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The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID), as one of the key IoT technologies, has been used to collect real-time production data to support the manufacturing decision-making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID-enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes). Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data. Note to Practitioners— This paper was motivated by a real-life company which has used RFID technology for over ten years. Great myriad of data has been captured from frontline production shop floors where machines, workers, materials, and manufacturing jobs were logically associated. Processing time and quality data are studied in this paper so as to investigate the production performance in the RFID-enabled smart factory. [ABSTRACT FROM AUTHOR]

The increasing use of induction motors (IMs) with variable frequency drives for industrial process control and automation has necessitated the need to evaluate their energy efficiency in the presence of voltage and current harmonics. As the determination of IM efficiency is becoming mandatory in many countries, there is a need to develop a testing approach that eliminates inaccuracies due to the operator. This paper presents an automated efficiency testing procedure developed to mitigate the adverse effect of varying technical skill and competence levels of testing personnel on efficiency test results. This is a particularly useful development considering the recently proposed IEC/TS 60034-2-3 methodology which specifies identical tests between sinusoidal supply and converter operation. A comparison of several manual and automated efficiency tests on a 55-kW IM highlighted improvements in repeatability, correlation, and temperature variation due to the latter. In addition, the advanced data acquisition and computation capability of the measurement device used in the developed test rig provides an excellent platform to investigate the dynamic operation of IM drives. [ABSTRACT FROM AUTHOR]

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the ‘freshness’ of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay $B$ , e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than $B$. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio $4\frac {4}{7}$ for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average. [ABSTRACT FROM AUTHOR]

Multichannel digital sinewave generators play a key role in digital automated impedance bridges. Along with the technological progress in the development of commercial data acquisition (DAQ) boards, which brings with it improvements in their metrological and functional properties, it is possible to realize the DAQ-based high-precision multichannel generators. This paper describes the evaluation of properties of three commercial DAQs, available in the form of PCI eXtensions for Instrumentation modules and working as two-channel generators supervised by software developed by the authors. Due to the fact that the main application area of these generators is digital impedance bridges, this paper presents research results the most relevant for the impedance bridges, i.e., the stability of amplitude ratio and phase difference as well as total harmonic distortion and spurious-free dynamic range. The results and conclusion presented in this paper may be useful when choosing DAQs intended to be used in the multichannel generators. [ABSTRACT FROM AUTHOR]

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- ${r}$ local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. Note to Practitioners—This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams. [ABSTRACT FROM AUTHOR]

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy. [ABSTRACT FROM AUTHOR]

In most of the existing mobile applications for data collection and data analytics, either the privacy issue is frequently neglected or the privacy options are not configurable by the participants. This paper proposes configurable privacy level by potential crowdsourcing participants who are able to choose their desirable privacy level and get paid based on the quality of data they provide to the originator in Device to Device network (D2D). Combining with the encryption technique, not only the user’s privacy is protected, but also the encryption complexity is reduced. We first formulate the participant recruitment process into an optimization problem from the participants’ perspective by considering the competition and collaboration among existing and candidate participants in order to achieve the best utility. Then we design a distributed approximate scheme that relies on participants’ local knowledge to complete the overall recruitment task. We implement the approximate approach in Dell Streak tablets and carry out a campus-scale experiment for 21 days, plus run simulations for more extensive and detailed evaluation under various task settings. The results demonstrate the efficiency of the proposed approaches and disclose valuable insights for practical considerations in D2D based crowdsourcing. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of emergency surgery insertion into a given elective surgery schedule of an operating theater (OT) composed of multiple operating rooms (ORs). Emergency surgeries with different emergency levels characterized by waiting time targets (WTTs) arrive according to a nonhomogeneous Poisson process and can be inserted into any OR. An event-based stochastic programming model is proposed to minimize the total cost incurred by exceeding WTTs of emergency surgeries, elective surgery delay, and surgery team overtime. A perfect information-based lower bound is proposed and the properties of the optimal policies are proved. Simple heuristic policies and a stochastic optimization (SO) approach derived from the simple policies by policy improvement are proposed. Numerical experiments show that the SO significantly outperforms the others and efficient emergency insertion significantly improves the system performance. A principal component analysis is performed to show how near-optimal policies differ from simple heuristic policies. Note to Practitioners—This paper is motivated by enhancing the efficiency of OTs by sharing the surgery capacity between the elective and emergency surgeries. More specifically, we consider the problem of inserting nonelective surgeries of different emergency levels in the execution of a given elective surgery schedule. An SO approach is proposed to dynamically prioritize emergency and elective surgeries in order to best balance meeting emergency surgery requirement, perturbation of elective schedule, and surgery team overtime. Numerical experiments based on the data collected from Saint-Joseph Hospital in Paris show the significant benefit of efficient emergency insertion over the current hospital practice. Elective surgery schedule is shown to have the most important impact on the system performance but efficient emergency insertion always adds significant improvement. [ABSTRACT FROM AUTHOR]

In this paper, we analyze some recently proposed radio-frequency identification group codes based on low-density parity-check matrices that check the integrity of collections of tagged objects. These codes write additional data to the memory of tags to compute the number of missing tags. By studying the underlying requirements for these computations, we obtain bounds and show that there are simpler group codes that perform better, requiring less tag memory. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Semi-supervised Learning with Incomplete Modality (SLIM) method considering the modal consistency and complementarity simultaneously, and Kernel SLIM (SLIM-K) based on matrix completion for further solving the modal incompleteness. As is well known, most realistic data have multi-modal representations, multi-modal learning refers to the process of learning a precise model for complete modalities. However, due to the failures of data collection, self-deficiencies, or other various reasons, multi-modal examples are usually with incomplete modalities, which generate utility obstacle using previous methods. In this paper, SLIM integrates the intrinsic consistency and extrinsic complementary information for prediction and cluster simultaneously. In detail, SLIM forms different modal classifiers and clustering learner consistently in a unified framework, while using the extrinsic complementary information from unlabeled data against the insufficiencies brought by the incomplete modal issue. Moreover, in order to deal with missing modality in essence, we propose the SLIM-K, which takes the complemented kernel matrix into the classifiers and the cluster learner respectively. Thus, SLIM-K can solve the defects of missing modality in result. Finally, we give the discussion of generalization of incomplete modalities. Experiments on 13 benchmark multi-modal datasets and two real-world incomplete multi-modal datasets validate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In crowdsourcing, human workers are employed to tackle problems that are traditionally difficult for computers (e.g., data cleaning, missing value filling, and sentiment analysis). In this paper, we study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., What is the age of Jet Li?). Although this problem has been studied before, existing work often treats related attributes independently, leading to suboptimal performance. In this paper, we present T-Crowd, which is a crowdsourcing system that considers attribute relationships. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is used to guide task allocation to workers. Our solution seamlessly supports categorical and continuous attributes. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference and reducing the monetary cost of crowdsourcing. [ABSTRACT FROM AUTHOR]

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the Geographical correlation-based RF-data Collection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only $1/28{\sim }1/3$ 1 / 28 ∼ 1 / 3 of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags. [ABSTRACT FROM AUTHOR]

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master–slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to characterize the jitter of waveform recorders by measuring the power spectral density (PSD) of the jitter components due to the phase noise of the sampling clock and the aperture delay in the sampling circuit. The method relies on a simple sinewave test, where the phase information is extracted from the acquired signal; however, the effects of the quantization and additive noise are minimized by exploiting the cyclostationary properties of the acquired sequence. The results in simulations and on digital storage oscilloscopes show the capability of the method of accurately measuring the PSDs of the considered components. [ABSTRACT FROM AUTHOR]

Subspace clustering aims to cluster a collection of data points lying in a union of subspaces. Based on the assumption that each point can be approximately represented as a linear combination of other points, extensive efforts have been made to compute an affinity matrix in a self-expressive framework for describing the similarity between points. However, the existing clustering methods consider the average feature solutions, which would not be powerful enough to capture the intrinsic relationship between points. In this paper, we present the $k$ -support norm subspace clustering (KSC) method by utilizing k-$ support norm regularization. The k-$ support norm trades off the sparsity of norm and the uniform shrinkage of \ell _{2} norm to yield better predictive performance on the data connection. The theoretical analysis of KSC makes up a large proportion of paper. In the noise-free case, we provide the kEBD condition, which ensures the coefficient matrix to be block diagonal. If the data are corrupted, we prove the incompletion-grouping effect for KSC. Moreover, we provide the statistical recovery guarantee for both noise-free and noise cases. The theory analyses show the validity and feasibility of KSC, and the experimental results on multiple challenging databases demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships among scalar variables, but cannot be directly applied to model a system with functional variables. In this paper, a novel functional graphical model is proposed to model complex systems where functional variables are measured. To cope with the small sample size problem, we further develop a special sparsity penalization approach to robustly learn the graphical model from limited sample size, and develop a difference from the mean penalization for functional variables. Simulation studies and a case study in a plasma spray manufacturing process are used to demonstrate the effectiveness of the proposed method.

Note to Practitioners—Emerging sensing and information technologies have provided unprecedented functional data collection capacity from systems over time. It is important and challenging to model the relationships among these variables. Among many different modeling alternatives, graphical models are powerful tools to characterize the underlying relationships among variables in systems. However, traditional graphical models cannot directly model a system with a mixture of functional and scalar variables. The proposed model aims to address this challenge by proposing a functional graphical model. Based on simulation studies and a case study for a plasma spray manufacturing process, it is shown that the proposed method performs well under various conditions. [ABSTRACT FROM PUBLISHER]

In the disaster and remote regions, unmanned aerial vehicles (UAVs) can assist the data acquisition for Internet of Things (IoT). How to cover massive IoT devices (IDs), which require diverse quality-of-service (QoS), is a crucial challenge. For UAV-assisted IoT, this paper studies the deployment scheme with QoS guarantee to place multiple UAVs for covering all ground IDs and maximizing the average data rate of UAVs. First, for the ground ID, we propose the QoS demand based power allocation (QDPA) algorithm to solve the diversity of QoS with respect to data rate demand. Then, the data rate maximization placement (DRMP) algorithm is proposed to optimize the placement of single UAV. Finally, based on QDPA and DRMP algorithms, we propose the joint power allocation and placement (JPAP) scheme with QoS guarantee, which can cover massive IDs, to deploy multiple UAVs for maximizing the average UAV data rate. Simulation experiments are conducted to verify the superiority of our proposed JPAP scheme, which can minimize the UAV number and maximize the average data rate of UAVs. [ABSTRACT FROM AUTHOR]

Our work in this paper stems from our insight that recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. Therefore, it may be feasible that we could adapt these techniques in such a way that they will provide valuable solutions to certain tricky problems in the wireless sensor network (WSN) domain. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL leverages one result from OVR to prove that the problem formulation is inherently NP-hard. Therefore, we proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the algorithm scalable for large-scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to compare its performance to related protocols in both simulations and a hardware testbed. [ABSTRACT FROM PUBLISHER]

Two-dimensional magnetic recording (TDMR) offers the opportunity to provide areal density (AD) gain, but questions were unanswered as to how the gain is achieved and what can be done to maximize the gain from this new technology. In this paper, we offer some reasons why different investigators might report different AD gain opportunities. We present data collected on a spin stand with two reader heads and processed with a commercially available field programmable gate array TDMR channel. The implications of this paper should provide guidelines on reader geometries, placement, and performance. [ABSTRACT FROM AUTHOR]

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data are offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit-sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated the performance superiority of our proposal as compared with the baselines. [ABSTRACT FROM AUTHOR]

To achieve the aim of the international thermonuclear experimental reactor (ITER) radial neutron camera diagnostic, the data acquisition prototype must be compliant with a sustained 2-MHz peak event per each channel. The data are acquired and processed using an IPFN FPGA Mezzanine Card (FMC-AD2-1600) with two digitizer channels of 12-bit resolution and a sampling rate up to 1.6 GSamples/s mounted in an peripheral component interconnect express (PCIe) evaluation board from Xilinx (KC705) installed in the host PC. The acquired data in the event-based data path are streamed to the host through the PCIe $\times 8$ direct memory access with a maximum data throughput per channel $\approx 0.5$ GB/s of raw data (event base), $\approx 1$ GB/s per digitizer, and up to 1.6 GB/s in continuous mode. The prototype architecture comprises a host PC with two KC705 modules and four channels, producing up to 2 GB/s in event mode and up to 3.2 GB/s in continuous mode. To reduce the produced data throughput from host to ITER archiving system, the real-time data compression was evaluated using the LZ4 lossless compression algorithm, which provides compression speed up to 400 MB/s per core. This paper presents the architecture, implementation, and test of the parallel real-time data compression system running in multiple isolated cores. The average space saving and the performance results for long-term acquisitions up to 30 min, using different data block sizes and different number of CPUs, are also presented. [ABSTRACT FROM AUTHOR]

The compressed baryonic matter (CBM) experiment will investigate the quantum chromodynamics phase diagram at high net baryon densities and moderate temperatures. CBM time-of-flight (TOF) system is composed of super modules containing high-performance multigap resistive plate chambers (MRPCs). During the mass production, each super module assembled with MRPCs needs quality evaluation, which includes time measurement and data readout. Read out electronics encounter the challenge of reading data from a super module at a speed of about 6 Gb/s. In this paper, a read out method based on peripheral component interconnect express (PCIe) over optical fiber is proposed for CBM-TOF super module quality evaluation. The digitized data from super module will be concentrated at the front-end electronics and then be transmitted to a PCIe switch module (PSM) over optical fiber using PCIe protocol. The PSM is directly plugged into the motherboard via vertical edge card connector at the back-end data acquisition server. With this readout method, a high-speed transmission rate can be reached. Furthermore, a PSM can receive data from several super modules simultaneously, which is important to improve the evaluation efficiency. This readout method simplifies the architecture of readout electronics and supports long-distance transmission between front end and back end. [ABSTRACT FROM AUTHOR]

Focusing on generalized sensor combinations, this paper deals with the attitude estimation problem using a linear complementary filter (CF). The quaternion observation model is obtained via a gradient descent algorithm. An additive measurement model is then established according to derived results. The filter is named as the generalized CF where the observation model is simplified as a linear one that is quite different from previous-reported brute-force nonlinear results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving the free-living environment, harsh external field disturbances, and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption, and so on with representative methods. The results show that not only the proposed filter can give fast, accurate, and stable estimates in terms of various sensor combinations but also produces robust attitude estimation in the scenario of harsh situations, e.g., irregular magnetic distortion. Note to Practitioners—Multisensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper, we make the problem more concise. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future. [ABSTRACT FROM AUTHOR]

In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols. [ABSTRACT FROM AUTHOR]

Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated (r =0.78), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics—the adjacency bit vector and node degree—from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

Satellite-based communication technology has gained much attention in the past few years, where satellites play mainly the supplementary roles as relay devices to terrestrial communication networks. Unlike previous work, we treat the low-earth-orbit (LEO) satellites as secure data storage mediums. We focus on data acquisition from a LEO satellite based data storage system (also referred to as the LEO based datacenters), which has been considered as a promising and secure paradigm on data storage. Under the LEO based datacenter architecture, one fundamental challenge is to deal with energy-efficient downloading from space to ground while maintaining the system stability. In this paper, we aim to maximize the amount of data admitted while minimizing the energy consumption, when downloading files from LEO based datacenters to meet user demands. To this end, we first formulate a novel optimization problem and develop an online scheduling framework. We then devise a novel coflow-like “Join the first $K$ K -shortest Queues (JKQ)” based job-dispatch strategy, which can significantly lower backlogs of queues residing in LEO satellites, thereby improving the system stability. We also analyze the optimality of the proposed approach and system stability. We finally evaluate the performance of the proposed algorithm through conducting emulator based simulations, based on real-world LEO constellation and user demand traces. The simulation results show that the proposed algorithm can dramatically lower the queue backlogs and achieve high energy efficiency. [ABSTRACT FROM AUTHOR]

Due to advantages of handling problems with nonlinearity and uncertainty, Gaussian process regression (GPR) has been widely used in the area of driver behaviour modelling. However, traditional GPR lacks the ability of transferring knowledge from one driver to another, which limits the generalisation ability of GPR, especially when sufficient data for driver behaviour modelling are not available. To solve this limitation, in this paper, a novel GPR model, Importance Weighted Gaussian Process Regression (IWGPR) is proposed. The importance weight (IW) represents the probabilistic density ratio between two drivers and the unconstrained least-squares importance fitting (ULSIF) is applied to calculate IW. Meanwhile, an IW-based model selection (IWMS) method is proposed to help the model select optimal parameters. Using IWGPR, sufficient historical data collected from one driver can be used to model another driver with insufficient data, and thus improve the generalisation ability of GPR. To verify the proposed algorithm, a toy regression problem is used to illustrate the working mechanism of IWGPR. With simulated and naturalistic driving data, three experiments for driver behaviour modelling in the lane change scenario, are designed and carried out. Experimental results indicate that IWGPR performs better than GPR when sufficient data are not provided by the new driver, which proves the generalisation ability of IWGPR. Meanwhile, the comparative study between different transferable driver behaviour learning methods is detailed and analysed. [ABSTRACT FROM AUTHOR]

This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances. [ABSTRACT FROM AUTHOR]

Under the paradigm of edge computing, the enormous data generated at the network edge can be processed locally. To make full utilization of these widely distributed data, we focus on an edge computing system that conducts distributed machine learning using gradient-descent based approaches. To ensure the system’s performance, there are two major challenges: how to collect data from multiple data source nodes for training jobs and how to allocate the limited resources on each edge server among these jobs. In this paper, we jointly consider the two challenges for distributed training (without service requirement), aiming to maximize the system throughput while ensuring the system’s quality of service (QoS). Specifically, we formulate the joint problem as a mixed-integer non-linear program, which is NP-hard, and propose an efficient approximation algorithm. Furthermore, we take service placement into consideration for diverse training jobs and propose an approximation algorithm. We also analyze that our proposed algorithm can achieve the constant bipartite approximation under many practical situations. We build a test-bed to evaluate the effectiveness of our proposed algorithm in a practical scenario. Extensive simulation results and testing results show that the proposed algorithms can improve the system throughput 56-69 percent compared with the conventional algorithms. [ABSTRACT FROM AUTHOR]

Undersampling the k-space during MR acquisitions saves time, however results in an ill-posed inversion problem, leading to an infinite set of images as possible solutions. Traditionally, this is tackled as a reconstruction problem by searching for a single “best” image out of this solution set according to some chosen regularization or prior. This approach, however, misses the possibility of other solutions and hence ignores the uncertainty in the inversion process. In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process. To this end, we introduce a low dimensional latent space and model the posterior distribution of the latent vectors given the acquisition data in k-space, from which we can sample in the latent space and obtain the corresponding images. We use a variational autoencoder for the latent model and the Metropolis adjusted Langevin algorithm for the sampling. We evaluate our method on two datasets; with images from the Human Connectome Project and in-house measured multi-coil images. We compare to five alternative methods. Results indicate that the proposed method produces images that match the measured k-space data better than the alternatives, while showing realistic structural variability. Furthermore, in contrast to the compared methods, the proposed method yields higher uncertainty in the undersampled phase encoding direction, as expected. [ABSTRACT FROM AUTHOR]

Kernel methods have achieved tremendous success in the past two decades. In the current big data era, data collection has grown tremendously. However, existing kernel methods are not scalable enough both at the training and predicting steps. To address this challenge, in this paper, we first introduce a general sparse kernel learning formulation based on the random feature approximation, where the loss functions are possibly non-convex. In order to reduce the scale of random features required in experiment, we also use that formulation based on the orthogonal random feature approximation. Then we propose a new asynchronous parallel doubly stochastic algorithm for large scale sparse kernel learning (AsyDSSKL). To the best our knowledge, AsyDSSKL is the first algorithm with the techniques of asynchronous parallel computation and doubly stochastic optimization. We also provide a comprehensive convergence guarantee to AsyDSSKL. Importantly, the experimental results on various large-scale real-world datasets show that, our AsyDSSKL method has the significant superiority on the computational efficiency at the training and predicting steps over the existing kernel methods. [ABSTRACT FROM AUTHOR]

In order to reduce the gap between the laboratory environment and actual use in daily life of human-machine interaction based on surface electromyogram (sEMG) intent recognition, this paper presents a benchmark dataset of sEMG in non-ideal conditions (SeNic). The dataset mainly consists of 8-channel sEMG signals, and electrode shifts from an 3D-printed annular ruler. A total of 36 subjects participate in our data acquisition experiments of 7 gestures in non-ideal conditions, where non-ideal factors of 1) electrode shifts, 2) individual difference, 3) muscle fatigue, 4) inter-day difference, and 5) arm postures are elaborately involved. Signals of sEMG are validated first in temporal and frequency domains. Results of recognizing gestures in ideal conditions indicate the high quality of the dataset. Adverse impacts in non-ideal conditions are further revealed in the amplitudes of these data and recognition accuracies. To be concluded, SeNic is a benchmark dataset that introduces several non-ideal factors which often degrade the robustness of sEMG-based systems. It could be used as a freely available dataset and a common platform for researchers in the sEMG-based recognition community. The benchmark dataset SeNic are available online via the website (https://github.com/bozhubo/SeNic and https://gitee.com/bozhubo/SeNic). [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) energy consumption and task completion time are two significant aspects in UAV-enabled Internet of Things (IoT) data acquisition. However, while energy consumption and task completion time are not conflicting constraints, optimizing the former often does not automatically lead to optimization of the latter, and vice-versa. In this paper, we aim to further decrease UAV energy consumption while minimizing UAV task completion time. We propose a novel UAV utility function to describe task completion time for data acquisition and energy consumption for propulsion and receiver's circuits. Then, we formulate a mixed-integer nonconvex utility maximization problem, involving joint optimization of UAV trajectory, velocity, acceleration, task completion time and scheduling of IoT devices for data uploading. To tackle this problem, we decompose the original problem into two sub-problems and propose a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm, which converges in polynomial time. Accordingly, we apply block coordinate descent (BCD) and successive convex approximation (SCA) algorithms to optimize UAV trajectory, and bisection search algorithm to minimize task completion time, respectively. Simulation results show that the proposed data acquisition scheme significantly decreases the UAV's energy consumption while minimizing task completion time, compared with the other time-minimization schemes. [ABSTRACT FROM AUTHOR]

Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold: i. data coverage is sparse; ii. data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g., air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms? This paper presents adaptive HMSS, an adaptive hybrid model-enabled sensing system for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of a physics guided model and a data driven model. To address the challenge of sparse coverage, the physical understanding of the spatiotemporal correlation for air pollution distribution in the physics guided model is utilized to infer values at unsensed sparse areas. Meanwhile, the data driven model is adopted to estimate the air pollution influential factors (e.g., buildings) not included in the physics guided model. To address the challenge of time-varying coverage, an adaptive model combination algorithm is designed to enable the system bias to either of the two models according to the amount of data collection and uncertainty of the model. To evaluate the system performance, we deployed 47 air pollution sensing devices on taxis and fixed locations in 2 cities for both controlled and uncontrolled experiments for over two weeks. The results show that with a resolution of $500 \;\mathrm m$ 500 m by $500\;\mathrm m$ 500 m by $1\;\mathrm {hour}$ 1 hour , our system achieves up to $3.2\times$ 3. 2 × error reduction when compared to the baseline approaches. [ABSTRACT FROM AUTHOR]

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community. [ABSTRACT FROM AUTHOR]

In this paper, the target patrolling problem was considered, in which a set of mobile data collectors, known as data mules (DMs), must efficiently patrol a given set of targets. Because the time interval (or visiting interval) between consecutive visits to each target reflects the degree to which that target is monitored, the goal of this paper was to balance the visiting interval of each target. This paper first presents the basic target points patrolling algorithm, which enables an efficient patrolling route to be constructed for numerous DMs, such that the visiting intervals of all target points are stable. For scenarios containing weighted target points, a weighted target points patrolling (W-TPP) algorithm is presented, which ensures that targets with higher weights have higher data collection frequencies. The energy constraint of each DM was also considered, and this paper presents a W-TPP with recharge (RW-TPP) algorithm, which treats the energy recharge station as a weighted target and arranges for DMs to visit the recharge station before running out of energy. The performance results demonstrated that the proposed algorithms outperformed existing approaches in average visiting frequency, DM movement distance, average quality of monitoring satisfaction rate, and efficiency index. [ABSTRACT FROM PUBLISHER]

Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2-D plane based on the CSMA/CA protocol. Due to UAV's continuous mobility, the communication durations of devices in different locations with UAV are not only time-limited, but also vary from each other. To analyze the throughput performance of uplink multiple access control (MAC) protocol, we propose a new analysis model to deal with the communications heterogeneity in the network. Firstly, we divide the devices in the coverage into different clusters according to their communication durations. Then, a quitting probability indicating the probability that a device quits the UAV's coverage at each time slot is clarified. A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis. Besides, we also propose a modified CSMA/CA protocol which fully considers the heterogeneity of the access time and adaptively allocates the time resource among the devices in different clusters. Finally, the effects of retry limit, initial contention window size, the density of the devices, UAV's speed and coverage area are discussed in the simulation section. [ABSTRACT FROM AUTHOR]

Vehicles equipped with sensors can participate in mobile crowdsourcing applications. Vehicular Ad Hoc Networks (VANETs) based on Dedicated Short Range Communication (DSRC) are used to carry sensing data. However, multi-hop transmissions for gathering data to Road Side Units (RSUs) in VANETs suffer from low data rate and long end-to-end delay, which can hardly meet the QoS requirements of delay-sensitive services. This triggers the consideration of constituting a DSRC and Cellular-Vehicle-to-Everything (C-V2X) hybrid vehicular network. Nevertheless, using cellular links to carry traffic can cause high cellular bandwidth costs. In this paper, we propose a Traffic Differentiated Clustering Routing (TDCR) mechanism in a Software Defined Network (SDN)-enabled hybrid vehicular network. The proposed mechanism includes a centralized one-hop clustering approach and a data delivery optimization method. Particularly, the optimization is to make a tradeoff between cellular bandwidth cost and end-to-end delay, for Cluster Heads (CHs) delivering their aggregated data either by multi-hop Vehicle-to-Vehicle (V2V) transmissions or by cellular networks. Since the problem is proven to be NP-hard, a two-stage heuristic algorithm is designed. We carry out simulations to evaluate the performance of our data collection scheme and the results show that it performs better than traditional mechanisms. [ABSTRACT FROM AUTHOR]

Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete data set for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved. One such an example is the use of Simultaneous Multiple Artifacts Reduction in Tomographic RECONstruction (SMART-RECON) technique. However, with SMART-RECON, one can only improve temporal resolution up to 1 frame per second (fps) which is an improvement of 4.5 times over that of the conventional FBP reconstruction. In this paper, a new technique referred to as enhanced SMART-RECON (eSMART-RECON) was introduced to enhance the temporal performance of SMART-RECON in a multi-sweep CBCT data acquisition protocol. Both numerical simulation studies with ground truth and in vivo human subject studies using C-arm CBCT acquisition systems were conducted to demonstrate the following key results: for a multi-sweep CBCT acquisition protocol, eSMART-RECON enables 4–7.5 fps temporal resolution for TR-CBCT which is 4–7.5 times better than that offered by the original SMART-RECON, and 18–34 times better than that offered by the conventional FBP reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of a new oscillatory component load (OCL) model for a measurement-based real-time estimation of dynamic load model parameters. The proposed OCL model has been derived from a second-order differential equation that considers three necessary components, namely static, exponential recovery, and damped oscillatory components. The performance evaluation of the proposed OCL model has been demonstrated through DIgSILENT PowerFactory-based dynamic power system simulations, real-time substation data captured from phasor measurement units located at the primary substations of Leadenham, Holwell, and Regent Street cities, and real-time transient voltage events data provided by the EirGrid. Further, the effectiveness of the proposed OCL model has been assessed by comparing its performance with that of the existing exponential recovery load model. A new flexible and efficient SynchroHub platform, developed at the University of Manchester for real-time data acquisition, processing and visualization, has been used for the real-time estimation of dynamic load model parameters. The validation results revealed the efficacy of the proposed OCL model in tracking both the first as well as the subsequent oscillations of dynamic load model response of the power system for both the steady state as well as transient operating conditions accurately. [ABSTRACT FROM AUTHOR]

Motion systems with multiple control loops often run at a single sampling rate for simplicity of implementation and controller design. The achievable performance in terms of position accuracy is determined by the data acquisition hardware, such as sensors, actuators, and analog-to-digital/digital-to-analog converters, which is typically limited due to economic cost considerations. The aim of this paper is to develop a multirate approach to go beyond this traditional performance/cost tradeoff, i.e., to use different sampling rates in different control loops to optimally use hardware resources. The approach appropriately deals with the inherent time-varying behavior that is introduced by multirate sampling. A multirate feedforward control design framework is presented to optimize the tracking of a dual-stage multirate system. The application of the proposed approach to an industrial dual-stage wafer system demonstrates the advantages of multirate control, both in simulations and experiments. [ABSTRACT FROM AUTHOR]

Objective: Fluorescence molecular tomography (FMT) can provide valuable molecular information by mapping the bio-distribution of fluorescent reporter molecules in the intact organism. Various prototype FMT systems have been introduced during the past decade. However, none of them has evolved as a standard tool for routine biomedical research. The goal of this paper is to develop a software package that can automate the complete FMT reconstruction procedure. Methods: We present smart toolkit for fluorescence tomography (STIFT), a comprehensive platform comprising three major protocols: 1) virtual FMT, i.e., forward modeling and reconstruction of simulated data; 2) control of actual FMT data acquisition; and 3) reconstruction of experimental FMT data. Results: Both simulation and phantom experiments have shown robust reconstruction results for homogeneous and heterogeneous tissue-mimicking phantoms containing fluorescent inclusions. Conclusion: STIFT can be used for optimization of FMT experiments, in particular for optimizing illumination patterns. Significance: This paper facilitates FMT experiments by bridging the gaps between simulation, actual experiments, and data reconstruction. [ABSTRACT FROM AUTHOR]

Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations. [ABSTRACT FROM AUTHOR]

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers’ noisy or even conflicting sensory data, truth discovery algorithms, which jointly estimate workers’ data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers’ strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism, named Theseus, that deals with workers’ such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers’ data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility. We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations. [ABSTRACT FROM AUTHOR]