the rack-aware tree construction algorithm, the top-down data processing algorithm and the rollback-based failure processing algorithm. For generality, we propose a code-independent update framework with the tree structure to support the MDS code with any coding parameter. For efficiency, a rack-aware tree construction algorithm is proposed to achieve the high available bandwidth, which organizes the data node and parity nodes as an update tree. Moreover, a top-down data processing algorithm is proposed to achieve the high transmission and computation efficiency, which pipelines the data transmission along the update tree and distributes the encoding computations among all the participating nodes. For adaptivity, we propose a rollback-based failure processing algorithm to achieve high adaptivity, which handles the node failure during update with the existing update tree in a rollback manner. To evaluate the performance of TA-Update, we conduct experiments on HDFS-RAID under various parameter settings on both 30 physical and 200 virtual machines. Extensive experiments confirm that TA-Update could support the various erasure codes with any parameter, improve the update efficiency by 30 percent and the adaptivity by 47 percent on average compared with the state-of-the-art approaches under various parameter settings. [ABSTRACT FROM AUTHOR]

Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been widely used as a nondestructive tool for the investigation of the subsurface, but it is challenging to automatically process the generated GPR B-scan images. In this paper, an automatic GPR B-scan image interpreting model is proposed to interpret GPR B-scan images and estimate buried pipes, which consists of the preprocessing method, the open-scan clustering algorithm (OSCA), the parabolic fitting-based judgment (PFJ) method, and the restricted algebraic-distance-based fitting (RADF) algorithm. First, a thresholding method based on the gradient information transforms the B-scan image to the binary image, and the opening and closing operations remove discrete noisy points. Then, OSCA scans the preprocessed binary image progressively to identify the point clusters with downward-opening signatures, and PFJ further validates whether the point clusters with downward-opening signatures are hyperbolic. By utilizing OSCA and PFJ, point clusters with hyperbolic signatures could be classified and segmented from other regions even if there are some connections and intersections between them. Finally, the validated point clusters are fitted into the lower parts of hyperbolas by RADF that solves fitting problems with additional constraints related to the hyperbolic central axis. By integrating these methods, the proposed model is able to extract information from GPR B-scan images automatically and efficiently. The experiments on simulated and real-world data sets demonstrate the effectiveness of the proposed model. 1 A point cluster is a collection of points with the same class identification. [ABSTRACT FROM AUTHOR]

Hierarchical temporal memory (HTM) is a machine learning algorithm inspired by the information processing mechanisms of the human neocortex and consists of a spatial pooler (SP) and temporal memory (TM). In this paper, we develop circuits and systems to achieve the optimized design of an HTM SP, an HTM TM, and a memristive analog pattern matcher for pattern recognition applications. The HTM SP realizes an optimized hardware design through the introduction of mean overlap calculations and by replacing the threshold determination in the inhibition stage with a weighted summation operator over the neighborhood of the pixel under consideration. HTM TM is based on discrete analog memristive memory arrays and a weight update procedure. The operation of the proposed system is demonstrated for a face recognition problem, using the standard AR, ORL, and Yale databases, and for speech recognition, using the TIMIT database, with achieved accuracies of 87.21% and approximately 90%, respectively, given an SNR of 10 dB. Visual data processing using binary HTM SP features requires less storage and processing memory than required by the traditional processing methods, with the area and power requirements for its implementation being 0.096 mm2 and 1756 mW, respectively. The design of the TM circuit for a single pixel requires 23.85 \mu\textm^2 of area and 442.26 \mu\textW of power. [ABSTRACT FROM PUBLISHER]

This paper addresses the correspondence between linear inequalities for Shannon entropy and differential entropy for sums of independent group-valued random variables. We show that any balanced (with the sum of coefficients being zero) linear inequality for Shannon entropy holds if and only if its differential entropy counterpart also holds; moreover, any linear inequality for differential entropy must be balanced. In particular, our result shows that recently proved differential entropy inequalities by Kontoyiannis and Madiman can be deduced from their discrete counterparts due to Tao in a unified manner. Generalizations to certain abelian groups are also obtained. Our proof of extending inequalities for Shannon entropy to differential entropy relies on a result of Rényi which relates the Shannon entropy of a finely discretized random variable to its differential entropy and also helps in establishing that the entropy of the sum of quantized random variables is asymptotically equal to that of the quantized sum; the converse uses the asymptotics of the differential entropy of convolutions with weak additive noise. [ABSTRACT FROM PUBLISHER]

The ongoing upgrade campaign at the INFN National Laboratories of Legnaro, required to accommodate the selective production of exotic species (SPES) facility, demands for a new beam diagnostic system that outperforms the legacy versabus module eurocard-based current and beam position monitor. The beam diagnostic is an essential component without whom the operator would blindly attempt to setup all accelerator parameters in order to transport the beam from the source to the target experiment. This paper provides a complete overview of the diagnostic data readout chain inclusive of custom hardware solutions, signal processing, and data management. Each diagnostic point, installed along the beam pipe, consists of a Faraday cup and a beam profile monitor. The front-end electronics is based on a custom general purpose motherboard endowed with an field programmable gate array that handles the data sampling, data filtering, and data buffering and with a computer on module that runs an EPICS input–output controller application and bridges the beam diagnostic information to the control network for remote visualization. The custom hardware platform at the heart of the diagnostic system is able to perform real-time tasks and allows for hardware standardization and modularity, then being the base for embedding the control of several different appliances in our accelerators complex. A precise beam current and position measurement is required by the SPES project that foresees a radioactive ion beam in a wide range of intensities and energies. The prototypes of the new electronics have shown good performance improvements and the beam current resolution has been extended to a few pA. [ABSTRACT FROM AUTHOR]

A measure of correlation is said to have the tensorization property if it does not change when computed for i.i.d. copies. More precisely, a measure of correlation between two random variables X, Y denoted by \rho (X, Y) , has the tensorization property if \rho (X^n, Y^n)=\rho (X, Y) where (X^n, Y^n) denotes $n$ i.i.d. copies of (X, Y)$ . Two well-known examples of such measures are the maximal correlation and the hypercontractivity ribbon (HC ribbon). We show that the maximal correlation and the HC ribbon are special cases of the new notion of \Phi $ -ribbons, defined in this paper for a class of convex functions \Phi $ . \Phi $ -ribbon reduces to the HC ribbon and the maximal correlation for special choices of \Phi $ , and is a measure of correlation with the tensorization property. We show that the \Phi $ -ribbon also characterizes the recently introduced and introduce an equivalent characterization of this ribbon. [ABSTRACT FROM AUTHOR]

This paper quantifies the intuitive observation that adding noise reduces available information by means of nonlinear strong data processing inequalities. Consider the random variables W\to X\to Y forming a Markov chain, where Y = X + Z with X and Z real valued, independent and X -norm. It is shown that I(W; Y) \le FI(I(W;X)) with FI(t) < t whenever $t > 0$ , if and only if $Z$ has a density whose support is not disjoint from any translate of itself. A related question is to characterize for what couplings $(W, X)$ the mutual information $I(W; Y)$ is close to maximum possible. To that end we show that in order to saturate the channel, i.e., for $I(W; Y)$ to approach capacity, it is mandatory that $I(W; X)\to \infty $ (under suitable conditions on the channel). A key ingredient for this result is a deconvolution lemma which shows that postconvolution total variation distance bounds the preconvolution Kolmogorov–Smirnov distance. Explicit bounds are provided for the special case of the additive Gaussian noise channel with quadratic cost constraint. These bounds are shown to be order optimal. For this case, simplified proofs are provided leveraging Gaussian-specific tools such as the connection between information and estimation (I-MMSE) and Talagrand’s information-transportation inequality. [ABSTRACT FROM PUBLISHER]

In this paper, a practical fault diagnosis algorithm is presented to realize multiple open-circuit fault diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault diagnosis algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification algorithms, the proposed algorithm is simple and stable to realize the multiple switches fault diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault diagnosis algorithm is verified by the experimental results. [ABSTRACT FROM AUTHOR]

Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, nLw(551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes. [ABSTRACT FROM PUBLISHER]

Location prediction is widely used to forecast users’ next place to visit based on his/her mobility logs. It is an essential problem in location data processing, invaluable for surveillance, business, and personal applications. It is very challenging due to the sparsity issues of check-in data. An often ignored problem in recent studies is the variety across different check-in scenarios, which is becoming more urgent due to the increasing availability of more location check-in applications. In this paper, we propose a new feature fusion based prediction approach, GALLOP, i.e., GlobAL feature fused LOcation Prediction for different check-in scenarios. Based on the carefully designed feature extraction methods, we utilize a novel combined prediction framework. Specifically, we set out to utilize the density estimation model to profile geographical features, i.e., context information, the factorization method to extract collaborative information, and a graph structure to extract location transition patterns of users’ temporal check-in sequence, i.e., content information. An empirical study on three different check-in datasets demonstrates impressive robustness and improvement of the proposed approach. [ABSTRACT FROM PUBLISHER]

With the interdependencies that exist between data in a scientific processing pipeline, the ability to track the provenance of the scientific process through multiple stages is necessary to determining the usability of the resulting data product. In this paper, we study the capture of provenance from an existing NASA instrument ingest pipeline. Since instrumenting the scientific code for a production system is not feasible, we show how provenance events can be scavenged from log files to generate detailed provenance graphs. Through extensions to the Karma provenance system, which have been implemented on a test instance of the AMSR-E production data pipeline, we determine that when the volume of provenance information is high, provenance graph visualizations provide a good tool for monitoring the ingest pipeline and identifying processing differences in ways not seen before. Two novel uses of provenance that we present in this paper are comparisons between processing runs and forward provenance for viewing downstream dependencies. [ABSTRACT FROM AUTHOR]

A real-world signal is always corrupted with noise. The separation between a signal and noise is an indispensable step in a variety of signal-analysis applications across different scientific domains. In this paper, we propose a double least-squares projections (DLSPs) method to estimate a signal from the noisy data. The first least-squares projection is to find a signal-dimensional optimal approximation of the noisy data in the least-squares sense. In this step, a rough estimation of the signal is obtained. The second least-squares projection is to find an approximation of a signal in another crossed signal-dimensional space in the least-squares sense. In this step, a much improved signal estimation that is close to orthogonal to the separated noise subspace can be obtained. The DLSP implements projection operation twice to obtain an almost perfect estimation of a signal. The application of the DLSP method in seismic random noise attenuation and signal reconstruction demonstrates the successful performance in seismic data processing. [ABSTRACT FROM PUBLISHER]

Recent developments in smart metering applications have led to the conceptualization and construction of a new type of energy meter, operating on the basis of event-driven principles. The event-driven metering concepts are applied to represent the information on the electrical load patterns, which have an integral value. This paper explains why these concepts are different from the ones used for event-based applications in other domains, discusses the principles used in the new type of electricity meter, presents the data formats structured in such a way to provide detailed knowledge representation, and shows a number of results on real-case applications. A specific index is defined in order to represent the effectiveness of the event-driven metering scheme illustrated to represent the details of the metered pattern, comparing the results with the ones that could be reached in the most favorable case through regular timer-driven metering. The presentation of specific applications based on real-life datasets highlights the advantages of the event-driven energy metering over the traditional timer-driven metering scheme. [ABSTRACT FROM AUTHOR]

Sequential querying of differentially private mechanisms degrades the overall privacy level. In this paper, we answer the fundamental question of characterizing the level of overall privacy degradation as a function of the number of queries and the privacy levels maintained by each privatization mechanism. Our solution is complete: we prove an upper bound on the overall privacy level and construct a sequence of privatization mechanisms that achieves this bound. The key innovation is the introduction of an operational interpretation of differential privacy (involving hypothesis testing) and the use of a data processing inequality along with its converse. Our result improves over the state of the art, and has immediate connections to several problems studied in the literature. [ABSTRACT FROM AUTHOR]

Seismic data acquired by geophones are processed to estimate images of the earth’s interior that are used to explore, develop, and monitor resources and to study the shallow structure of the crust for geological, environmental, and geotechnical purposes. These multidimensional data sets are often irregularly sampled and incomplete in the so-called midpoint and offset acquisition coordinates. Multidimensional seismic data reconstruction can be viewed as a low-rank matrix or tensor completion problem. In this paper, we introduce a fast and efficient low-rank tensor completion algorithm named parallel square matrix factorization (PSMF) and adopt it to reconstruct seismic data in the typical seismic data processing coordinates: frequency, midpoint, and offset. For each frequency slice, we establish a tensor minimization model composed of a low-rank constrained term and a data misfit term. Then we adopt the PSMF algorithm for the recovery of the missing samples. In the PSMF method, we avoid using unbalanced “long strip” matrices that result from conventional tensor unfolding. Instead, the tensor is unfolded into almost square or square matrices that are low rank. We also compare the proposed PSMF method with other completion methods. Experiments via synthetic data and field data sets validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The maximum and minimum normalized difference vegetation indexes (NDVIs) describe two extremes of vegetation greenness during a predefined period. A maximum NDVI image can be composited easily via the direct selection of the maximum NDVI from multiple observations without the need to mask out cloud or snow. But, a minimum NDVI image cannot be built in a similar manner. In this paper, an approach was proposed to composite the minimum NDVI (the least vegetation greenness) image. The minimum spectral index that consists of the green (555 nm) and SWIR bands (2130 nm) from MODIS data, which was named here as the Brown Vegetation Index (BVI), was taken as a proxy to composite the minimum vegetation NDVI. This composite method performs well on a global scale for the NDVIs that were derived from MODIS land surface reflectance (MOD09A1) products. The BVI-based minimum NDVI was compared with the direct selection of the minimum NDVI after excluding contaminated observations using a refined cloud/snow mask. The comparison shows that the difference for 97% of the minimum NDVI between the two approaches is within the range of ±0.1 NDVI unit. Various potential spectral indices for compositing the minimum NDVI were compared, which demonstrated the BVI-based approach was top rated. Several examples demonstrated that the composited minimum NDVI is valuable and effective for identifying evergreen forests, monsoon forests, and double cropping. The minimum NDVI combined with the maximum NDVI would simplify the way to describe intraannual vegetation changes. [ABSTRACT FROM PUBLISHER]

Radon transforms have been widely utilized for exploration seismic data processing. They have been part of the seismic data processing workflow for the last few decades. They are robust, easy to compute, and mathematically well established. This paper suggests a new method for obtaining an undersampled high-resolution Radon transform. The proposed method is based on a nonlinear sampling technique known as compressive sensing, which assumes that seismic data is sparse in a certain domain. The Radon transform domain can be sparse for exploration seismic data. The proposed method was applied for different seismic data processing applications including: 1) attenuation of multiple reflections; 2) first-arrival picking; and 3) seismic denoising. The method was tested on synthetic as well as real seismic data. Additionally, it was compared with existing methods for low- and high-resolution Radon transforms. From the simulation results, it is clear that the proposed method not only reduces the number of measurements needed but also produces high-resolution Radon transforms with less computational time. Therefore, it is believed that the proposed method is an appropriate alternative to some of the existing methods for efficient high-resolution sparse Radon transform computation. [ABSTRACT FROM AUTHOR]

In a normal environment on Earth, there are several methods for obtaining the deflection curve of a satellite wing frame, such as the laser method and the electronic resistance strain gauge method. However, none of them are suitable for the outer space environment. Therefore, developing a reliable sensing solution for a high electronic noise, vacuum, and extremely low-temperature environment may be a very challenging task. In this paper, a new fiber Bragg grating (FBG) sensors method is proposed to meet this harsh environment need. First, hundreds of FBG sensors are pasted onto the surface of a satellite wing frame to detect the strain data. Second, the cubic spline interpolation function, quadratic integral function, and quantic polynomial function are used for the data processing to obtain the shape of the satellite wing frame. Finally, a satellite wing frame is placed in a heat sink to conduct a shape monitoring experiment. Furthermore, a verification experiment in a normal environment shows the reliability of the FBG sensors method. Because the FBG sensors and the supporting equipment are available in the market, this method is a feasible solution for shape monitoring in outer space. [ABSTRACT FROM PUBLISHER]

The shift to the in-memory data processing paradigm has had a major influence on the development of cluster data processing frameworks. Numerous frameworks from the industry, open source community and academia are adopting the in-memory paradigm to achieve functionalities and performance breakthroughs. However, despite the advantages of these in-memory frameworks, in practice they are susceptible to memory-pressure related performance collapse and failures. The contributions of this paper are two-fold. First, we conduct a detailed diagnosis of the memory pressure problem and identify three preconditions for the performance collapse. These preconditions not only explain the problem but also shed light on the possible solution strategies. Second, we propose a novel programming abstraction called the leaky buffer that eliminates one of the preconditions, thereby addressing the underlying problem. We have implemented a leaky buffer enabled hashtable in Spark, and we believe it is also able to substitute the hashtable that performs similar hash aggregation operations in any other programs or data processing frameworks. Experiments on a range of memory intensive aggregation operations show that the leaky buffer abstraction can drastically reduce the occurrence of memory-related failures, improve performance by up to 507 percent and reduce memory usage by up to 87.5 percent. [ABSTRACT FROM PUBLISHER]

Increasing yield is a primary concern to integrated circuit manufacturing companies as it dictates the readiness of a new process for high volume manufacturing. In order to expedite the process of discovering yield issues, companies have started looking for ways to perform early prediction for such issues. This paper suggests the use of the support vector machines (SVMs) for early wafer classification. The choice of SVM is motivated by the model's ability to effectively classify multivariate, multimodal, and inseparable data points. This model uses multidimensional hyperplanes to separate and classify wafers into low-yield and high-yield classes. This paper includes a proposal for how the classification model can be applied for yield classification and how it can be adaptively updated in a manufacturing environment. We show how the values for the SVM parameters can be selected for best yield classification. Furthermore, performance evaluation is conducted on real manufacturing data, comparing the proposed SVM classifier to state of the art. Results show that in all cases, SVM consistently outperforms other methods with and without adaptive model updates. The experiments also show that all classifiers' performances depend on yield thresholds. It is also shown that the classification model can be built and executed using a reduced set without compromising its accuracy. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to share the experience gained in, and the efforts made toward, introducing and implementing a new course in the challenging and important area of geophysical signal processing at the Electrical Engineering (EE) Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. The new course, titled “Geo-Signal Processing,” was offered both at the graduate level and as a special topics course to undergraduates. The course was developed in collaboration with the Earth Sciences Department at KFUPM. This paper contributes new information because it stresses the multidisciplinary aspects of digital signal processing (DSP) technologies when applied to estimating the Earth's layered structure on the basis of seismic data. Unlike many Earth sciences seismic data processing courses, this Geo-Signal Processing course also emphasizes that the perspective taken by those working in DSP is different from that taken by geophysicists. The course presents DSP with particular emphasis on seismic data signals and the artifacts accompanying them while covering the principles and algorithms needed for processing seismic signals in both deterministic and statistical fashion. Topics include, but are not limited to, basic seismic theory, acquisition of seismic data, analysis of seismic signals and noise, deterministic filtering of seismic data, and statistical processing of seismic data. [ABSTRACT FROM PUBLISHER]

Disk additions to a RAID-6 storage system can simultaneously increase the I/O parallelism and expand the storage capacity. To regain a balanced load among both old and new disks, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling are restricted by preserving a round-robin data distribution, and require migrating all the data, resulting in an expensive cost for RAID-6 scaling. In this paper, we propose Xscale, a new approach to accelerating X-code RAID-6 scaling by using lightweight data reorganization. Xscale minimizes the number of data blocks that require being moved, while maintaining a uniform data distribution across all disks. Furthermore, Xscale eliminates metadata updates while guaranteeing data consistency and data reliability. Compared with the round-robin approach, Xscale reduces the number of blocks to be moved by 63.6-89.5 percent, decreases the reorganization time by 35.62-37.26 percent, and reduces the I/O latency by 23.29-37.74 percent while the scaling programs are running in the background. In addition, there is no penalty in the performance of the data layout after scaling using Xscale, compared with the layouts maintained by other existing scaling approaches. [ABSTRACT FROM AUTHOR]

This paper is concerned with the optimal estimation problem for discrete-time stochastic systems with finite-step auto- and cross-correlated noises and multiple packet dropouts induced by the unreliable networks. When a packet transmitted from the sensor to the data processing center is lost, its predictor is used as the compensation. The optimal linear estimators including filter, predictor and smoother that depend on the packet arriving rate are proposed in the linear minimum variance sense via an innovative analysis approach. They are computed in terms of the solutions of some auto- and cross-covariance matrices. A tracking system example is given to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The modified ambiguity function approach (MAFA) is one of the integer least square estimation methods. Although it is a simple and efficient method, it has a significant limitation. This limitation is formulated and analyzed in this paper. A graphical representation of the necessary condition for the MAFA method is presented by plots of Voronoi cells and error ellipsoids, along with a detailed procedure for constructing these plots. It is shown that the necessary condition for the MAFA method is not satisfied in some cases. To overcome this problem, a solution is presented in the final section of this paper. [ABSTRACT FROM AUTHOR]

A new generation of advanced surveillance systems is being conceived as a collection of multisensor components such as video, audio, and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers), and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronization. This paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of real-time networks/data-oriented requirements implementation scheme. It also proposes the implementation of these components using the well-known middleware technology common object request broker architecture. Results using this architecture for video surveillance are presented through an implemented prototype. [ABSTRACT FROM AUTHOR]

This paper combines a digital signal processor (DSP) and a field programmable gate array (FPGA) to realize the online empirical mode decomposition (EMD)-based signal processing system. The EMD algorithm is a novel signal analysis technique, decomposing signals into a series of intrinsic mode functions. First, the EMD algorithm is implemented in the DSP, named the EMD processor, which has the ability to eliminate noise from the original signal. Next, in order to process the online sequential signal, this paper proposes and implements pipeline and data transfer controllers in the FPGA, called the data processing flow processor. Then, the data processing flow processor coordinates the EMD processor, analog-to-digital converter, and digital-to-analog converter module boards. Finally, this paper presents a prototype of the online EMD-based electrocardiogram denoise system to verify the features of the proposed architecture. The emulations and experimental results demonstrate the effectiveness of the presented system as expected. [ABSTRACT FROM AUTHOR]

Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data. [ABSTRACT FROM AUTHOR]

In order to extract a local and faint variation generated by a specific source from a physical value obtained by a remote-sensing satellite, we should focus on the difference between physical values at two points. We have developed a new data- restructuring algorithm based on this concept. This algorithm enables us to obtain a distribution of physical values at any interval and to calculate the difference between any two points regardless of the sensor-sampling interval. This paper presents this algorithm and how to implement it on a computer. Based on this data-restructuring algorithm, we have established a new data-processing method and analyzed the brightness-temperature data obtained by a microwave radiometer. As a result, we have successfully extracted the feature associated with a volcanic eruption. This feature cannot be extracted from brightness temperatures directly. This paper demonstrates this data-processing method by analyzing a volcanic eruption. [ABSTRACT FROM AUTHOR]

The ATLAS Hadronic Tile Calorimeter detector (TileCal) is an iron-scintillating tiles sampling calorimeter de- signed to operate at the Large Hadron Collider accelerator at CERN. The central element of the back-end system of the TileCal detector is a 9U VME Read-Out Driver (ROD) board. The operation of the TileCal calorimeter requires a total of 32 ROD boards. This paper summarizes the tests performed during the ROD production and the results obtained. Data processing is performed in the ROD by digital signal processors, whose operation is based on the use of online algorithms such as the optimal filtering algorithm for the signal amplitude, pedestal and time reconstruction and the online Muon tagging algorithm which identifies low transverse momentum muons. The initial performance of both algorithms run during commissioning is also presented in this paper. [ABSTRACT FROM AUTHOR]

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described. [ABSTRACT FROM AUTHOR]