Computer Processing Unit Research Articles

A green energy consumption policy of Bluetooth mobile devices for smart cities

Green computing mode becomes a social concern in recently years. Consequently people have higher demand for Bluetooth device with lower power consumption. In this paper, a green energy consumption policy (ECP) of battery of Bluetooth devices during data transmission was put forward. ECP extracted detail information from four key configuration parameters during Bluetooth data transmission, namely, transmission mode, connection type, packet type and communication rate. Based on the verification data time, verification data transmission time and Bluetooth package time acquired by the computer processor unit, it calculated the battery energy consumption during equipment idle. Moreover, it calculated the actual transmission data and energy consumption according to the data size of the transmitted document. A simulation analysis on ECP was carried out in this paper. Results showed that ECP could estimate energy consumption for document transmission in the independent Bluetooth communication module.

Efficient modelling of optimised pure acoustic wave equation in 3D transversely isotropic and orthorhombic anisotropic media

ABSTRACTCompared with full elastic wave equations, pseudo-acoustic wave equations (PWEs) derived from the acoustic approximation can significantly reduce computational cost, thus they are widely used in seismic modelling, imaging and inversion in anisotropic media. However, these PWEs tend to suffer from inevitable SV-wave artefacts and numerical instabilities during wave propagation. The most effective solution to resolve the above issues is to adopt pure acoustic wave equations (PAWEs) which can produce pure P-wave responses without SV-wave residuals. Because the PAWE involves a complicated pseudo-differential operator (PDO), which is difficult to be solved using some common numerical algorithms. Here, we develop a new approach to implement pure acoustic wave propagation in 3D general anisotropic media. By minimising the difference between PDO and the new approximation, we compute optimal coefficients based on a least-squares method, and then derive the new time–space domain PAWEs in both 3D transversely isotropic and orthorhombic anisotropic media. Although the new PAWE contains a Poisson’s equation, we introduce a fast Poisson’s solver by using fast Fourier transform. To further improve computational efficiency, parameter gridding, coefficient reutilisation and graphic processing unit acceleration strategies are employed during wavefield extrapolation. Several numerical examples demonstrate that our proposed schemes can generate a pure and stable acoustic wavefield with high accuracy in general anisotropic media.

Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery

Vehicle detection in remote sensing imagery is a prominent issue over the last few years. In this application, the processing of optical remote sensing images becomes critical due to the complex environment, large size, occlusions and color variations. However, several approaches have been proposed to improve the training process but still, the efforts are moving towards optimal solutions. The training process is time-consuming and a large amount of memory is required to store those training images. Numerous traditional state-of-the-art approaches are suffering from problematic high computational time. In this paper, a new training methodology which is based on compressed and down-scaled images is implemented to reduce the training time. The training images are compressed at Quality Factor (QF) of 50 and down-scaled by scale factor of 0.5 to evaluate the performance for vehicle detection. The existing approaches of computer vision are taking advantage of high computational Graphical Processing Units (GPUs) to speed up the training process. The proposed framework is also a better way to reduce the computational time. To compare performance, we have trained the RCNN, Fast-RCNN, Faster-RCNN and Cascade detectors by using three types of training image sets and several experiments have been performed. More specifically, our approach makes the training faster than the training based on original images and training based on compressed images provides optimal results.

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs.

Road transportation is the backbone of modern economies, albeit it annually costs million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.

Optimal approximation of stochastic integrals in analytic noise model

We study approximate stochastic Itô integration of processes belonging to a class of progressively measurable stochastic processes that are Hölder continuous in the rth mean.Inspired by increasing popularity of computations with low precision (used on Graphics Processing Units – GPUs and standard Computer Processing Units – CPUs), we introduce a suitable analytic noise model of standard noisy information about X and W. In this model we show that the upper bounds on error of the Riemann–Maruyama quadrature are proportional to n−ϱ+δ1+δ2, where n is a number of noisy evaluations of X and W, ϱ ∈ (0, 1] is a Hölder exponent of X, and δ1, δ2 ≥ 0 are precision parameters for values of X and W, respectively. Moreover, we show that the error of any algorithm based on at most n noisy evaluations of X and W is at least C(n−ϱ+δ1). Finally, we report numerical experiments performed on both CPU and GPU, together with some computational performance comparison between those two architectures.

Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling.

Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {Ktrans, Kep, Vb, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10−8%, 10−7%, and ×10−6%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.

Implementation and Identification of Preisach Parameters: Comparison Between Genetic Algorithm, Particle Swarm Optimization, and Levenberg–Marquardt Algorithm

Electromagnetic simulations of devices with soft materials and the study of the influence of cutting edge deformations/stresses on the degradation of magnetic performances require the identification of hysteresis parameters. Hence, computationally efficient implementation and identification of the hysteresis model is needed to obtain a solution with high accuracy within a reasonable time. Many studies on hysteresis identification have been reported, but few compare different methods to identify the optimal one. In the present study, we focus on the Preisach hysteresis model combined with the Lorentz modified distribution function to describe the magnetic behavior of a fully processed nonoriented Fe–3wt% Si steel sheet under static excitation. Three different identification techniques are implemented and evaluated: particle swarm optimization (PSO), genetic algorithms (GAs), and nonlinear least-squares approximation based on the Levenberg–Marquardt (LM) method. We evaluate each approach with regard to the accuracy, central processing unit computation time, and repeatability of the results. All the techniques perform well in this high-nonlinearity problem. The root-mean-square error is < 3%. However, the implementation of the GA is more complex than the PSO and LM methods. The optimized parameters are obtained in a few minutes in the case of the LM method, but a few hours are required for both the other techniques. Therefore, the LM method is the most suitable technique for the identification of Preisach hysteresis.

Control System in Open-Source FPGA for a Self-Balancing Robot

Computing in technological applications is typically performed with software running on general-purpose microprocessors, such as the Computer Processing Unit (CPU), or specific ones, like the Graphical Processing Unit (GPU). Application-Specific Integrated Circuits (ASICs) are an interesting option when speed and reliability are required, but development costs are usually high. Field-Programmable Gate Arrays (FPGA) combine the flexibility of software with the high-speed operation of hardware, and can keep costs low. The dominant FPGA infrastructure is proprietary, but open tools have greatly improved and are a growing trend, from which robotics can benefit. This paper presents a robotics application that was fully developed using open FPGA tools. An inverted pendulum robot was designed, built, and programmed using open FPGA tools, such as IceStudio and the IceZum Alhambra board, which integrates the iCE40HX4K-TQ144 from Lattice. The perception from an inertial sensor is used in a PD control algorithm that commands two DC motors. All the modules were synthesized in an FPGA as a proof of concept. Its experimental validation shows good behavior and performance.

Electron Heating by Langmuir Soliton on Electrostatic Vlasov–Poisson Simulation

The wave-particle interaction has been studied in the presence of Langmuir solitons with non-Maxwellian distribution function electrons. The numerical simulation is based on the phase space volume conserving splitting scheme by Cheng and Knorr. By employing graphics processing unit computing, the calculation speed has increased. Time evolution of the electron distribution function by providing the initial distribution function with the kappa distribution function has been compared with the nominal case starting with the Maxwellian.

Неологізми у сучасній англійській мові на прикладі мови інтернет-ресурсів

This article explores English neologisms in the spheres of information technologies based on the Internet resources. Neologisms and their most essential classification are observed and studied in the article. The most widely used classifications of neologisms according to their ways and methods of creation are clarified. The meanings of neologisms and their influence on the language and language flow in the modern world are identified as well. Due to rapidly developed computer technologies and the Internet, the neologisms and new words are created very quickly and constantly. It is important to state that English is enriched about 800 words yearly. However, it would take a long time to consolidate the position of new words in a language, and to make their content comprehensible to an average citizen. The similar situation is observed with the use of widely used words in a new meaning in other spheres of communication. Along with new inventions and devices, new software is emerging, for example, we now are not able to imagine our lives without search engines and systems, as well as the internet, or devices such as a smartphone. All these phenomena, accompanied by the formation of new vocabulary units, and information resources that have a target audience, namely professional users of information technology products. They provide the spread of the latest vocabulary (i.e. neologisms), its socialization (acceptance in society), and then lexicalization (consolidation in language). We can speculate that neologisms are one of the linguistic means that reflects cultural and technological progress in society. Modern humanity lives in the age of information, the main feature of which is computer technology. That is why the problem of the existence and functioning of the Internet language is urgent. The issue of computer language usage has been studied since the first computer was created in the mid-twentieth century. The researches were carried out by prominent scientists in different countries of the world. It might be added that various aspects of the impact of computer communication on language and vice versa were researched, as well as the relation between the development of lexical units and their ways of appearing in language. But it is essential to note that there are linguistic classifications of neologisms. The first one is according to the way of formation, namely, it names a new subject and gives the old concept a new name in order to renew and refresh the language. The emergence of neologisms of the first type is necessary to name a new phenomena in society, but the second type arises involuntarily. They could also be divided into lexical, authorial, individual-stylistic, phonological, loanwords, semantic and syntactic neologisms. They are also morphological according to the methods of creation, for example, defriend, web master, googlable and facebookian. As for shortening and abbreviation, we could define that such as methods are most widely used in English (CPU – Computing processing unit). To sum up, neologism is one way to increase the vocabulary of the language and a means of generating names for new phenomena in society. They appear in all areas of our lives, but they have gained the most development in the field of information technology because this area is the fastest growing in the world today. Since the primary purpose is to name a new subject, most modern neologisms belong to the lexical group, and they are called a new phenomenon because in our time, a new information revolution is taking place and this process is accompanied by the formation of new concepts and phenomena that need new words. It is also not surprising that the percentage of authorial neologisms is now growing, which has expanded significantly with the development of social networks and independent authors. They form a certain initial group of speakers, sometimes quite large, as the accounts of certain IT journalists have up to one and a half million followers, so the spread of these neologisms is extremely rapid.

Why Did the Machine Work?

Computational functionalism assumes a synonymy between abstract functional processes in the central processing unit of a typical digital computer and the human brain, hence the conclusion that an appropriately programmed computer is a mind. Arguably, the point is that neural firings are synonymous with the transfer of electrical currents. Both are accountable and susceptible to a physicalist’s explanation. But, the reason they both worked is ultimately premised upon a causal relationship with nature. However, to understand why nature works raises some problems. Nature is either a self-propelled machine or is propelled by another force. The paper submits that, much as the discourse implies some form of “theism,” the only consistent construal is functional-theism. This, again, raises further problems.

A parallelizable chaos-based true random number generator based on mobile device cameras for the Android platform

True random number generators are used in high security applications such as cryptography where non-determinism is required. However, they are slower than their pseudorandom counterparts because they need to extract entropy from physical phenomenon. To overcome this drawback, generators have been designed to extract unpredictability from devices such as computer processing units or microphones. This paper introduces a new generator for the Android mobile platform based on images captured by a built-in camera. Although similar generators exist, they suffer from poor performance and a lack of proper security evaluation. The proposed generator implements a chaos-based postprocessing algorithm that eliminates statistical defects and increases its throughput. These goals are achieved by using the inherent properties of a chaotic system to amplify entropy extracted from the captured images. The proposed generator is evaluated in two phases: first, statistical test suites are executed to identify statistical defects. Next, the generator’s forward and backward security is analysed. Results indicate that the proposed true random number generator is able to generate statistically secure true random number sequences faster than existing mobile-based generators. In addition, the generator is designed to support parallel processing, thus allowing its performance to scale according to the mobile device’s multicore architecture.

Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals

In sight of fundamental thermal limits on further substantial performance improvements of modern digital computational processing units, wave-based analog computation is becoming an enticing alternative. A wave, as it propagates through a carefully tailored medium, performs the desired computational operation. Yet, the necessary designs are so intricate that experimental demonstrations will necessitate further technological advances. Here, we show that, counterintuitively, the carefully tailored medium can be replaced with a random medium, subject to an appropriate shaping of the incident wave front. Using tunable metasurface reflect-arrays, we demonstrate our concept experimentally in a chaotic microwave cavity. We conclude that off-the-shelf wireless communication infrastructure in combination with a simple reflect-array suffices to perform analog computation with Wi-Fi waves reverberating in a room.

Investigation of Mesh Size Effect on Dynamic Behaviour of an Assembled Structure with Bolted Joints using Finite Element Method

The predicted results of the finite element (FE) model of an assembled structure with different types of joints are highly dependent on the mesh size of the FE model. The complexity of the FE model has forced engineers to seek the most efficient techniques for the selection of the appropriate mesh size specifically in obtaining accurate predicted results in normal modes analysis. This paper concerns the investigation into the effects of the mesh sizes and selection technique of the appropriate mesh size in the FE modelling and analysis of the assembled structure with bolted joints. The investigation was carried out by predicting the modal parameters of the FE models with the predefined range of mesh sizes. The predicted results of the FE models were compared with the measured counterparts obtained from the experimental modal analysis (EMA). The total error obtained from the comparison between FE and EMA was recorded. Evaluations were made by comparing the number of nodes and elements of the FE models, percentage of total error, computer processing unit (CPU) elapsed time and memory usage. The outcomes of the evaluations showed that there are significant effects of the mesh sizes on the accuracy, computing time and memory usage of the FE modal analysis of the assembled structure with bolted joints. This work also demonstrated an efficient technique for the selection of the appropriate mesh size in achieving a reliable, efficient and economic FE modelling and analysis of the assembled structure with bolted joints.

Experimental and Numerical Study Effect of Using Nanofluids in Perforated Plate Fin Heat Sink for Electronics Cooling

An experimental and numerical investigation of the effect of using two types of nanofluids with suspending of (Al2O3 and CuO) nanoparticles in deionized water with a volume fraction of (0.1% vol.), in addition to use three types of fin plate configurations of (smooth, perforated, and dimple plate) to study the heat transfer enhancement characteristics of commercial fin plate heat sink for cooling computer processing unit. All experimental tests under simulated conditions by using heat flux heater element with input power range of (5, 16, 35, 70, and 100 W). The experimental parameters calculated are such as water and nanofluid as coolant with Reynolds number of (7000, 8000, 9400 and 11300); the air is blown in the inlet duct across the heat sink with Reynolds number of (10500, 12300, 14200 and 16000). The distance fin-to-fin is kept constant at (2.00 mm), and the channel employed in this work has a square cross-section of (7 cm) inside. It was observed that the average effectiveness and Nusselt number of the nanofluids are higher compared with those of using conventional liquid cooling systems. However, the perforated fin plate showed higher air heat dissipation than the other configuration plate fin employed in this study. The experimental results were supported by numerical results which gave a good indication to heat transfer enhancement in studied ranges.&#x0D;

Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission

Gaofen-3 is China's first meter-level multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes for the scientific and commercial applications. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw data simulation is highly demanded. In the paper, the multiple mode SAR simulation framework will be briefly introduced to expose how the raw data simulation guarantees the development of Gaofen-3 and its ground processing system. As an engineering simulation, the complex working modes and practical evaluation requirements of Gaofen-3 mission put forward to the higher demand for simulation simplification and data input/output (I/O) efficiency. To meet the requirements, two improvements have been proposed. First, the stripmap mode based multiple mode decomposition method is introduced to make a solid and simplified system simulation structure. Second, the cloud computing and graphics processing unit (GPU) are integrated to simulate the practical huge volume raw data, resulting in improved calculation and data I/O efficiency. The experimental results of sliding spotlight imaging prove the effectiveness of the Gaofen-3 mission simulation framework and the decomposition idea. The results for efficiency assessment show that the GPU cloud method greatly improves the computing power of a 16-core CPU parallel method about $40\times$ speedup and the data throughput with the Hadoop distributed file system. These results prove that the simulation system has the merits of coping with multiple modes and huge volume raw data simulation and can be extended to the future space-borne SAR simulation.

Magnetohydrodynamics with GAMER

Abstract GAMER, a parallel Graphic-processing-unit-accelerated Adaptive-MEsh-Refinement (AMR) hydrodynamic code, has been extended to support magnetohydrodynamics (MHD) with both the corner-transport-upwind and MUSCL-Hancock schemes and the constraint transport technique. The divergent preserving operator for AMR has been applied to reinforce the divergence-free constraint on the magnetic field. GAMER-MHD has fully exploited the concurrent executions between the graphic process unit (GPU) MHD solver and other central processing unit computation pertinent to AMR. We perform various standard tests to demonstrate that GAMER-MHD is both second-order accurate and robust, producing results as accurate as those given by high-resolution uniform-grid runs. We also explore a new 3D MHD test, where the magnetic field assumes the Arnold–Beltrami–Childress configuration, temporarily becomes turbulent with current sheets, and finally settles to a lowest-energy equilibrium state. This 3D problem is adopted for the performance test of GAMER-MHD. The single-GPU performance reaches 1.2 × 108 and 5.5 × 107 cell updates per second for the single- and double-precision calculations, respectively, on Tesla P100. We also demonstrate a parallel efficiency of ∼70% for both weak and strong scaling using 1024 XK nodes on the Blue Waters supercomputers.

An efficient local operator-based Q-compensated reverse time migration algorithm with multistage optimization

Most existing [Formula: see text]-compensated reverse time migration ([Formula: see text]-RTM) algorithms are based on pseudospectral methods. Because of the global nature of pseudospectral operators, these methods are not ideal for efficient parallelization, implying that they may suffer from high computational cost and inefficient memory usage for large-scale industrial problems. In this work, we reported a novel [Formula: see text]-RTM algorithm — the multistage optimized [Formula: see text]-RTM method. This [Formula: see text]-RTM algorithm uses a finite-difference method to compensate the amplitude and the phase simultaneously by uniquely combining two techniques: (1) a negative [Formula: see text] method for amplitude compensation and (2) a multistage dispersion optimization technique for phase correction. To prevent high-frequency noise from growing exponentially and ruining the imaging results, we apply a finite impulse response low-pass filter using the Kaiser window. The theoretical analyses and numerical experiments demonstrate that this [Formula: see text]-RTM algorithm precisely recovers the decayed amplitude and corrects the distorted phase caused by seismic attenuation effects, and hence produces higher resolution subsurface images with the correct structural depth information. This new method performs best in the frequency range of 10–70 Hz. Compared with pseudospectral [Formula: see text]-RTM methods, this [Formula: see text]-RTM approach offers nearly identical imaging quality. Based on local numerical differential operators, this [Formula: see text]-RTM method is very suitable for parallel computing and graphic processing unit implementation, an important feature for large 3D seismic surveys.

Dynamic simulation and steady-state computation of 3D physiologically structured population models

The PSP modelling approach allows you to model biological/pharmaceutical behaviour by combining micro-scale ordinary differential equation (ODE) models with macro-scale ODE models and their bi-directional interaction. E.g.: based on the model of a single cell, billions of cells can be simulated to get the response of an entire organ (also incorporating the organ to cell reaction). The PSP approach allows to simulate this in a mathematically efficient way by characterising different cells by a set of physiologically relevant quantities [2], [3]. Our framework is capable of taking into account up to 3 physiological parameters resulting in a 3D structure for which a partial differential equation (PDE) should be solved. We achieve considerable speed-up by using a semi-Lagrangian PDE solver that allows big stable time stepping. Combining this with a low level C-language implementation, we achieve exceptional efficient usage of the computing processing unit (CPU). Furthermore we accelerate the computation of the steady-state by using a Newton-Krylov method. For ease of use, a PSP description language is constructed to allow straightforward input of different models. The simulator should empower future computational/mathematical biologists to create and evaluate more detailed models than currently is common practice.