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Multiple reflections contain abundant structural information about the subsurface because of their smaller reflection angles and wider coverage. Multiple imaging has increasingly attracted attention. However, the imaging of multiples first needs to separate the multiples, which is a time-consuming and high-cost job. Therefore, the simultaneous imaging of primaries and multiples, which can make good use of multiple reflections with no need for multiple separation, has increasingly attracted attention. However, there are still some challenges to the conventional method, including wavelet selection and image artifacts suppression. In this paper, an improved method of the simultaneous imaging of primaries and multiples is proposed by introducing two creative strategies. First, considering that the wavelet, which is usually estimated by signal analysis under certain assumptions, is crucial to the simultaneous imaging, we propose to estimate the wavelet by iterative SRME (surface-related multiple elimination) using the original data containing multiples based on wave equation theory instead of signal analysis. Second, the stereographic imaging condition is introduced to suppress the crosstalk artifacts in the image. According to the numerical examples and field data test, the feasibility and effectiveness of our approach is verified.

Social Sciences and Demographics are in search of multiples ways to analyze person actions besides global factors like the environment. This paper aims to propose a protocol to construct a dataset from videos, then applying it to build a dataset of Peruvian News. Finally, we evaluate it using different Machine Learning (ML) methods, achieving an accuracy ranging 85% and 89%.

Contingency Screening and Coherent Identification are two fundamental parts of power system planning and operation. A common characteristic among these two methods is the need to analyze multiples contingencies. However, most of the current work existing in the literature is based on the paradigm of analyzing one contingency at a time, using 2D arrays (matrices) for the event representation. The drawback with this type of representations is the impossibility to consider multiple contingencies simultaneously. In this paper a reformulation of the problem using 3D arrays (tensors) is presented. Then, the extraction of the information is carried out using PARAFAC2. With this information, a severity index for contingency screening is proposed and identification of the coherent areas is accomplished. The approach is validated in the IEEE NETS-NYPS test system. The results confirm that the proposed approach allows to extract more information than in the traditional form.

The known and newly proposed methods of piecewise-linear approximation of both closed and non-closed contours are investigated. There are considered the methods of contour approximation, which provide a given approximation accuracy in terms of the distance from each pixel of the contour to the corresponding approximating straight line. Two methods for detecting straight lines in a contour image are proposed, having a linear computational complexity that is a multiple of the number of pixels in the contours in a binary image.

The basic principle of the synchronized SVPWM with non-3 integer multiples (SSVM-N3M) is introduced firstly in this paper. Then analyzes the characteristics of the space vector sequence of SSVM-N3M at each sampling point, and proposes a carrier-based SSVM-N3M (CBSSVM-N3M) by researching the equivalent relationship between SSVM-N3M and carrier-based PWM (CBPWM). Compared with the SSVM-N3M, the CBSSVM-N3M proposed in this paper does not need to calculate the dwell time of the space vector, which is more convenient in engineering practice. Based on this, the modulation strategy of the three-level (3-L) converter in the full frequency range is designed on the premise that the switching frequency is not more than 450Hz. The simulation results verify the correctness of the theoretical analysis.

The free-surface related multiples of submarine seismic data are very serious. At present, the method to deal with such multiples is to combine the pressure component and vertical velocity component first and then use the conventional multiple suppression technology applied to tow-streamer data. In this paper, a multiple separation method for submarine seismic data on both sides is developed, that is, multiples on detection side are separated by P Z summation and then multiples waves on source side are separated by deconvolution of up-going wave and down-going wave. This method is fully data-driven and has the advantages of fast operation and high computational efficiency. Theoretical models verify the method and satisfactory separation effect is obtained.

We discuss a novel approach for constructing deterministic reactive systems that evolves around a temporal model which incorporates a multiplicity of timelines. This model is central to LINGUA FRANCA (LF), a polyglot coordination language and compiler toolchain we are developing for the definition and composition of concurrent components called Reactors, which are objects that react to and emit discrete events. What sets LF apart from other languages that treat time as a first-class citizen is that it confronts the issue that in any reactive system there are at least two distinct timelines involved; a logical one and a physical one-and possibly multiple of each kind. LF provides a mechanism for relating events across timelines, and guarantees deterministic program behavior under quantifiable assumptions.

Complex Adaptive Systems are known to be amongst the most resilient systems due to their ability to self-organize when faced with challenge. Such self-organization is marked by innovation and this paper simulates a mathematical model of innovation positioned to be at the heart of any complex adaptive system, regardless of scale. Innovation in general involves moving from a basis of negative to positive sources of real-time action and reaction. Sources of innovation are modeled as four distinct sets that research indicates have categorized sustainable organization at the quantum-particle level to the level of civilizations. Further, these four sources of innovation are modeled to themselves exist at four levels of possibility based on the breaking of prevailing patterns. The model of innovation simulated in this paper hence is based on a four-by-four framework of innovation and can bring about a multiple of innovation of up to 18,000x, over 10 years as compared with default system innovation of up to 15x over 10 years when prevailing patterns are allowed to persist.

We explore the adaptation of 2D small-multiples visualisation on flat screens to 3D immersive spaces. We use a "shelves" metaphor for layout of small multiples and consider a design space across a number of layout and interaction dimensions. We demonstrate the applicability of a prototype system informed by this design space to data sets from different domains. We perform two user studies comparing the effect of the shelf curvature dimension from our design space on users’ ability to perform comparison and trend analysis tasks. Our results suggest that, with fewer multiples, a flat layout is more performant despite the need for participants to walk further. With an increase in the number of multiples, this performance difference disappears due to the time participants had to spend walking. In the latter case, users prefer a semi-circular layout over either a fully surrounding or a flat arrangement.

Nowadays, the game industry related to the interaction of multiples devices is still incipient due the current limitations between specific attributes like connection, compatibility of different platforms and documentation for development. In this paper we proposed a multiplayer game called INKATAN that integrates ensemble interactions and cross device technologies between Smart TVs and Android devices. We use a library based on Web sockets that allow, based on server-client principle, the connection of multiple devices that share similar environment independently of the platform that it belongs. INKATAN is a cooperative strategy game that allow up to 4 players to play at the same time using Android devices and a Smart TV as an interaction chart. The tests performed to 30 users divided in groups of 2 and 4 players show that the experience, quality and satisfaction present between accord and totally accord of all participants.

For the reconfigurable manufacturing system (RMS) with multi-part flow-line configuration producing multiples parts within the same family, the production cost and the delivery time are closely related to its configuration and corresponding scheduling for certain demand period. Up to now, the study on robust optimization of configuration design and scheduling for RMS considering the uncertainty of operation processing time is absent. First, a scenario based robust optimization model for integrated configuration design and scheduling is established, which is a mixed integer nonlinear programming formulation. Then, a mixed integer linear programming (MILP) model is derived to minimize the sum of total cost, the cost variation for prescribed scenarios, and penalty of earliness and tardiness. Based on the MILP model, LINGO is used to obtain the optimal solution. The validation and advantages of the robust MILP model are illustrated against two cases adapted from the literature.

Online news comment sections face a surge in uncivil, abusive and even straightforwardly hateful and threatening posts. In Germany especially the refugee crisis beginning in 2015 has sparked a lot of controversial and even unacceptable user comments. Overwhelmed by the amount of content and facing the risk of fines and a churn of readers as well as advertisers, many platforms shut down their comment sections as a last resort. To reduce their moderation effort, academics started applying machine learning to classify comments automatically. However, these efforts so far have been mostly focused on English texts. To provide similar systems for German, this paper implements and evaluates six different machine learning classifiers and five different strategies to convert textual comments into machine-compatible vectors. Contrary to common belief in the domain, comments often evade binary classification: Often comments are not only hateful, or insulting or threatening but fall within multiple of these categories. Hence, we will go beyond traditional multi-class classification models and prototypically evaluate the use of multi-label techniques. The first evaluations indicate that systems for abusive language detection are transferable to the German language and that supporting multi-labels might not only help to improve the detection of rare abusiveness types but also lead to a more realistic representation of actual online commentary.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The rapid availability of new services makes that network operators cannot exhaustively test their impact on the network or anticipate any capacity exhaustion. This situation will be worse with the imminent introduction of the 5G technology and the kind of totally new services that it will support. In this paper, we present CURSA-SQ, a methodology to analyze the network behavior when the specific traffic that would be generated by groups of service consumers is injected. CURSA-SQ includes input traffic flow modelling with second and sub-second granularity based on specific service and consumer behaviors. The methodology allows to accurately study traffic flows at the input and outputs of complex scenarios with multiples queues systems, as well as other metrics such as delays. This work was partially supported by the EC through the METRO-HAUL project (G.A. nº 761727), from the AEI/FEDER TWINS project (TEC2017-90097-R), and from the Catalan ICREA Institution.

In this paper, the effectiveness of a matched filter to cope with continuous wave – i.e. pure sinusoidal – EMI disturbances is studied. The bit-error-rate is taken as the metric to quantify the effect of the matched filter and compare this to the case when no matched filter is applied. It is seen that a matched filter becomes very effective when the frequency of the continuous wave equals an integer multiple of the sampling frequency or when the frequency of the continuous wave is significantly larger than the sampling frequency.

This paper deals with the influence of pre-whitening for the task of fundamental frequency estimation in noisy conditions. Parametric fundamental frequency estimators commonly assume that the noise is white and Gaussian and, therefore, they are only statistically efficient under those conditions. The noise is coloured in many practical applications and this will often result in problems of misidentifying an integer divisor or multiple of the true fundamental frequency (i.e., octave errors). The purpose of this paper is to see if pre-whitening can reduce this problem, based on noise statistics obtained from existing noise PSD estimation algorithms. For this purpose, different noise types and prediction orders of LPC pre-whitening are considered. The results show that pre-whitening improves significantly the estimation accuracy of an NLS pitch estimator when the noise is fairly stationary. For nonstationary noise, the improvements are modest at best, but we hypothesize that this is due to the noise PSD estimation performance rather than the LPC pre-whitening principle.

International audience; The aim of the study is to compare and assess the efficiency of conventional hyperspectral techniques (dimension reduction, learning and classification methods) to classify the urban tree vegetation diversity. A specific focus is made using very high spatial resolution hyperspectral images acquired from airborne sensor and simulated at various spatial/spectral resolutions. Results show that 78.8% of overall accuracy can be reached using MNF and SVM methods to classify urban tree diversity. Moreover, results are more sensitive to learning methods rather than dimension reduction or classification ones. Finally, VHRS hyperspectral imagery are promising for such a key urban environmental issue.

With the amount of data being created now, it has become very important to analyze this data and make useful deductions from it. The enormous mass of data causes an information overload for the user and searching for relevant and useful data has become very tough. In this paper, we propose GUP, a system that unifies multiples sources of data which explore the different kinds relationships between enterprise, universities, and academia around the world into one centralized framework, which can then be analyzed using cloud cognitive services, that offer smarter, self-service capabilities so systems can quickly and identify insight, to find meaningful relations between universities and corporate employees which are then visualized for better understanding. The challenges to developing such a system is the wide variety of data sources to consider and finding optimal methods of aggregating them. The key contributions of this paper include:i.Data Collection: Aggregate all known data sourcesii.Data Analysis: Analyze this Data to make meaningful conclusionsiii.Data Visualization: Present the data in an easy to understand manner in the form of visualizations.

The paper introduces a novel model for optimizing the placement of a multiple of Dynamic Voltage Restorers-DVR for global voltage sag mitigation in distribution systems. The location of DVRs is optimally selected on the basic of minimizing the system average rms variation frequency index - SARFI x of the system of interest. The problem of optimization is introduced where the new method for modelling a number of DVRs mitigating globally voltage sag in the event of short-circuit in distribution system using the Thevenin's superposition principle is proposed. The DVR's performance of systematic voltage sag mitigation is considered in the case of a given maximum current generated by DVR. The paper uses the IEEE 33-buses distribution feeder as the test system for voltage sag simulation and influential parameters to the outcomes of the problem of optimization are considered and discussed.

The islanding detection methods that detect an island through injecting high/low frequency currents or negative sequence currents are called irregular current injection methods in this paper. A synchronization issue of such methods is raised in multi-DG (Distributed Generation) operation. This issue requires that the same frequency irregular currents injected by DG units should be in phase to avoid cancelling each other out. If this issue is not handled properly, the effect of such methods is uncontrollable. In view of this, an approach to this issue is proposed in this paper. According to this approach, the terminal voltage of a DG unit is taken as a reference to conduct the irregular current injection, and only parts of the irregular currents whose frequencies are an integer multiple of the frequency of the reference voltage are employed. Finally, experiments and simulations verify the effect of the proposed approach.

The growth of Intelligent Traffic System (ITS) have recently been quite fast and impressive. Analysis and prediction of network traffic has become a priority in day to day planning in social, economic and more widespread set of areas. With a vision to further contribute to this vast field of research, we propose an approach to forecast level of traffic congestion on the basis of a time series analysis of collected data using machine learning. Moreover, the proposed approach allows us to find a correlation between varying parameter of weather and level of traffic congestion. Traffic data collected from Uber Movement for the city of Mumbai, India was fed to multiple of pre assessed machine learning algorithm. Comparative analysis of the results of the different machine learning algorithms used have shown us that logistic regression works best with an accuracy of 85% on the collected Uber data. Thus our model can accurately predict the time to travel between different nodes (locations) in Mumbai city based on the data collected from Uber Movement.

This work is about environmental sound classification by deep convolutional neural networks and data augmentation. Data augmentation is applied to increase the labeled training dataset. Data augmentation process improves the performance of audio classification. In this paper, first we present a strategy for generating a deep convolutional neural network (CNN) framework for environmental sound analysis with Urban-sound8K audio dataset. Secondly we analyze the performance of data augmentation methods on Urbansound8K audio dataset and compare the performance of CNN with different data augmentation methodologies. Data augmentation is basically a deformation technique. By this approach we can increase the number of dataset elements into its multiples. Here, compare the performance of different augmentation method to identify which one is the best augmentation technique for environmental sound analysis. Different types of data augmentations were applied to the dataset in the previous works. We introduce a new data augmentation method using LPCC feature.

A new sparse array structure based on the recently proposed thinned coprime arrays is proposed to maximize the number of unique lags. The design process involves two stages: the first stage displaces one subarray from its original position for an increase in the number of lags; as the displacement results in the minimum interelement spacing equal to integer multiples of half-wavelength, an additional sensor at a distance of half-wavelength is then added in the displaced subarray to avoid spatial aliasing. The strategic location of the additional sensor results in a significant increase in the overall unique lags which can be utilized for direction-of-arrival estimation (DOA) using compressive sensing based methods. Furthermore, the new structure has excellent performance in the presence of mutual coupling as shown by simulation results.

HERE we offer a detailed "works-in-progress" plan for expanding our Siemens PETLINK TM Guideline. Our goal is to continue to support the ever-growing needs of clinical PET data acquisitions for many years to come. For over 20 years, the PETLINK Guideline has served to optimize the acquisition of list-mode data in PET – i.e. for PET products originally from CTI, Inc. and now from Siemens. Many generations of PET and PET/CT products (and even one PET/MR product) shipped in quantity throughout this two-decade period have adhered to this guideline. By helping to ensure backward compatibility of both hardware and software developed period have adhered to this guideline. By helping to ensure backward compatibility of both hardware and software developed for PET, the primary contributions this guideline has made to the PET community are to help minimize rework, preserve technical investments in functionality, and to make the analysis of our PET list-mode data more publically accessible across a wide group of PET contributors. At the core of this guideline has been a special 64-bit packet format which organizes PET detector-pair coincidence-event list-mode data as well as non-event or "tag" data. See Table 1 . This detector-pair event-packet format serves to digitally identify which specific two scintillating crystals within the PET cylinder-of-crystals array are found to be in time coincidence as a pair of arriving gamma photons are detected in real time. Since the inception of PET, successive generations of PET detector arrays have grown in size – i.e. increasing the quantity of crystal scintillators used per system. Originally (around 1997) this 64-bit packet format was developed out of a need to grow beyond a 32-bit packet size. Prior to 1997, 32 bits were largely sufficient to address the entire PET detector array for each in-coincidence pair of scintillators. After 1997, 32 bits became increasingly insufficient, especially with the looming need to support time-of-flight (TOF or TF) information within this same (packet) grouping of bits. Also, this 1997 leap from a 32-bit packet to a 64-bit packet was made partly as a matter of convenience. Modern computer systems typically are more efficient when handling multiples of 32-bit quantities – e.g. a 48-bit packet quantity supported within a simple packet framework quickly becomes problematic when efficient use of memory space, file space, and operating system calls are considered. [1]

We present recent developments on intermodal nonlinear interactions present in fibers supporting multiple spatial modes that are linearly coupled or uncoupled.

This paper presents novel two-stage comb decimation filter for the multiple of three odd decimation factors. The novelty of this filter is the alias improvement in all folding bands at low cost. More specifically, like in comb-zero rotation method, the alias rejection improvement is obtained by placing additional zeros into comb folding bands. However, this is achieved by using simple multiplierless filters, placed in both stages of the comb structure. The second stage is decimated by three, while the first stage is decimated by one third of the overall decimation factor. The choice of multiplierless filters is discussed and the corresponding structures are presented. Finally, the comparisons with some methods, proposed in literature, are provided.

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare email communications between staff-staff or staff-clients or staff-customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons (anomalies). From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one's confidence in one's ability to deal with social and organisational information). We considered a case study, an Enron Organisation Email Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to email relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with our industrial partner, Red Sift UK, to find anomalies, monitor and compare how email relationships change over time and how it defines the meaning of socio-organisational communication efficacy.

The perfect Gaussian integer sequences, which satisfy the ideal periodic autocorrelation functions, are of practical interest in both orthogonal frequency division multiplexing (OFDM) systems and code division multiple access (CDMA) systems. In this work, we propose an infinity family of the perfect Gaussian integer sequences constructed from a multiple of the original Payne's o-polynomial. Experimental results show that the obtained sequences are balanced and have odd period. The energy efficiency of the resulting sequences is slightly increased when compared with those of the already known sequences.

In this paper, we study a new problem called probabilistic A-weighted coverage placement, which is a generalization of the s-coverage placement. s-coverage placement assumes that the monitored area has uniform coverage requirement: events within the monitored area should be detected with probability Q X100% (i.e., detected by Q sensors), while probabilistic A-weighted coverage placement assumes that the monitored area has A-degrees of coverage requirement (i.e., A kinds of detection probability): events within distinct region of the monitored area are detected with distinct detection ability, i.e., one of the A kinds of detection probability. Besides, Q-coverage placement requires that the coverage requirement is integer multiple of 100% detection probability, while probabilistic A-weighted coverage placement allows coverage requirement to be non-integer multiple of 100% detection probability. We derive a lower bound on the number of sensors needed to satisfy the coverage requirement of a probabilistic A-weighted monitored area, and introduce a greedy algorithm to solve the problem.

An important aspect of designing information displays to support monitoring and decision-making in critical care is the representation of change of patient data over time. We systematically reviewed articles to identify novel alternatives to tabular and single variable plots of values over time to convey information about change in multiple related variables. Following screening of 5,119 articles, 28 met our inclusion criteria. They described 26 unique displays evaluated in 31 experiments. Methods for representing change varied widely. We classified these methods as enhanced graphical displays (enhanced plots of quantitative data over time), other displays (novel object and metaphoric displays), small multiples displays (multiple co-presented small graphic displays), and simple change indicator displays. Overall, findings support the value of an explicit display of trend information using many different approaches. Few studies directly compared different methods for displaying trend information in ways that would support broader conclusions about which approaches may be preferred for specific applications. The studies suggest that, for displaying patient data trends, it is feasible to develop electronic displays that will outperform both historical paper-based flowcharts and current electronic health record (EHR)-based tabular approaches. There is evidence to suggest that even minor improvements to current approaches such as the automatic presentation of simple line plots of trends on EHRs or the addition of simple graphical indicators of trend direction on patient monitors could lead to clinically meaningful improvements in diagnostic accuracy and efficiency.

This study discusses a design method for sampled-data multi-rate control systems, in which the sampling interval of the plant output is an integer multiple of the hold interval of the control input. In this study, a desired response in discrete time is assumed to be obtained using a multi-rate control law, and then the multi-rate control law is extended such that an existing discrete-time disturbance and reference responses are simultaneously maintained. In the proposed multi-rate control system, the intersample response can be improved independently of the discrete-time response. Finally, the effectiveness of the proposed method is shown through numerical examples.

The technology for activity classification presents new opportunities for control and monitoring of serious games players. Other than for step detection, human activity classification is normally undertaken by calculating features from a fixed interval length of sensor data and comparing them to values expected from a range of activities. It was observed that many human activities, especially vigorous activities, are cyclic. This paper tested the hypothesis that, if features are calculated over an integer number of cycles of a cyclic activity, more accurate activity classifications will be achieved. An algorithm was developed that determines whether an activity is cyclic and if so, identifies the cycles and calculates the feature set over an integer multiple of cycles. If the activity is determined to be non-cyclic, the features are calculated over a fixed time window. The hypothesis that more accurate activity classifications will be achieved was confirmed with a pairwise t-test at the 99% significance level. Knowledge that a cyclic activity is taking place is already informative and can enable generation of features from individual cycles. One of these, cycle length, was added to the feature set which improved recognition rates for activities where the cycle length varied greatly from other activities. For example, brushing teeth improved from 39% with a fixed window to 54% for an adaptive window and 74% for an adaptive window with cycle length. An overall improvement in activity classification success rate was found for this adaptive windowing method compared to a fixed window approach with overall success rates of 54% for a fixed window, 62% for an adaptive window and 64% for an adaptive window with the additional feature.

Scaling database is one of the important aspects of testing the database engines. Traditionally, scaling performed in the most of the databases is with respect to the size of the database. That is, given a scaling factor, the row cardinality of each relation is scaled up by a factor. In this paper, we describe a new method of scaling database called cost based scaling. The cost is the time taken to execute a query workload on scaled database. The goal of cost based scaling is to scale the database so that time taken for executing queries on scaled database is some multiple of time for executing the queries on original database. This cost based scaling can be used as an alternative to the traditional size based scaling for testing the database engines.

The need of computational environment and processing power increases per day due to the large amount of data generating real-time applications like e-Healthcare systems, manufacturing systems, e-Government sites, online shopping portals, social networking sites, and weather and agricultural forecasting applications. For the purpose of handling large data, and to find insights from such data, a platform with methodologies is mandatory. In this paper, we proposed a parallel computing based iterative approach to give the analytics and improve the performance of the system. We tested the approach on the historical data of weather published by Open Government Data Platform of India. The proposed system based approach are used to handle a large amount of data and capable of processing it on the parallel computing platform. The approach is used to forecast the results by applying different parameters present in the database. The main aim of the work is to reduce the execution time required and forecast the results. In this study, the experiments are tested on parallel computing workers and gives multiple of 100 times better performance than the single worker system.

Co-location pattern discovery is a process to find a subset of Boolean spatial feature that is frequently located in the same geographic area. There are some approaches have used for this process. Mostly co-location mining discovery has been done for point type and the feature has the same domain. But in reality spatial data has three types, which are point, line, and polygon. In this paper, we tried to discover spatial co-location pattern that involves three types of data spatial from a different domain. We propose multiple neighborhood relationship function to find neighborhood relation from the multiple types and multiples domains of data spatial and apply co-location mining with join less approach to find co-location pattern. The evaluation of our proposed method that using real data shows that multiple neighborhood relationship function is needed to extract the correct and complete spatial relationship to the data that have expansion of the data types and heterogeneous data source.

In many applications the pulse duration of a periodic pulse signal is the parameter of interest. Thereby, the non-band-limited pulse signal is sampled during a finite observation period yielding to aliasing and windowing effects, respectively. In this work, the pulse duration estimation based on the mean value of the samples is considered, and an exact expression of the mean squared estimation error (averaged over all possible time shifts) is derived. The resulting mean squared error expression depends on the observation period, the pulse period and the pulse duration. Analyzing the effect of these parameters shows that the mean squared error can be reduced (i) if the observation period is a multiple of the pulse period, (ii) if the pulse period is not a multiple of the sampling period, and (iii) if the total number of samples is a prime number. All results were validated with simulation results.

Wave Field Synthesis aims at the accurate reproduction of a sound field inside an extended listening area which is surrounded by individually driven loudspeakers. Recently a Local Wave Field Synthesis technique has been published which utilizes focused sources as a distribution of virtual loudspeakers in order to increase the reproduction accuracy in a particular local region. Similar to conventional Wave Field Synthesis, this technique relies heavily on delaying and weighting the input signals of the virtual sound sources. As these delays are in general not an integer multiple of the input signals' sample rate, delay interpolation is necessary. This paper analyses in how far the accuracy of the delay interpolation influences the spectral properties of the synthesised sound field. The results show, that an upsampling of the virtual source's input signal is an computationally efficient tool which leads to a significant increase of accuracy.

This study discusses a design method for a sampled-data multi-rate control system in which the sampling interval of the plant output is an integer multiple of the hold interval of the control input. In this study, a pre-designed multi-rate control law is extended such that the sampled behavior is maintained. Using the extended control law, both the transient and steady-state intersample behavior can be redesigned independently of the sampled behavior. The effectiveness of the extended method is demonstrated through numerical examples.

This work presents theory and simulation of a method to solve the divergent problem of multiples of one-half wavelength. It is theoretically shown that, by using the special relationship between the electromagnetic parameters of the sample to be tested and of the known material, the divergent problem of multiples of one-half wavelength can be avoided. Simulation results show that the divergent problem of multiples of one-half wavelength is solved by changing the known material.

A peak-picking method has been proposed and integrated into baseline AMDFs for pitch detection. Many of the known peak-picking methods for AMDF/ACF based PDAs expect to search the sole peak located at pitch period. However, it is a fact that correct pitch information is also contained in the peaks located at positive integer multiple of pitch period, whose existence are strong evidence of the validity of detected pitch as well. The proposed peak-picking method uses clustering analysis searching these peaks as many as possible. A necessary condition and some constraints are used to select the optimal cluster produced by clustering analysis. The proposed method shows a very good adaptability and noise-robustness. 5 improved AMDFs are employed to evaluate performance of proposed peak-picking method. Gauss white noise is added into speech for anti-noise tests. Experiments indicate slight improvements in low-noise environments and clear improvements in high-noise environments compared with the reference PDAs.

Taking VAST 2015 Mini-challenge 2 (MC2) as background, this paper presents a visual analytics solution for exploring various communication patterns in the mobile application data generated by a great number of visitors in a theme park. The main body of our solution consists two parts: a three-step data preprocessing and two visual analysis tools. A variety of advanced analytic techniques are applied in our solution including community detection, aggregation analysis, collaborative analysis, multifaced analysis based on small multiples, and dynamic network analysis based on massive sequence view. Lots of interesting communication are discovered in our solution. These patterns can help park officials understand how people communicate in the park and improve their management.

Computing and presenting crowd simulation in real-time requires an intensive processing effort, since it is necessary processing the behavior and render of each entity. The advent of GPU computing has enabled the development of many strategies for accelerating these simulations. In this paper we propose an architecture for multiples GPUs for crowd simulation, that allows a massive number of entities to be processed and rendered in real time. Also, we implement a representative case-study based on the behavior of a crowd during the street carnival from Rio de Janeiro from which we run benchmarks and compare the benefits achieved using more the presented architecture.

The present paper deals with a bandlimited signal which is sampled with 1-bit precision at the receiver. The sampling rate is assumed to be an arbitrary integer multiple of the symbol rate. Assuming a scenario with additive Gaussian noise, pulse shaping filter and receive filter, the setup corresponds to a discrete channel with memory. The achievable rate is investigated by providing upper and lower bounds, where the latter correspond to different receiver principles. The numerical evaluation shows significant differences in the quality of the bounds. The results are valuable to evaluate system performance which confirm that oversampling with 1-bit precision is beneficial in terms of the achievable rate.

It has been previously suggested to use multiple reference frame (MRF) structures, based on proportional-integral controllers in synchronous reference frames (SRFs), to suppress current harmonics caused by nonlinearities in multiphase drives. However, the rotational transformations are computationally demanding. In Part 1, the three-phase strategy based on resonant controllers (RCs) implemented in an SRF has been extended to any phase number. Nevertheless, while the computational burden of MRF and multiple RC (MRC) schemes has been assessed for three-phase systems in previous papers, it is yet to be studied for other numbers of phases. Part 2 analyzes the computational load of the MRC and MRF strategies as a function of the phase number and of the highest harmonic order to be compensated. It is proved that the former provides a resource saving with respect to the latter that decreases with the phase number and increases with the highest harmonic order under consideration. It is also concluded that the MRC strategy is particularly preferable in machines with even phase number (e.g., a multiple of six).