Global Bandwidth Research Articles

On Bandwidth Choice for Spatial Data Density Estimation

Summary Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial data. The paper investigates the choice of adaptive and non-adaptive bandwidths for density estimation given data on a spatial lattice. An adaptive bandwidth depends on local data and hence adaptively conforms with local features of the spatial data. We propose a spatial cross-validation (SCV) choice of a global bandwidth. This is done first with a pilot density involved in the expression for the adaptive bandwidth. The optimality of the procedure is established, and it is shown that a non-adaptive bandwidth choice comes out as a special case. Although the cross-validation idea has been popular for choosing a non-adaptive bandwidth in data-driven smoothing of independent and time series data, its theory and application have not been much investigated for spatial data. For the adaptive case, there is little theory even for independent data. Conditions that ensure asymptotic optimality of the SCV-selected bandwidth are derived, actually, also extending time series and independent data optimality results. Further, for the adaptive bandwidth with an estimated pilot density, oracle properties of the resultant density estimator are obtained asymptotically as if the true pilot were known. Numerical simulations show that finite sample performance of the SCV adaptive bandwidth choice works quite well. It outperforms the existing R routines such as the ‘rule of thumb’ and the so-called ‘second-generation’ Sheather–Jones bandwidths for moderate and big data sets. An empirical application to a set of spatial soil data is further implemented with non-Gaussian features significantly identified.

Achieving robust global bandwidth along with bypassing geo-restriction for internet users

<p class="Abstract">Not all Internet Service Providers provide a sufficient amount of bandwidth to their users. Although the amount of local bandwidth is reasonable, global bandwidth is not satisfactory at all. Based on bandwidth allocation, location and price; service providers capped their users’ global bandwidth i.e., reducing global internet speed. As a consequence, we observe severe global bandwidth limitation among Internet users. In this article, we implement a flexible and pragmatic solution for Internet users to bypass global bandwidth restriction. To achieve robust global bandwidth, we utilize a combination of communication technologies and devices namely, Internet Exchange Point, Virtual Private Network, chain VPN technology etc. In this project, we show that internet speed of international route i.e., global bandwidth can enhance significantly if there are multiple ISPs use a common IXP and at least one of those ISPs provides pleasant global bandwidth. Usually, regional ISPs use a common IXP to route their local traffic using local bandwidth within the region without wasting global bandwidth. We show that using our proposed method global internet speed of a user can raise several times effectively utilizing assigned local bandwidth. In addition, we also implement a geo-restriction bypassing technique integrating an offshore ISP with local ISP using VPN. Thus, we enjoy tremendous Internet speed along with unrestricted access to the websites.</p>

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today’s mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network.

Global bandwidth scheduling algorithm for complex IP over OTN network

This paper presents a global paths planning in IP over OTN uniform control inter-datacenter network and designs algorithm based on fairness, in order to due with big traffic flow paths planning problem. By this algorithm, apps are allocated bandwidth in a fair way on the premise of trying to meet the bandwidth demand. Simulation results reveal that, compared with algorithm based on priority, global paths planning improves fairness by up to 0.2 on premise of not reducing satisfaction degree. Compared with algorithm in IP controlled network, number of router ports is saved by up to more than 65% in IP over OTN uniform controlled network.

Parametric analysis for optimized piezoelectric bistable vibration energy harvesters

This study focuses on vibration energy harvesting with piezoelectric bistable inertial generators in order to provide an alternative or a support to chemical batteries in the power supply of isolated wireless sensors (leading to a better autonomy of these devices). The objective of the present work is to provide guidelines to optimize this kind of generator through the investigation of the influence of different parameters (load resistance, mass, stiffness, buckling level) on its frequency response. These guidelines will be obtained exploiting a new analytical model for piezoelectric bistable harvesters which will be constructed based on a recent model introduced for electromagnetic bistable harvesters. This model includes the study of subharmonic behaviors which can be used to enhance the global bandwidth of the harvester as well as a stability robustness criterion which allows a better prediction of experimental observations. The new model will be validated by experimental data and finally exploited to provide guidelines for piezoelectric bistable harvester optimization.

High-speed GPU implementation of a secret sharing scheme based on cellular automata

Parallel implementation provides a solution for the problem of accelerating cellular automata (CA)-based secret sharing schemes and make them appropriate for bulk data sharing and real-time applications. By presenting new platforms, we need new implementation techniques to run algorithms as fast as possible on the platform. In this paper, we present a new implementation of a CA-based secret sharing scheme using the Graphic Processing Unit (GPU). We propose a new data arrangement that reduces the total number of accesses to the memories in GPU. Our algorithm further reduces the amount of data required by each thread and at the same time achieves a high cache hit rate. Also, it can achieve coalesced memory accesses to optimal use of the global memory bandwidth. The proposed method obtains speedup up to four times faster than the best previous GPU implemented CA-based multi-secret sharing schemes.

GraphH: A Processing-in-Memory Architecture for Large-Scale Graph Processing

Large-scale graph processing requires the high bandwidth of data access. However, as graph computing continues to scale, it becomes increasingly challenging to achieve a high bandwidth on generic computing architectures. The primary reasons include: the random access pattern causing local bandwidth degradation, the poor locality leading to unpredictable global data access, heavy conflicts on updating the same vertex, and unbalanced workloads across processing units. Processing-in-memory (PIM) has been explored as a promising solution to providing high bandwidth, yet open questions of graph processing on PIM devices remain in: 1) how to design hardware specializations and the interconnection scheme to fully utilize bandwidth of PIM devices and ensure locality and 2) how to allocate data and schedule processing flow to avoid conflicts and balance workloads. In this paper, we propose GraphH, a PIM architecture for graph processing on the hybrid memory cube array, to tackle all four problems mentioned above. From the architecture perspective, we integrate SRAM-based on-chip vertex buffers to eliminate local bandwidth degradation. We also introduce reconfigurable double-mesh connection to provide high global bandwidth. From the algorithm perspective, partitioning and scheduling methods like index mapping interval-block and round interval pair are introduced to GraphH, thus workloads are balanced and conflicts are avoided. Two optimization methods are further introduced to reduce synchronization overhead and reuse on-chip data. The experimental results on graphs with billions of edges demonstrate that GraphH outperforms DDR-based graph processing systems by up to two orders of magnitude and $5.12 {\times }$ speedup against the previous PIM design.

Myocardial perfusion imaging detects mechanical dyssynchrony in left ventricular infarcted and noninfarcted areas early after acute myocardial infarction in a porcine model.

Left ventricular mechanical dyssynchrony (LVMD) is closely associated with left ventricular dysfunction and poor prognosis in patients with acute myocardial infarction (AMI). However, whether mechanical dyssynchrony is present in the noninfarcted areas remains controversial. This research aimed to quantitatively evaluate the global and regional mechanical dyssynchrony early after AMI by phase analysis of single-photon emission computed tomography (SPECT) gated myocardial perfusion imaging (GMPI) and to further explore the related influencing factors. Of 11 Bama suckling pigs, eight animals were successfully subjected to left anterior descending artery occlusion by balloon to generate porcine AMI models and completed the study. SPECT GMPI was performed before AMI and at 1 day, 1 week, and 4 weeks after AMI. The global bandwidth (BW), SD, entropy, total perfusion deficit, summed rest score, regional BW, regional summed motion score, and regional summed thickening score were measured by SPECT GMPI. The global BW, SD, and entropy values significantly increased after AMI and showed no significant change among the three time points after AMI. The BW in the infarcted area (left anterior descending artery-dominated area) at 1 day, 1 week, and 4 weeks after AMI was significantly higher than that before AMI, as was the BW in the noninfarcted areas (left circumflex artery-dominated and right coronary artery-dominated areas), which revealed that there was less dyssynchrony in the noninfarcted areas than in the infarcted area at the three time points after AMI. The global BW was positively correlated with the scar burden measured by summed rest score (r=0.709-0.832, all P<0.05), whereas the regional BW in the noninfarcted areas after AMI showed moderate to good correlation with regional summed motion score (r=0.733-0.875, all P<0.05) and regional summed thickening score (r=0.713-0.889, all P<0.05). LVMD occurs early on the first day after AMI, with no significant worsening over the next 4 weeks. Mechanical dyssynchrony was present in both the infarcted and noninfarcted areas. The global LVMD is mainly influenced by the scar burden, and the regional mechanical dyssynchrony in the noninfarcted areas is closely associated with the abnormal regional wall thickening and motion, which are indicative of reduced myocardial contractility.

Ultra-Short-Reach Interconnects for Die-to-Die Links: Global Bandwidth Demands in Microcosm

To meet the ever-increasing demand of global data traffic, the Hybrid Memory Cube (HMC) specification and High-Bandwidth Memory standard call for an aggregate bandwidth of 8 Tb/s in next-generation memory-to-processor links [1], [2]. At the same time, the data rates processed by individual line cards in data center switches will soon go beyond 25 Tb/s [3]. However, as the data rate of serial links over copper interconnects increases, the links appear highly lossy. Further, the power consumption of a wireline transceiver, including all clocking and equalization circuits, increases. Publications suggest a tenfold increase in power consumption with a 30-dB increase in channel loss [4]. Hence, energyefficient communication equipment should be engineered to keep the channel loss as low as possible.

Electrically tuned microwave CP cross slot patch antenna using liquid crystal technology

ABSTRACTThe rapid progress in material science and electronic technology is great possibility in designing reconfigurable wireless devices which are frequency tunable, flexible and consume low energy. In this paper, the anisotropic propriety of a new liquid crystal mixture ‘GT3-23001ʹ in their nematic phase are exploited for designing tunable circular polarized cross slot patch antenna for WLAN applications. Reconfiguration of frequency response is controlled through an electrical external command. The proposed reconfigurable antenna based on liquid crystal satisfies a continuous frequency tuning of 15.83% with a global bandwidth of around 467.92 MHz (19.5%). to validate this novel mixture of liquid crystal, the simulated results are compared with the measured data and a good agreement is noticed.

Nonparametric density estimation based on beta prime kernel

In this work, we propose beta prime kernel estimator for estimation of a probability density functions defined with nonnegative support. For the proposed estimator, beta prime probability density function used as a kernel. It is free of boundary bias and nonnegative with a natural varying shape. We obtained the optimal rate of convergence for the mean squared error (MSE) and the mean integrated squared error (MISE). Also, we use adaptive Bayesian bandwidth selection method with Lindley approximation for heavy tailed distributions and compare its performance with the global least squares cross-validation bandwidth selection method. Simulation studies are performed to evaluate the average integrated squared error (ISE) of the proposed kernel estimator against some asymmetric competitors using Monte Carlo simulations. Moreover, real data sets are presented to illustrate the findings.

Propagation Path Loss Modeling and Outdoor Coverage Measurements Review in Millimeter Wave Bands for 5G Cellular Communications

The global bandwidth inadequacy facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks, and mmWave band is one of the promising candidates due to wide spectrum. This paper presents propagation path loss and outdoor coverage and link budget measurements for frequencies above 6 GHz (mm-wave bands) using directional horn antennas at the transmitter and omnidirectional antennas at the receiver. This work presents measurements showing the propagation time delay spread and path loss as a function of separation distance for different frequencies and antenna pointing angles for many types of real-world environments. The data presented here show that at 28 GHz, 38 GHz and 60 GHz, unobstructed Line of Site (LOS) channels obey free space propagation path loss while non-LOS (NLOS) channels have large multipath delay spreads and can utilize many different pointing angles to provide propagation links. At 60 GHz, there is more path loss and smaller delay spreads. Power delay profiles PDPs were measured at every individual pointing angle for each TX and RX location, and integrating each of the PDPs to obtain received power as a function of pointing angle. The result shows that the mean RMS delay spread varies between 7.2 ns and 74.4 ns for 60 GHz and 28 GHz respectively in NLOS scenario.

Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience

Streaming video services represent a very large fraction of global bandwidth consumption. Due to the exploding demands of mobile video streaming services, coupled with limited bandwidth availability, video streams are often transmitted through unreliable, low-bandwidth networks. This unavoidably leads to two types of major streaming-related impairments: compression artifacts and/or rebuffering events. In streaming video applications, the end-user is a human observer; hence being able to predict the subjective Quality of Experience (QoE) associated with streamed videos could lead to the creation of perceptually optimized resource allocation strategies driving higher quality video streaming services. We propose a variety of recurrent dynamic neural networks that conduct continuous-time subjective QoE prediction. By formulating the problem as one of time-series forecasting, we train a variety of recurrent neural networks and non-linear autoregressive models to predict QoE using several recently developed subjective QoE databases. These models combine multiple, diverse neural network inputs, such as predicted video quality scores, rebuffering measurements, and data related to memory and its effects on human behavioral responses, using them to predict QoE on video streams impaired by both compression artifacts and rebuffering events. Instead of finding a single time-series prediction model, we propose and evaluate ways of aggregating different models into a forecasting ensemble that delivers improved results with reduced forecasting variance. We also deploy appropriate new evaluation metrics for comparing time-series predictions in streaming applications. Our experimental results demonstrate improved prediction performance that approaches human performance. An implementation of this work can be found at https://github.com/christosbampis/NARX_QoE_release.

Drastic bandwidth enhancement of bistable energy harvesters: Study of subharmonic behaviors and their stability robustness

In order to provide a serious alternative to chemical batteries for the energy supply of isolated sensors, bistable generators have been enthusiastically highlighted in recent years for their ability to harvest vibration energy over a wider frequency range compared to linear generators. Nevertheless, these bistable harvesters are generally characterized through a frequency sweep which does not reveal all the steady-state behaviors they can reach and therefore their full energy harvesting potential. Among such behaviors, subharmonic motions are hidden by this classical characterization and therefore had not received a lot of attention. This study proposes an original complete analytical analysis of subharmonic orbits for energy harvesting to predict their contribution to the global bandwidth of bistable generators. In addition, a new criterion, referred as stability robustness, is introduced to estimate the sensitivity of those behaviors to disturbances of different levels, allowing to finely and accurately estimate suitable behaviors for energy harvesting purposes in realistic conditions (behaviors easy to reach and maintain in time). Experimental results conducted with a buckled beam based electromagnetic generator confirm the relevance of this criterion showing good agreement with the analytical predictions. Subharmonic behaviors finally appear, both theoretically and experimentally, to be of significant interest, as exploiting them leads to a 180% increase of the global operating frequency range of the considered bistable energy harvester, for which more than 100 µW are generated on a 70 Hz bandwidth at 0.5 g.

On the utility of asymptotic bandwidth selectors for spatially adaptive kernel density estimation

Implementation of the spatially adaptive kernel estimator relies on choice of a ‘global bandwidth’. We derive the closed-form asymptotic bias for this estimator with the aim of developing relevant selectors, and note non-uniform convergence hinders its usability for this purpose.

Nonparametric localized bandwidth selection for Kernel density estimation

ABSTRACTAs conventional cross-validation bandwidth selection methods do not work properly in the situation where the data are serially dependent time series, alternative bandwidth selection methods are necessary. In recent years, Bayesian-based methods for global bandwidth selection have been studied. Our experience shows that a global bandwidth is however less suitable than a localized bandwidth in kernel density estimation based on serially dependent time series data. Nonetheless, a difficult issue is how we can consistently estimate a localized bandwidth. This paper presents a nonparametric localized bandwidth estimator, for which we establish a completely new asymptotic theory. Applications of this new bandwidth estimator to the kernel density estimation of Eurodollar deposit rate and the S&P 500 daily return demonstrate the effectiveness and competitiveness of the proposed localized bandwidth.

16‐Element circular polarised antenna array based on sequential arrangement of non‐identical disc patches

A circular polarised antenna array based on the recursive application of the sequential rotation architecture is presented. The first level sequential arrangement is applied to a set of four non-identical slightly detuned patches, resulting in a broadband improper array. At the second level, the sequential arrangement is applied to four identical first-level sub-arrays, regularising the structure. Experimental validations confirm a gain exceeding 10 dB from 5.35 to 7.25 GHz, with a maximum of 13 dB. Global bandwidth is 30% despite an area of only 2.84 λ 0 2

Fast computation of spatially adaptive kernel estimates

Kernel smoothing of spatial point data can often be improved using an adaptive, spatially varying bandwidth instead of a fixed bandwidth. However, computation with a varying bandwidth is much more demanding, especially when edge correction and bandwidth selection are involved. This paper proposes several new computational methods for adaptive kernel estimation from spatial point pattern data. A key idea is that a variable-bandwidth kernel estimator for d-dimensional spatial data can be represented as a slice of a fixed-bandwidth kernel estimator in $$(d+1)$$ -dimensional scale space, enabling fast computation using Fourier transforms. Edge correction factors have a similar representation. Different values of global bandwidth correspond to different slices of the scale space, so that bandwidth selection is greatly accelerated. Potential applications include estimation of multivariate probability density and spatial or spatiotemporal point process intensity, relative risk, and regression functions. The new methods perform well in simulations and in two real applications concerning the spatial epidemiology of primary biliary cirrhosis and the alarm calls of capuchin monkeys.

Weighted quantile regression for censored data with application to export duration data

Existing literature on censored quantile regression requires global linearity, bandwidth selection, or complex computation. In the current study, we propose weighted quantile regression for censored data with weights obtained through Aalen’s estimator. Our estimator is simple to compute and does not require bandwidth selection and global linearity. It can be applied to unconditionally and conditionally independent censoring even if the censoring depends on the error terms conditional on covariates. The proposed estimator is consistent and asymptotically normal. We illustrate the finite-sample performance of our estimator through simulations. Finally, we apply our method to the export duration data of China’s agricultural products. Empirical results show that the effects of determinants on duration vary across quantiles.

Evaluation of redundant data storage in clusters based on multi-multicast and local storage

Cluster platforms have an important role in high performance computing (HPC). They execute cloud computing, data-intensive computing and data center applications, which are supported on distributed file systems. The implementation of data redundancy in these file systems provides a support for high availability and error tolerance. This work proposes an implementation of redundant data storage based on the storage included in the cluster nodes, instead of more expensive approaches with a dedicated storage and network, and on multi-multicast transfers, instead on unicast transfers, to perform the multiple simultaneous data diffusion required for implementing redundant data storage. The proposal applies a recently proposed congestion control scheme that adjusts the sender injection rate, taking into account control information from the receiver nodes and the storage technology available on the cluster nodes. The implementation takes full advantage of the switch diffusion hardware and of the IGMP snooping capability of current switches, which allows to multicast a packet just to the output links with receivers joined to a multicast group. It is made at the user level directly on the UDP interface. Evaluation tests with multiple simultaneous storage accesses were performed in a CentOS cluster. Test results show a more efficient use of the cluster storage. The global bandwidth improves by using hardware related to the storage (network and storage devices) more efficiently.