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The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.

Deadlock prevention policies are used to solve the deadlock problems of FMSs. The theory of regions is recognized as the unique method for obtaining maximally permissive (i.e., optimal) controllers in the existing literature. In this paper, the selective siphons and critical markings method [17] is merged in the new deadlock prevention policy. First of all, reachability graph is still needed. Second, CMTSIs are identified. Further, selective siphons and critical markings method is used to check if all dead/quasi-dead markings of CMTSIs are covered by these selective siphons. Furthermore, choose anyone CMTSI that belongs to a same selective siphon to be processed. Finally, controllers are therefore obtained. Experimental results indicate that the computational cost can be reduced again. Besides, it is the most efficient policy to obtain maximal permissive behavior of Petri net models.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

In this paper, the generalized detector (GD) constructed on the basis of the generalized approach to signal processing in noise is employed by synthetic aperture radar (SAR) system to define a target location. The GD is proposed to solve the common problem of target detection at the very low signal-to-noise-ratio (SNR) when the conventional detection algorithms are not able to do it. Simulation results demonstrate a GD performance superiority in comparison with the search for undefined maritime objects (SUMO) detector one.

Delivering reliable service requires satisfactory operation of numerous line components, spanning significant geographical areas and subject to environmental, electrical, and mechanical stresses, plus normal aging. Conventional reliability practices use periodic maintenance to minimize failures, coupled with rapid restoration procedures when outages occur. Reliability statistics have shown little change in decades. Newer technologies can shorten outages, by automatically rerouting service to customers on healthy feeder segments, but they: require redundant paths for power flow; react only after outages occur; and often require communications with pole-mounted switches. Over the past decade, Texas A&M University has demonstrated that electrical waveforms, available via conventional substation-based CTs and PTs, contain evidence of failures, incipient failures, and improper operation of feeder equipment, often well before these conditions escalate and cause outages. This provides the basis for conditioned-based maintenance and outage avoidance. This paper provides examples, from operating feeders, demonstrating some of these advances.

The Cygnus Dual Beam Radiographic Facility consists of two identical radiographic sources each with a dose rating of ∼4-rad at 1 m, and a ∼1-mm diameter spot size. The development of the rod pinch diode (RPD) was responsible for the ability to meet these criteria. The RPD in each machine uses a 0.75-mm tungsten diameter tapered anode rod extended through a 9-mm diameter cathode aperture. When properly configured, the electron beam born off the aperture edge can self-insulate and pinch onto the tip of the rod creating an intense, small x-ray source. These sources are utilized in concert with an imaging system on complex experiments, which are single-shot, high-value events. In such an application there is an emphasis on machine performance (reliability and reproducibility). Vacuum quality is a significant determinator for source performance. In this paper we discuss the Cygnus Dual Beam Radiographic Facility, the associated vacuum systems, vacuum monitor calibration and a multi-purpose vacuum chamber. Vacuum system performance, using various enhancements, will be reported. Using the vacuum system enhancements, as well as other machine tweaks, recent source performance has improved. This new level of performance will be presented via dosimetry and electrical measurements.

This paper presents a method for characterizing chemical solutions using nanowire field effect transistors. A pulsed gate potential method is used to prevent instabilities related to the dynamics of ions and other charged species present in the solution. Applying this method realizes a significant increase of the stability of the drain current versus gate potential characteristics of the devices, enabling reproducible characterization of chemical solutions with nanowire field effect transistors in aqueous environments.

Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

Ontologies have been designed for the purpose of representing a domain of knowledge in a way that makes it understandable by machines and humans. Ontologies have been used in many areas in computer sciences such as knowledge engineering, semantic web, and information retrieval. Religious studies is one of the areas were ontologies are in demand. In this paper, we present the design of domain ontology about prophets and messengers in Islam from trustworthy resources: the Quran and Hadith. The ontology can be used to better understanding all life aspects of prophets and messengers in Islam. The classes, relations and properties of this special domain ontology were gathered and integrated to help researchers to identify and explore knowledge about prophets in Islam such as their lives, books and teachings, the nature of the messages they brought to their nations and tribes and much more.

Air quality and fuel efficiency has become important factors in decision-makings on urban traffic planning and management. To support the process, simulation models have potential to play essential roles in evaluation of planning alternatives and control strategies. However, traffic and its environmental impacts are different processes and often require various levels of models. With concerns on high computing performance and rich functionalities, it may be not appropriate to model emission inventory within traffic simulation. In this paper, we present a distributed simulation approach, and an independent emission/energy computing platform is built to simulate, visualize and analyze online emission outputs, given a microscopic traffic simulation tool, KTH-TPMA. Two distributed computing frameworks, common objects request broker architecture (CORBA) and service oriented architecture (SOA), are adopted in the distributed software design and implementation. Several emission models are implemented and generally evaluated in micro simulation runs of two road networks.

Gain-cell embedded DRAM (GC-eDRAM) is an attractive alternative to traditional SRAM, due to its high-density, low-leakage, and inherent 2-ported operation, yet, its dynamic nature leads to limited retention time that requires periodic, power-hungry refresh cycles. This drawback is further aggravated in scaled technologies, where increased leakage currents and decreased in-cell storage capacitances lead to accelerated data integrity deterioration. However, the emerging approximate computing paradigm utilizes the inherent error resilience of some applications to tolerate data errors. Such error tolerance can be exploited by reducing the refresh rate in GC-eDRAM to achieve a substantial decrease in power consumption, at the cost of an increase in cell failure probability. In this paper, we present the first fabricated and fully functional GC-eDRAM in a 28 nm bulk CMOS technology. The array, which is based on a novel mixed-V t 4T bitcell, can be used in both traditional and for approximate computing applications, featuring a small silicon footprint and supporting high-performance operation. Silicon measurements demonstrate successful operation at 800 Mhz under a 900 mV supply, while retaining almost 30% lower area than a single-ported 6T SRAM in the same technology.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

The learning behavior of the agent is a challenging and interesting issue in an unknown environment. Reinforcement learning obtain the developed strategy through exploration and interaction with the environment, and the characteristic of online learning make it as an important branch of machine learning research. In this paper, we summarize the current research of the reinforcement learning applications and hotspots. Firstly, the principle, structure and the main classic algorithms of the reinforcement learning are introduced. Secondly, according to the recent research results, we introduce four main applications of reinforcement learning, namely robot, unmanned aerial vehicle, multi-agent and intelligent traffic. Finally, the research hotspots and the development direction of the reinforcement learning are introduced, which conclude the partial perception, hierarchical reinforcement learning, combination with other artificial intelligence technologies and game theory.

Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for roadside safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

This paper discusses the performance of the first CrIS flight instrument and its data product performance. Together with ATMS, the CrIS sensor is a critical payload for National Polar-orbiting Operational Environmental Satellite System (NPOESS) and will first fly on the NPOESS Preparatory Project (NPP) mission, the risk reduction flight for NPOESS. NPOESS is the next generation weather and climate monitoring system for the U.S. Air Force and National Oceanic and Atmospheric Administration (NOAA), being developed under contract by Northrop Grumman Aerospace Systems. This paper describes instrument radiometric performance such as calibrated spectral response shape, error residuals and absolute spectral calibration of the CrIS Flight Model 1 (FM1) sensor including the signal processing algorithms used to radiometrically and spectrally calibrate the CrIS sensor. This includes a description of the CrIS ground algorithms that convert Raw Data Records (RDR) into calibrated Sensor Data Records (SDR) and then the SDRs into Environmental Data Records (EDR).

A compact microwave bandpass filter with λ/4 stepped impedance resonators (SIRs) is presented in this paper. λ/4 SIRs are employed here not only to miniaturize the circuit size, but also to eliminate spurious resonance at 2f 0 . To achieve the maximal number of finite transmission zeros, this filter is synthesized based on the 4th-order fully canonical coupling topology. As a result, four finite transmission zeroes are analytically achieved, and used to sharpen filter rejection skirts. Three capacitive J couplings are systematically designed to meet the required coupling strengths for the filter frequency response. Besides, they also adjust their coupling zeros for 3f 0 spurious suppression. Lastly, a 4th-order fully canonical bandpass filter is designed and fabricated with central frequency of 1.8 GHz and fractional bandwidth of 11.0% The proposed filter possesses preferred merits: compact size, 4 transmission zeros and up to 4.8f 0 20 dB attenuated stopband.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

This paper presents a new approach for an efficient decimator architecture for cascaded sigma-delta (/spl Sigma//spl Delta/) modulators. The special aspect of the presented structure is the effort to integrate the recombination logic for cascaded modulators in general and additionally a gain error correction for continuous time (CT) modulators in the first stage of the decimator. The design of an appropriate filter topology is derived, analyzed, and verified by simulations. The proposed structure is compared to a conventional implementation, thereby improving efficiency by decades.

Vehicles may move at high speed in vehicular ad-hoc networks (VANETs) and the network topology changes arbitrarily as the nodes move. Vehicles may not find suitable resources because sharing information could quickly become obsolete or irrelevant resources. This paper proposes an efficient geo-aware Peer-to-Peer (P2P) search mechanism to enable resource discovery and sharing in VANETs. We propose discovery and sharing schemes based on the virtual backbone architecture, and then limit the data exchanging region to reduce unnecessary message transmissions. Because the resource node changes very fast, the coordinator must be set to maintain the local node of geographic information in the network. In this scheme, coordinators are established to store information and manage local resource information. In order to ensure dissemination performance, it must guarantee that vehicles possess enough resource pieces to allow efficient bartering. Furthermore, three kinds of piece selection mechanisms, including Nearest-First, Rarest-First, and In-Order are proposed to download resource pieces from other vehicles and to improve the dissemination efficiency. Experiment results demonstrate that our searching and sharing schemes are efficient and outperforms other schemes in terms of satisfactory ratio and download time.

Insurance telematics is a disruptive technology that is expected to reform the vehicle insurance industry. Based on sensor data, the traditional measures for calculating the insurance premium are complemented to determine a fee that more accurately predicts the risk profile of the policyholder. From an instrumentation and measurement point of view, there are several insurance telematics challenges that have to be tackled. It is about consistently extracting relevant figures of merit (FoMs) like number of harsh braking, speeding, heavy cornering, trip smoothness, etc, and then to transform these FoMs into a valid measure, or score, that determines the risk profile of the insurance customer. The paper presents a characterization of FoMs in terms of actuarial relevance, the driver's influence, stationarity and observability, which are important for the given task. Dedicated hardware to be fitted into the on-board diagnostics outlet as well as contemporary smartphones are two different insurance telematics platforms for data collection. The paper discusses the properties of the smartphone in the perspective of insurance telematics as a low-cost alternative to the hardwired platforms like the one connected to the vehicle's on-board diagnostics outlet.

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.

This paper describes a new weighting sub-system for fruit sorting and grading machinery. Based on a fixed point DSP, it controls up to 10 lines at 20 fruit/second with an accuracy of /spl plusmn/1 gram. The system applies an adaptive filtering and a posterior deconvolution of the behaviour of the load sensor using an ARMA model. A final estimation procedure to obtain weight in grams is also described. The best results have been obtained with an ALMS algorithm for the adaptive filter.

In technical study programs on the bachelor level the focus is mainly directed on the scientific education in theory and practice. Publication is usually started during the PhD phase when students write their first scientific papers. During the last years some very successful projects were made at the university of applied sciences Regensburg to motivate bachelor students doing their first own small publications. The results of these projects are presented in this paper and ideas how students can publish are given.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS.

This paper proposed smart agricultural framework over RCAS in CATV network. The proposed framework adopts RCAS, which is a security system which is dedicated to cable television system, as a data protection system, and cable two-way broadband network as a communication channels among smart agricultural service participants.

Synthetic Aperture Radar (SAR) image classification is becoming increasingly important in military or scientific research. SAR image classification based on unsupervised learning usually requires optimization of some metrics. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for SAR image classification. The new algorithm composes of three main processes: firstly, selecting training samples for every region in the SAR image. Secondly, training these samples using PSO, and obtain clustering center of every region. Finally, output the classification result of SAR image according to clustering center obtained. To show the effectiveness of this approach, experiment with simulated SAR image was considered. The classification results are evaluated by comparing with two well-known algorithms, K-means and fuzzy K-menas. According to the overall accuracy and Kappa coefficient, PSO has high classification precision and can be used in SAR images classification.

Based on the Location Quotient (LQ) method, this paper selected datas of Statistical Yearbook from 2001 to 2008, and calculated the LQ of all sectors of industry in Xinjiang, finding three industries has the phenomenon of clustering, namely Extraction of Petroleum and Natural Gas industry, Oil Processing, Coking and Nuclear Fuel Processing industry and Processing of Ferrous Metals Ores industry. The cluster degree of all sectors has also been calculated, sorted and analyzed. In addition, changes of cluster degree from 2001 to 2008 in clustered industrial sectors was described and reasons for, and some suggestions on the industrial cluster development has been put forward of this region.

The massive big data transfer requirements in the cloud age are posing unprecedented challenges on todays' network infrastructure. Delivering data in a cost effective and scalable manner is of critical importance to support the continued growth of cloud services. In this paper, we revisit the problem of delivering large data on top of dynamic circuit switched networks, with a particular focus on how it can be used together with packet switching in an integrated data delivery model. We argue that the Software Defined Networking (SDN) concept and flow based switching are crucial to realize scalable and energy efficient integrated data delivery. We propose to use built-in or public storage to resolve contentions between large data flow transfers.

Design of analog and digital electronic circuits is an integral part of the electrical engineering curriculum. Software tools, which enable design, simulation, measuring and circuit analysis as well as PCB (Printed Circuit Board) design, are called EDA (Electronic Design Automation). The paper describes the introduction of EDA software in the first semester of the professional study of electrical engineering at the Zagreb University of Applied Sciences within course Personal Computers in Electrical Engineering. Examples of laboratory exercises related to electrical circuit diagrams, use of component libraries, work in standard laboratory environment (power supplies, function generators, voltmeters, ammeters, oscilloscopes, etc.), circuit analysis in a DC and AC mode as well as in time and frequency domain are described. Focus is on the optimal implementation of EDA tool NI Multisim and its integration with the existing learning management system Moodle as a platform for exercise learning and knowledge assessment.

Truncated multiplication can be used to significantly reduce the power dissipation for applications that do not require correctly-rounded results. This paper presents an efficient method for truncated multiplication called hybrid-correction truncation that utilizes the advantages of two previous methods to obtain lower average and maximum absolute error. Comparisons are presented contrasting power, area, and delay for all three methods compared to standard parallel multipliers. Estimates indicate that hybrid truncated multipliers dissipate slightly less power and consume slightly less area than previous methods for truncated multiplication. In addition, utilization of the hybrid truncation method can provide a method for altering the implementation within certain limits to meet a given precision.

In this paper, we focus on the identification of discrete-time piecewise affine (PWA) systems from noisy data. This problem consists of the estimation of both the local affine subsystems and the partition of the regression space. A two-stage robust identification approach is proposed to estimate the local affine subsystems in the presence of noise. This approach includes an optimization-based initial estimation process and a least-squares-based refinement procedure. In addition, to estimate the partition of the regression space for continuous dynamic PWA systems, an intersection approach is proposed as an alternative to the widely used pattern recognition approaches. Simulation studies demonstrate the effectiveness of the two-stage identification approach and the intersection approach in noisy case.

In this paper, an experimental validation of vertical forces estimation is presented. An original method using high order sliding mode observer is developed. The test bench is a static vehicle excited vertically by hydraulic jack. It's equipped by different sensors in order to measure its dynamics. Real time tests are done and results are presented in this paper.

Repeated X-ray cone beam computed tomography (CBCT) scans are frequently entailed in some clinical cases, such as CT-guided lung lesions puncture examination, image-guided intervention and radiotherapy, which result in individual patient doses soaring due to the associative volume scanning. Sparse-views based image reconstruction, as an effective method for reducing the radiation dose in CBCT scans, has been extensively studied recently. Due to the huge anatomical information similarity of images from repeated scans, if given a full-views scan, the associative reconstructed images can be used as important priori information for image reconstruction from sparse-views data. With above observation, in this paper, we propose a normal-dose scan induced nonlocal prior (ndiNLM-Prior) for yielding accurate image from the sparse-views data with an iterative image reconstruction process. The present ndiNLM-prior can exploit the similar information from the images reconstructed from the full-views data without needing accurate image registration. Evaluations with the physical and digital phantom data clearly demonstrate that the presented method achieves higher image reconstruction accuracy in terms of streak artifacts suppression.

System losses, power generation and generation cost are some of the most important factors that significantly influence the efficiency of power systems. Integrating distributed generation systems into distribution networks is one of the effective solutions. Many studies focused on finding the optimal planning of distributed generation systems to get the maximum benefits from them. In this paper, a brief recent review on a variety of heuristic optimization techniques applied in literature to optimally allocate and size distributed generations will be presented.

Event horizon imaging requires a third-generation space Very Long Baseline Interferometer on a 500 GHz band. The interferometer challenges available frequency standards. Namely, the practical coherent integration time is about 45 times shorter than needed. A self-calibration could aid this though a 3rd satellite is needed hence, an increased mission cost. This paper is devoted to experimental verification of a novel concept for two-way frequency transfer.We built a dedicated breadboard, test set-up, and run tests with and without temperature differences between branches of the breadboard. A phase difference between derived oscillators is the white noise-like function of the time with 1-σ of 0.5° at 103.2 GHz over more than 3 hours. Varying the temperature difference between branches ±3 C during 4.5 hours, being the orbit period, confirmed that the phase difference is the function of this temperature difference too. The Allan Deviation demonstrated between two derived 103.2 GHz oscillators is of 1.1×10−14/τ over 10 ms < τ < 1000 s, where τ is the averaging time, and the optical communication link path length is 5 km (17 ms). Degradation of this Allan Deviation is of 2 times at the longest-possible delay of 0.2 s in the interferometer system; the 0.2 s is the biggest sum of geometric and communication delays. This worst-case satisfies the requirement with a margin of 11 times. An obtained coherence is in the range of 0.997−0.9998 which is beneficial for the interferometer, at 557 GHz. This is one of the key technology enablers for black hole imaging from space.

Elasticity makes cloud computing an attractive platform for executing complex large-scale expensive dataflows, as it enables different trade-offs between execution time and monetary cost, by varying the number of resources to be provisioned. With cloud providers offering heterogeneous types of resources with different performance and price characteristics, the problem of identifying the various trade-offs available is a great challenge, as the number of possible alternative configurations increases significantly compared to a homogeneous environment, which is itself already computationally difficult. This paper proposes a novel algorithm for dataflow scheduling on heterogeneous clouds that identifies solutions (schedules) close to the optimal pareto front, by exploring the search space in an efficient way. The results of an experimental comparison with the state of the art show that, in several cases, the proposed algorithm provides a richer, more diverse set of solutions, several of which are characterized by significantly better time-money trade-offs.

This paper presents a new voltage-mode multifunction filter with four inputs and three outputs employing three current controlled differential difference current conveyors (CCDDCCs) and two grounded capacitors. The proposed circuit can provide low-pass, band-pass, high-pass, band-stop and all-pass voltage responses by appropriately connecting the input and output terminals, at a high impedance input terminal which enable easy cascadability. The circuit parameters ω 0 and Q can be set orthogonally by adjusting the circuit components. PSPICE simulation results are also performed to confirm the theoretical analysis.

Wireless sensor networks possess significant limitations in storage, bandwidth, and power. Additionally, real-time sensor networks cannot tolerate high latency. While some good compression algorithms exist specific to sensor networks, in this paper we present an energy-efficient method with high compression ratio that reduces latency, storage and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression scheme exploits temporal locality and delta compression to provide better bandwidth utilization in the network, thus reducing latency for real time applications. Our performance evaluations show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches.

Given an image, scene understanding is the process of segmenting and identifying the objects present, and classifying the overall scene. Several frameworks already exist to perform these tasks coherently but training of their probabilistic models is time consuming thereby limiting their scalability. This paper presents a scalable framework adopting an object-based approach. The steps taken by the algorithm are saliency detection for unsupervised object discovery, graph-cut for object segmentation, bag-of-features for object classification and binary decision trees for scene classification. A region of interest (ROI) detector is proposed to automatically provide object location priors from saliency maps for graph-cut. We tested our system on a novel NUS/NTU dataset and compared the scene classification accuracy using different classifiers. Unlike other existing frameworks, the proposed algorithm is scalable and can easily accommodate more object and scene classes.

This paper evaluates a wristband-type tactile interface as a user interface for controlling an industrial robot remotely. When operating a remote monitoring system, information delivery from a local site to an operator at a remote site is important. Visual and auditory feedback is often used. However, perception of the information might be limited if different kinds of information in particular sense modality is delivered at the same time. To solve the problem, we proposed a tactile interface which uses tactile information and we evaluate it. First, we explain the remote operation system for industrial robot and interface, then after that, we present an experiment of vibrotactile information of industrial robot.

Based on the principle of piezoelectric driving and ultrasonic near-field levitation technology, this paper designs a piezoelectrically driven ultrasonic levitation bearing, which is mainly composed of a rectangular piezoelectric sheet and a metal cylinder. The piezoelectric sheet drives cylinder wall to squeeze air in bearing gap under the action of high-frequency alternating voltage to form a gas squeezed film. According to the theory of viscous fluid mechanics, squeeze film Reynolds equation is derived. From the equation, it can be seen that cylinder radial deformation and squeeze number are the key parameters that determine the bearing capacity of gas squeeze film. With finite element software, radial deformation displacement function of cylinder is obtained, and the relationship between squeeze number and bearing capacity is analyzed by solving Reynolds equation. Based on above simulation and theoretical analysis, bearing is studied experimentally. The experimental results prove that theoretical calculation method is accurate, which has guiding significance for theoretical research and structural design of ultrasonic levitation bearings.

Current component interfaces, such as those defined by CORBA, are devoid of additional constraints that make helps in the task of composing software, therefore the dependent relations and interaction protocols among components are hidden within the implementations. The "implicity" reduces the composability and makes it difficult to reuse, validate and manage components. This paper presents a message based architectural model and an XML-message based Architecture Description Language (XADL). XADL promotes the description of dependent relations from the implementation level to the architectural level and enhances the interface specification by adding the sequencing, behavior and quality constraints. Furthermore, we formulate the notion of architectural mismatch and propose a checking algorithm to prevent systems from potential mismatches. The use of XML facilitates not only the specification descriptions but also the system implementations, and increases the openness of the whole system as well.

The paper presents a novel short-range wireless sensor node architecture, based on Integral Pulse Frequency Modulator (IPFM) and Ultra-Wideband (UWB) pulse generator. Due to the lack of internal clock signal source and multi user implementation without application of a microprocessor, the architecture is simple and energy efficient. Multi-user coding is performed using delay elements, where each user has unique delay time value. The output of the IPFM is fed to the delay element. Delayed and original signal from the IPFM are feeding UWB pulse generator. The paper presents the transmitter architecture and measurements of the transmitter signals in time and frequency domain.

The construction and the deployment of the service bearer network contain multiple network nodes. So it needs to do a unified operation. In the deployment processing, the whole service instance will fail if one operation fails. So it is a distributed transaction with ACID properties. This paper has done some research on the distributed transaction in a service bearer network. In addition to the basic concepts of distributed transaction. It has done a deep research on the following three aspects: the parallel mechanism of the subtransactions, the processing of the subtransaction failure and priority deployment strategies for the subtransactions. The key point is the designation of a more effective resolution algorithm for the preparation failure of the subtransactions, and then combined with relative examples to validate the efficiency of the algorithm.

According to the environment adaptability require-ment of an autonomous agent system, this paper studies the mapping relationships between the dynamic environment aspects and task scheduling, and then, gives the definitions and computation models of the task, quality of scheduling (QoS) and fitness. Considering the dynamic and inaccurate parameters of environment, a new Fuzzy-logic based Environment Adaptive Scheduler (FEAS) is proposed. In FEAS, the Multi-State Fuzzy Controller (MSFC) can make decision with the environment aspects to adjust the scheduling quality of the multi task groups so as to ensure that the execution process can suit to dynamic environment as possible. Finally, an experiment to make an agent system with FEAS avoids obstacle autonomously is carried out. And the results prove that such task scheduling method we studied can improve its adaptability of an agent system.

Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.

This paper presents an iterative, yet effective minimum mean square error (MMSE) space-time multiuser detector (MUD) with the assistance of soft interference to enhance the performance in the multipath code division multiple access (CDMA) systems. The proposed space-time MUD is of rank one and comprises of a spatial filter and a temporal filter. To offset the performance loss due to such a constraint, the new MUD alternatively updates the spatial filter or the temporal filter with the soft interference cancellation in between so as to refine the received signal. Computer simulations show that the new MUD can drastically enhance the performance compared to previous works with modest computations, especially in highly loaded scenarios.

This paper reviews the educational program development of alternative energy technology at Wayne State University and specifically describes the development and implementation of a set of 2+2+2 programs in electric drive vehicle engineering and technology, including a Master's Degree in Electric-drive Vehicle Engineering (EVE), a Bachelor's Degree in Electrical Transportation Technology (ETT), and Associate's Degrees in Automotive Technology and Electronic Engineering Technology with emphasis on electric-drive vehicles. The programs also include certificates with Electric Vehicle Technology courses, an undergraduate concentration and a graduate certificate program in EVE, and to simultaneously provide for general public and consumer education. The programs aim to prepare our current and future workforce with the education and skills necessary for the advancement and maintenance of electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), and fuel cell vehicles (FCV).