Increase In Computational Load Research Articles

A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal

Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.

GFDM-Based Asynchronous Grant-Free Multiple-Access

For next-generation Internet-of-Things (IoT) networks, asynchronous instant transmission has attracted increasing research interest with the expectation of achieving near-zero latency without excessive initiation procedure. However, in an asynchronous multiple-access scenario, there exist significant inter-carrier interference between sub-carriers allocated to different users. To suppress out-of-band emission (OOBE) of each sub-carrier, a new generalized frequency division multiplexing (GFDM) has been proposed, which has lower OOBE than the conventional orthogonal frequency division multiplexing (OFDM). In this paper, by using GFDM, two types of receivers are proposed with the aim of reducing latency and improving throughput: a GFDM-based minimum mean square error (MMSE) receiver and a GFDM-based MMSE-successive interference cancellation (SIC) receiver. Then, we develop a lightweight scheme using an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-conservative rate control with GFDM-based MMSE receivers and also invent a performance-focused scheme using an advanced rate control with GFDM-based MMSE-SIC receivers. In particular, the latter scheme provides higher throughput with limited increase in computational load of user equipments. Numerical results show that with a high successful transmission probability higher than 99 &#x0025;, the performance-focused scheme and the lightweight scheme achieve up to 85 &#x0025; and up to 70 &#x0025; higher throughput compared to the conventional OFDM-based asynchronous multiple-access scheme, respectively. Furthermore, since our proposal does not require any centralized user scheduling or initiation procedure, it presents a significant reduction in latency compared to the existing low-latency technologies.

Harmonic Mitigation In Grid-Tied TPSPPS Via Novel PRESH Controller

The proposed paper deals with a new type of controller credited with a name of “Novel PRESH” helpful in mitigating the harmonic of Grid Tied TPSPPS (Grid-Tied Three Phase Solar Photovoltaic Power System) even during abnormal grid condition. Novel PRESH controller is formed by combining the characteristic of PRES and RESH Controller and its harmonic mitigation analysis is done with the help of both Bode-Plot and MATLAB Simulink. Presently, for eliminating the harmonic from Grid -Tied TPSPPS, various filters and controllers have already been proposed but it either resulted in increase of computational load or make the system bulky. Earlier technique develops in eliminating the harmonic face difficulty in eliminating the harmonic when the reference waveform get distorted from its original position. The proposed “Novel PRESH” controller can also be incorporated when grid suffers from any abnormal condition. The presented paper will also discuss the performance analysis of controller along with its design procedure that are helpful in mitigating the THD of three phase SPPS near to 1% and thus satisfying the IEEE standard 519 and 1547.

A Novel PRESH controller design for three phase grid tied SPPS

The proposed paper presents the novelty of new type of controller for three phase grid tied SPPS. This new controller is credited with a name of “PRESH” helpful in mitigating the harmonic of three phase GTSPPS (Grid Tied Solar Photovoltaic Power System) even during abnormal grid condition. Presented Novel PRESH controller is formed by combining the characteristic of PRES and RESH Controller but is totally different from the already proposed controller of present days. Further, investigation of Novel PRESH controller is done in term of its harmonic mitigation technique with the help of both Bode-Plot and MATLAB Simulink. Presently, for eliminating the harmonic from three phase GTSPPS, various filters and controllers have already been proposed but it either resulted in increase of computational load or make the system bulky. Earlier technique develops in eliminating the harmonic face difficulty in eliminating the harmonic when the reference waveform gets distorted from its original position. The proposed Novel “PRESH” controller can also be incorporated when grid suffers from any abnormal condition. The proposed paper has also discussed the performance analysis of Novel PRESH controller along with its design procedure that are helpful in mitigating the THD of three phase SPPS near to 1% and thus satisfying the IEEE standard 519 and 1547.

Exploiting Computation Replication for Mobile Edge Computing: A Fundamental Computation-Communication Tradeoff Study

Existing works on task offloading in mobile edge computing (MEC) networks often assume a task is executed once at a single edge node (EN). Downloading the computed result from the EN back to the mobile user may suffer long delay if the downlink channel experiences strong interference or deep fading. This paper exploits the idea of computation replication in MEC networks to speed up the downloading phase. Computation replication allows each user to offload its task to multiple ENs for repetitive execution so as to create multiple copies of the computed result at different ENs which can then enable transmission cooperation and hence reduce the communication latency for result downloading. Yet, computation replication may also increase the communication latency for task uploading, despite the obvious increase in computation load. The main contribution of this work is to characterize asymptotically an order-optimal upload-download communication latency pair for a given computation load in a multi-user multi-server MEC network. Analysis shows when the computation load increases within a certain range, the downloading time decreases in an inversely proportional way if it is binary offloading or decreases linearly if it is partial offloading, both at the expense of linear increase in the uploading time.

Pilot Sequence Design for Mitigating Pilot Contamination With Reduced RF Chains

Massive multiple-input multiple-output (MIMO) communication is a promising technology for increasing spectral efficiency in wireless networks. Two of the main challenges massive MIMO systems face are degraded channel estimation accuracy due to pilot contamination and increase in computational load and hardware complexity due to the massive number of antennas. In this paper, we focus on the problem of channel estimation in massive MIMO systems, while addressing these two challenges: We jointly design the pilot sequences to mitigate the effect of pilot contamination and propose an analog combiner which maps the high number of sensors to a low number of RF chains, thus reducing the computational and hardware cost. We consider a statistical model in which the channel covariance obeys a Kronecker structure, and treat two special cases, corresponding to fully- and partially-separable correlations. We prove that under these models, the analog combiner design can be performed independently of the pilot sequences. Given the resulting combiner, we derive a closed-form expression for the optimal pilot sequences in the fully-separable case and suggest a greedy sum of ratio traces maximization (GSRTM) method for designing sub-optimal pilots in the partially-separable scenario. We demonstrate via simulations that our pilot design framework achieves lower mean squared error than the common pilot allocation framework previously considered for pilot contamination mitigation.

Binary neural networks for speech recognition

Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.

Joint Object Detection and Depth Estimation in Multiplexed Image

This paper presents an object detection method that can simultaneously estimate the positions and depth of the objects from multiplexed images. Multiplexed image is produced by a new type of imaging device that collects the light from different fields of view using a single image sensor, which is originally designed for stereo, 3D reconstruction and broad view generation using computational imaging. Intuitively, multiplexed image is a blended result of the images of multiple views and both of the appearance and disparities of objects are encoded in a single image implicitly, which provides the possibility for reliable object detection and depth/disparity estimation. Motivated by the recent success of CNN based detector, a multi-anchor detector method is proposed, which detects all the views of the same object as a clique and uses the disparity of different views to estimate the depth of the object. The proposed method is interesting in the following aspects: firstly, both locations and depth of the objects can be simultaneously estimated from a single multiplexed image; secondly, there is almost no computation load increase comparing with the popular object detectors; thirdly, even in the blended multiplexed images, the detection and depth estimation results are very competitive. There is no public multiplexed image dataset yet, therefore the evaluation is based on the simulated multiplexed image using the stereo images from KITTI, and very encouraging results have been obtained.

Using spike train distances to identify the most discriminative neuronal subpopulation

BackgroundSpike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses. New methodFor the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best. ResultsThe best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease. Comparison with existing methodsSpike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary. ConclusionsThe spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.

Guaranteeing the integrity and reliability of distributed personal information access records

Attackers try to forge or delete personal information access records to hide traces of their attacks. As personal information access records can be used to analyze infringement accidents or as legal evidence in the event of malicious attacks, maintaining their integrity is very important. This article presents measures to efficiently prove the integrity of distributed personal information access records. To construct a reliable log system, diversified security requirements are established, and mechanisms such as a hash chain, message authentication code, and Merkle tree are incorporated. Moreover, as integrity is proved through a third-party verification institution, attacks by external as well as internal attackers can be detected. During the validation process, existing log record protection methods fail to detect forgery or deletion of certain data or have difficulty identifying the time of attack, but such drawbacks are addressed by the proposed integrity verification process, with only a minor increase in computational load.

Low-cost adaptive square-root cubature Kalman filter for systems with process model uncertainty

A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of process models while only increasing the computational load slightly. It is well-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading factor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as well as STSCKF, while the increase in computational load is much lower than STSCKF.

A dimension reduced INS/VNS integrated navigation method for planetary rovers

Inertial navigation system/visual navigation system (INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors (position and attitude) of planetary rovers, the performance of the Kalman filter (KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter (EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.

Establishment of a simplified simulation method for Axially Reinforced Pneumatic Artificial Muscle by introducing Anisotropic Material

Pneumatic soft actuators are expected to be applied to various mechanisms in different fields, agriculture, medical, welfare etc. Axially Reinforced Pneumatic Artificial Muscle (ARPAM) has fibers lined along the length of its body which helps it to contract axially when pressurized. However, the fibers make the simulation process complex because of the difficulty in modeling and increase in computational load. In this research, a novel analysis method, realizing an instant simulation by introducing anisotropic material instead of fibers has been investigated.

A local power‐law approximation to a smooth viscosity curve with application to flow in conduits and coating dies

Coating dies distribute liquid into a uniform layer for coating onto a moving substrate. A die comprises one or two cavities spanning the coating width and adjoining narrow slots of much higher resistance to flow. In modeling coating dies, the flows in the slots and cavities are often approximated as one‐dimensional to achieve a fully geometrically parameterized model of low computational load suitable for optimizing design for multiple liquids and flow rates. The power‐law model is mathematically efficient for one‐dimensional flows of shear‐thinning liquids but does not include limiting viscosities at low and high shear rates that are frequently present. In previous work, the truncated power‐law model, which is terminated at the limiting Newtonian viscosities, was used to alleviate this shortcoming without sacrificing the mathematical advantages. In this work, the Carreau–Yasuda model replaces the truncated power‐law model as an improvement. For flows in slots and cavities, the Carreau–Yasuda model can be approximated accurately by a local power‐law model with little increase in computational load over the truncated power‐law model. In the transition regions of the Carreau–Yasuda model between Newtonian and power‐law behavior, the local power‐law model gives more accurate results than the truncated power‐law model. POLYM. ENG. SCI., 54:2301–2309, 2014. © 2013 Society of Plastics Engineers

Identification of step response estimates utilizing continuous time subspace approach

Direct identification procedures using raw data seem to face difficulties especially when the data is corrupted with noise or the data acquisition leads to huge amount of data to be processed. This will lead to complexity in obtaining the accurate model of the system and the increase of computational load and time may also arise. In this paper, we present 2-stage identification, in which, the first stage involves a process to obtain step response estimates. A multi input multi output frequency sampling filter model is used to simulate the estimates. With the aid of finite impulse response model, maximum likelihood method and the predicted sum of square statistics, this procedure able to clean the noise that occurred at high frequency region, compressed the data into the reduced amount and obtained only meaningful parameter that describes the system. Next, at the second stage the continuous time subspace model identification is conducted using the step response estimates obtained from the first stage. Here, three continuous time subspace methods will be observed to develop a state space mathematical model; those are the MOESP, CCA and ORT methods. A Monte Carlo simulation is performed as to see the efficacy and robustness of those models in identifying the step response estimates of the observed system. Comparative analysis with respect to two-stage identification and direct identification procedure is also conducted. This is to show the significant contribution of having MIMO FSF in the overall identification procedure. From results, the developed MIMO FSF is able to compress raw MIMO data into fewer numbers, and produce cleaned and unbiased step response estimates. When it is implemented to MIMO continuous-time subspace identification, MOESP method has demonstrated good performance based on the accuracy and robustness of the developed model.

A multiple hypothesis tracker with interacting feature extraction

The multiple hypothesis tracker (mht) is an optimal tracking method due to the enumeration of all possible measurement-to-track associations. However, its practical implementation is limited by the exponential growth in this enumeration. Feature data, in addition to kinematic measurements, can improve track quality and reduce false tracks. In this paper, a new approach whereby there is an intrinsic interaction between feature extraction and the mht is presented. A measure of the feature extraction quality is input into the measurement-to-track association while the prediction step feeds back information to be used in the next round of feature extraction. The motivation for this interaction between feature extraction and tracking is to improve the performance in both steps. In addition, a track-specific detection probability becomes available to the prior. This prior probability moderates the state covariance growth when measurements are not available for track continuation. This moderation permits growth of the tracking gate when a target is out maneuvering the tracker, while this growth is limited when the target is within the tracking gate but with a low probability of detection. Simulation results demonstrate the benefits of the proposed approach with a small increase in computational load.

Telephone Channel Compensation in Speaker Verification Using a Polynomial Approximation in the Log-Filter-Bank Energy Domain

This correspondence presents a novel feature-space channel compensation technique that models the convolutional distortion in the log-energy mel-filter domain by means of a polynomial approximation. The proposed parametric distortion model generates appropriate constraints in the spectral domain that help to improve the channel cancelling estimation with limited data. In a text-dependent speaker verification task, the polynomial-based channel estimation scheme can lead to reductions in equal error rate (EER) as great as 22% and 8% when compared with the baseline system and with the standard cepstral bias removal approach, respectively, with no significant increase in computational load.

A semi-implicit approach for fast parameter estimation by means of the extended Kalman filter

An assessment is performed of the impact of the time-integration scheme on data assimilation based on the extended Kalman filter approach. The usage of the implicit Euler scheme, which leads to unconditionally stable model updates for the whole range of time steps, results in a less accurate estimation of model parameters, which can be overcome using a parameter estimator based on the explicit Euler scheme. However, the latter limits the maximum time step which may lead to an unacceptable increase in computational load. The alternative can be found using a semi-implicit estimator, where a model is updated using the implicit Euler scheme, whereas the propagation of the error covariance matrix in time is based on the explicit time integration scheme. The improved properties of the proposed algorithm are demonstrated on a one-dimensional problem of permeability estimation in a porous medium for a single phase oil flow.

Application of Unscented Transformation in Nonlinear System Identification

AbstractThis article addresses the problem of system identification of nonlinear dynamical state-space models from input and output data. The problem is tackled by using Expectation Maximization (EM) algorithm for calculating the Maximum Likelihood (ML) estimates of the model parameters. The novelty of the presented algorithm is related to an efficient employment of the Unscented Transformation (UT) in the expectation step, which lowers the number of computations required. This property enables the algorithm to cope with high-dimensional models without a significant increase in computational load. The overall performance of the algorithm is demonstrated using both numerical and real data examples.

Utilization driven power-aware parallel job scheduling

In this paper, we propose UPAS (Utilization driven Power-Aware parallel job Scheduler) assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in its power dissipation and energy consumption, but introduces a penalty in its performance. Furthermore, performance of other jobs may be affected as their wait times can increase. For this reason, we propose to apply DVFS when system utilization is below a certain threshold, exploiting periods of low system activity. As the increase in run times due to frequency scaling can be seen as an increase in computational load, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors and scheduling jobs with UPAS can lead to lower energy consumption and higher performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 8% on average depending on allowed job performance penalty. Applying UPAS to 20% larger systems, CPU energy needed to execute same workloads can be decreased by 20% while having same or better job performance.