The importance of vibration monitoring in the Internet of Things (IoT) is reflected in many aspects, especially in the fields of industry, infrastructure, health monitoring, etc. Laser heterodyne interferometer has been widely used in the measurement of vibration displacement and velocity. The phase demodulation method of the Doppler signal is crucial to realizing the real-time and high precision measurement with wide bandwidth. To reduce the requirements for the data acquisition system and the resource consumption, the improved digital arctangent demodulation method is proposed, which is more suitable to run on the digital signal processor. Utilizing the symmetry of the Doppler signal spectrum, the Doppler signal can be acquired by special Nyquist or bandpass sampling rates. Then the pair of orthogonal signals are generated by specific delays of the digitized Doppler signals. With the sine approximation method (SAM), the vibration displacement or velocity can be obtained from the unwrapped phase after the arctangent calculation. Compared with the classical arctangent demodulation method and the commercial decoder of the laser Doppler vibrometer (LDV), experiments are designed to demonstrate the feasibility and performance of the proposed method with the vibration frequency from 500 Hz to 1 MHz and the peak amplitude of the vibration velocity from 31.63 lm/s to 3.16 m/s. Additionally, simulation experiments further explore its applicability in demodulating Doppler signals with asymmetric spectrum. The improved digital arctangent demodulation method offers significant potential for developing Doppler signal demodulation systems for laser heterodyne interferometers, particularly in real-time, efficient, and high-precision vibration monitoring applications.

Wind noise caused by turbulent flows over microphones usually has detrimental impacts on the reference signal of an active noise control (ANC) system and degrade its performance considerably. This paper evaluates the influence of wind noise on the filtered-x affine projection algorithm (FxAPA) for ANC systems. To improve the performance of noise control systems in windy environments, the FxAPA is modified based on a wind-noise-free least mean squares error and the diffusion control technology that could suppress interruptions in the collected primary noise. Performance analysis of the resulting de- regularized diffusion FxAPA (Diff-FxAPA) algorithm has been carried out at the presence of wind noise, which reveals the underlying mechanism of the new algorithm on suppressing the wind noise interruption. Difference equations describing the mean and mean squares convergence behaviors of this ANC system are derived to characterize its optimal solution, estimation bias and variance, and convergence conditions. Computer simulations with real-recorded data validate the theoretical analysis and show that the noise reduction performance of conventional ANC methods degrades with wind noise while the proposed algorithm has a stable performance under a variety of windy conditions.

This paper proposes an online torque compensation-based DC-biased sinusoidal excitation scheme for switched reluctance motors (SRMs) with torque ripple minimization capability. Firstly, the instantaneous torque model is deeply analyzed in the DC-biased sinusoidal current-excited SRM drive by fully considering the harmonic components of phase inductance. Secondly, the specific zero-sequence current constituents are complemented into the phase windings to compensate the specific alternative torque components, and the magnitude of biased DC current is determined by the extreme point determination method. Thirdly, considering the inevitable modeling error, the online torque compensation is proposed to smooth the torque ripple in the featured positions. Finally, the effectiveness of the proposed scheme is verified in MATLAB/Simulink based on a three-phase 12/8-pole SRM prototype.

With the fast advances in Internet technologies, social networks have become a major platform for social interaction, lifestyle demonstration, and message dissemination. Effective community detection in social networks helps to assess public sentiment, identify community leaders, and produce personalized recommendation. While different community detection approaches have been proposed in the literature, the trust model based detection schemes model user interactions as trust transfer, which helps to capture the implicit relation in the network. Unfortunately, trust model based detection schemes face acold startproblem, i.e., they cannot accurately model newly joined users as these users have few interactions for a duration after joining the network. In this paper, we propose TLCDA, a novel trust model based community detection algorithm. By enhancing the traditional trust computation with inter-node relation strength and similarity in social networks, TLCDA detects communities through coarse-grained K-Mediods clustering. Our evaluation on real social networks shows that the communities detected by TLCDA exhibit superior preference cohesion while satisfying the topology cohesion.

The setting of railway station waiting room and waiting zones relates to passengers' feeling and satisfaction. In this article, the authors develop an optimization model for railway station waiting room assignment, as well as considering adjustment of platforms. With four types of improvement strategies: zone optimization, room optimization, time optimization and interactive priority policy, this optimal model aims to effectively and efficiently improve the railway service quality and security.

: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

Markov logic is a knowledge-representation language that allows one to specify large graphical models. However, the resulting large graphical models can make inference for Markov logic a computationally challenging problem. Recently, dual decomposition (DD) has become a popular approach for scalable inference on graphical models. We study how to apply DD to scale up inference in Markov logic. A standard approach for DD first partitions a graphical model into multiple tree-structured sub problems. We apply this approach to Markov logic and show that DD can outperform prior inference approaches. Nevertheless, we observe that the standard approach for DD is suboptimal as it does not exploit the rich structure often present in the Markov logic program. Thus, we describe a novel decomposition strategy that partitions a Markov logic program into parts based on its structure. A crucial advantage of our approach is that we can use specialized algorithms for portions of the input problem -- some of which have been studied for decades, e.g., coreference resolution. Empirically, we show that our program-level decomposition approach outperforms both non-decomposition and graphical model-based decomposition approaches to Markov logic inference on several data-mining tasks.

Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary’s approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called Hogwild which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then Hogwild achieves a nearly optimal rate of convergence. We demonstrate experimentally that Hogwild outperforms alternative schemes that use locking by an order of magnitude.

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.

The Internet is composed of multiple economically-independent service providers that sell bandwidth in their networks so as to maximize their own revenue. Users, on the other hand, route their traffic selfishly to maximize their own utility. How does this selfishness impact the efficiency of operation of the network? To answer this question we consider a two-stage network pricing game where service providers first select prices to charge on their links, and users pick paths to route their traffic. We give tight bounds on the price of anarchy of the game with respect to social value--the total value obtained by all the traffic routed. Unlike recent work on network pricing, in our pricing game users do not face congestion costs; instead service providers must ensure that capacity constraints on their links are satisfied. Our model extends the classic Bertrand game in economics to network settings.

We present a novel genre-independent SVM framework for detecting scene changes in broadcast video. Our framework works on content from a diverse range of genres by allowing sets of features, extracted from both audio and video streams, to be combined and compared automatically without the use of explicit thresholds. For ground truth, we use hand-labeled video scene boundaries from a wide variety of broadcast genres to generate positive and negative samples for the SVM. Our experiments include high-and low-level audio features such as semantic histograms and distances between Gaussian models, as well as video features such as shot cut positions. We evaluate the importance of these measures in a structured frame-work, with performance comparisons obtained via ROC curves. We achieve over 70% detection rate for 10% false positive rate on our corpus of over 7.5 hours of data collected from news, talk shows, sitcoms, dramas, music videos, and how-to shows.

Distributed sensing, a system-wide deployment of sensing devices, has resulted in both temporally and spatially dense data-rich environments. This new technology provides unprecedented opportunities for quality and productivity improvement. This paper discusses the state-of-the-art practice, research challenges, and future directions related to distributed sensing. The discussion includes the optimal design of distributed sensor systems, information criteria, and processing for distributed sensing and optimal decision making in distributed sensing. The discussion also provides applications based on the authors' research experiences. Note to Practitioners—This paper is based on a panel discussion on the topic of the emerging technology of distributed sensing and the associated challenges and opportunities. The panel, constituted by a group of leading researchers and practitioners with expertise in operations and statistics, convened during the Institute for Operations Research and the Management Sciences (INFORMS) 2003 annual meeting in Atlanta, GA. This panel focused its discussion on the information layer technology of distributed sensing for quality and productivity improvements, which differentiates this panel from other similar panels that were formed in a different society. The panelists provided their visions about the state-of-the-art practice, research challenges, and future research directions, and also discussed potential applications based on their own experiences.

Customer feedback in the form of warranty/field performance is an important direct indicator of quality and robustness of a product. Linking warranty information to manufacturing measurements can identify key design and process variables that are related to warranty failures. Warranty data have been traditionally used in reliability studies to determine failure distributions and warranty cost. This paper proposes a novel fault region localization methodology to link warranty failures to manufacturing measurements (hence, to design and process parameters) for diagnosing warranty failures and to perform tolerance revaluation. The methodology consists of identifying relations between warranty failures and design/process variables using rough sets-based analysis on training data consisting of warranty information and manufacturing measurements. The methodology expands the rough set-based analysis by introducing parameters for inclusion of noise and uncertainty of warranty data classes. Based on the identified parameters related to the failure, a revaluation of the original tolerances can be performed to improve product robustness. The proposed methodology is illustrated using case studies of two warranty failures from the electronics industry. Note to Practitioners—Warranty failures are indicative of the performance and robustness of the product. Warranty failures, especially those that occur early (e.g., within six months after sale), can be caused by interactions between various design and process characteristics of the individual components of the product. Due to the large number of components and the interactions between them, it is difficult to identify all of these relations during design. Furthermore, it is difficult to replicate actual product usage in the field during the design stage. The methodology proposed in this paper integrates a product's warranty failure information with that of measurement data collected during manufacturing, to identify relevant design and process variables related to the failures. It also identifies the warranty fault region within the original design tolerance window for the parameters. This can help in avoiding warranty failure(s) through design changes and/or tolerance revaluation. The methodology was applied in the electronics and semiconductor industries.

Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single activity. In this paper, we present a novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities. Experiments on a database of video clips of different activities show that our method is effective

In this paper, we present new analytical, simulated, and experimental results on the performance of relative location estimation in multihop wireless sensor networks. With relative location, node locations are estimated based on the collection of peer-to-peer ranges between nodes and their neighbors using a priori knowledge of the location of a small subset of nodes, called reference nodes. This paper establishes that when applying relative location to multihop networks the resulting location accuracy has a fundamental upper bound that is determined by such system parameters as the number of hops and the number of links to the reference nodes. This is in contrast to the case of single-hop or fully connected systems where increasing the node density results in continuously increasing location accuracy. More specifically, in multihop networks for a fixed number of hops, as sensor nodes are added to the network the overall location accuracy improves converging toward a fixed asymptotic value that is determined by the total number of links to the reference nodes, whereas for a fixed number of links to the reference nodes, the location accuracy of a node decreases the greater the number of hops from the reference nodes. Analytical expressions are derived from one-dimensional networks for these fundamental relationships that are also validated in two-dimensional and three-dimensional networks with simulation and UWB measurement results.

We present the analytic and simulation results of the performance of relative location estimation in multi-hop wireless sensor networks. It is found that the location estimation accuracy has a fundamental upper boundary. Location accuracy improves by adding more mobile nodes into the network. However, the resulting accuracy converges towards a fixed asymptotic value that is determined by the total number of links to the reference nodes. It is also established that for multi-hop networks, the location accuracy of a node is highly dependent upon the number of hops it is away from the reference nodes. The further away it is from the reference nodes, the worse the location accuracy.

New MEMS resonating structures using standard CMOS IC processes are proposed and analytical models for proposed structures are established. The static/dynamic analysis for these MEMS resonating structures using developed models and commercially available simulation tools is conducted and results are presented. Theoretical formulas governing the performance critical parameters are derived for the MEMS trench resonators and the results show that the performance of these MEMS resonators as measured by the resonator capacitance ratio improves with shrinking resonator size. These intrinsically low cost and small size devices are a key enabling technology for pervasive device applications.