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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Robustness, security, anonymity, and scalability are valued features for mobile ad hoc network routing protocols. Unfortunately, the existing ad hoc routing protocols have limitations in each area. In this paper, we first analyze several existing mobile ad hoc routing protocols for robustness, security, anonymity, and scalability. Based on the analysis, we propose a new robust anonymous ad hoc on-demand routing protocol based on a proposed anonymous neighborhood trust model. We demonstrate that the protocol has strong security and anonymity protection, better scalability and is more robust when compared with other protocols., MILCOM 2009 - 2009 IEEE Military Communications Conference, October 18-21 2009, Boston, MA, USA

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.

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 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).

This paper presents the design and the analysis of a "fish-like underwater robot". In order to develop swimming robot like a real fish, extensive hydrodynamic analysis were made followed by the study of biology of the fishes especially its maneuverability and propel styles. Swimming mode is achieved by mimicking fish-swimming of carangiform. This is the swimming mode of the fast motion using its tail and peduncle for propulsion. In order to generate configurations of vortices that gives efficient propulsion, yawing and surging with a caudal fin is applied, and in order to submerge and maintain the body balance, pitching and heaving motion with a pair of pectoral fin is used. We have derived the equation of motion of PoTuna by two methods. In first method, we use the equation of motion of underwater vehicle with the potential flow theory for the power of propulsion. In second method, we apply the method of the equation of motion of UVM (underwater vehicle-manipulator). Then, we compared these results.

The aim of this paper is to introduce a novel phase-based feature representation for robust speech recognition. This method consists of four main parts: autoregressive (AR) model extraction, group delay function (GDF) computation, compression, and scale information augmentation. Coupling GDF with an AR model results in a high-resolution estimate of the power spectrum with low frequency leakage. The compression step includes two stages similar to MFCC without taking a logarithm of the output energies. The fourth part augments the phase-based feature vector with scale information which is based on the Hilbert transform relations and complements the phase spectrum information. In the presence of additive and convolutional noises, the proposed method has led to 15% and 12% reductions in the averaged error rates, respectively (SNR ranging from 0 to 20 dB), compared to the standard MFCCs.

There is a universal problem of high professional demand in data cleaning of professional field demanding. According to this problem, this paper improves data cleaning, and it puts forward a method association mining data cleaning on this basis. Finally, this paper verifies the effectiveness of the method by an example.

Reliability and fault-tolerance are essential requirements of critical, autonomous computing systems. In this paper, we propose a methodology to quantify, and maximize, the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on an homogeneous multiprocessor system with voltage and frequency scaling capabilities. As the likelihood of transient errors due to radiation is environment- and component-specific, we use machine learning to estimate the actual fault-rate of the system. Furthermore, we leverage probability theory to define a trade-off between power consumption and fault-tolerance. If a processing element fails, our methodology is able to re-map the application, establishing whether the real-time requirements will still be met, and how reliable the new, impaired system will be. Results show that the proposed methodology is able to adjust mapping and operating frequencies in order to maintain a fixed level of reliability for different fault-rates.

Due to the existing cryptographic schemes based on the satisfiability problem (SAT) lack the specific security model and the security analysis of the provable security theory, the security is questioned. For this problem, combining with the advantages of SAT and Elgamal scheme, we construct a secure, verifiable encryption scheme in this paper. Firstly, combining and improving the SRR(N, k, s) model based on the difficulty problem of the random regular formula near the phase transition point, we construct an encryption algorithm. Secondly, on the basis of the existing Damgârd Elgamal scheme, a more secure scheme based on SAT and variants of discrete logarithm problem is proposed. Lastly, the analysis results show that compared with the existing SAT-based hybrid cryptographic schemes, the private key of the hybrid encryption scheme proposed in this paper is shorter and the ciphertext satisfies the verifiability, and the scheme can secure against adaptive chosen ciphertext attacks (CCA2).

Electric potential difference method is widely used for diagnosing and monitoring flow conduits. We study the prospect of the method to evaluate pipe wall thinning. Based on our previous work on non-intrusive resistance thermometer for fast breeder reactor, this paper describes theoretical analysis and the 3-D exact solution of the electrical potential field in pipe wall. Complex potentials are applied for 2-D approximation of the potential on the wall surface to evaluate the pipe wall thinning.

This paper presents comparative study of palmprint matching algorithms for low resolution and noisy images. The principal lines and wrinkles are the only features easily extractable under low resolution. Therefore widely used Radon based approach has been studied and presented in this paper. Based on the computational complexity and fast matching, four different approaches are studied.

Malignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy, image processing methods, as well as the ever-increasing computing power of computers caused that researchers are able to consider significantly more features of the analyzed lesion than has been done so far using methods recognized in a medical community such as ABCD or Menzies methods. From the other hand more features not always imply an improvement in terms of efficiency of the diagnosis and its transparency. Hence, in this paper we survey the kind of features taken into account by the researchers and then, selected the most efficient set of them. Proposed method jointly selects the optimal set of features representing the analyzed lesion together with the accompanying form of the neural classifier (number of neurons, activation functions). The evolutionary algorithms are used in order to carry out the optimization. Obtained results are even better than the ones obtained by the most efficient these days deep classifiers.

In this paper we introduce the concept of Adaptive Orthogonal Frequency Division Multiplexing (AOFDM) for two-way relay systems. In order to implement AOFDM at the nodes, the Channel State Information (CSI) is required. Usually CSI is estimated and hence the effect of estimation error should be considered while implementing AOFDM. In the case of two-way relay, channel estimation at the nodes usually result in the overall channel between the source and relay. But for implementing AOFDM, we require the individual channel between node and relay. We propose a simple method to obtain the individual channel assuming that the estimate of combined channel is available. The loading algorithm used is the Levin-Campello loading algorithm. But the general frame work provided by us can be used to implement other loading algorithms for two-way relay systems.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

In recent years, chaos-based image cipher has been widely studied and a growing number of schemes based on permutation-substitution architecture have been proposed. To better meet the challenge of real-time secure image communication applications, this paper suggests a new image encryption scheme using a parallel substitution. In the permutation stage, the Arnold cat map is employed to shuffle the pixel positions so as to erase the strong relationship between adjacent pixels. In the substitution stage, the scrambled image is firstly decomposed into eight bit-planes which are then parallelly mixed with keystreams generated by the chaotic logistic map. Theoretically, the parallel substitution strategy runs eight times faster than the serial strategy on an 8-thread processor as the volume of data processed by each substitution unit is 1/8 of that of the input image. Experimental results show that the proposed parallel scheme runs more than five times faster than the serial scheme. Extensive security analysis is carried out with detailed analysis, demonstrating the satisfactory security of the proposed scheme.

The traditional teaching model of assets evaluation course generally emphasizes the theoretical knowledge impartment, lacking the cultivation of students' ability to solve practical problems. Therefore, it is difficult to satisfy the society needs of people with professional assets evaluation talent. Case teaching is a kind of heuristic teaching method, which places emphasis on the development of the students' spirit of innovation and practical ability. On the basis of characteristics of the assets evaluation course such as theoretical, technical, practical and operational, the implementation of case teaching of assets evaluation course plays a great realistic role in the development of the students' spirit of innovation and practical ability. Taking the characteristics of assets evaluation course for entry points, this paper discusses the meaning and the new teaching model of the case teaching of assets evaluation, providing a probability of wider application of case teaching of assets evaluation.

The Probabilistic Index Map (PIM) model was originally proposed for video processing to extract background of video frames. In this paper, we introduce the PIM model for texture segmentation. We first extract texture features based on Laws and Gabor filters respectively. Then we present a Fuzzy K-Means method to generate the index map and palette, and use the PIM model to improve the segmentation accuracy. Based on the comparison of experimental results produced using different features and different resolutions, we show the proposed method is effective for texture segmentation.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus