Dual-core CPU Research Articles

Optimal wireless power transfer to hybrid energy storage system for electric vehicles: A comparative analysis of machine learning-based model-free controllers

Wireless charging technology for electric vehicles (EVs) is gaining popularity as a result of advancements in battery technology and government incentives. It offers convenience, efficiency, and safety, and is much quicker than traditional plug-in charging. However, optimal power transfer in a wireless power transfer (WPT) system remains a challenge. To design an effective control system for a hybrid energy storage system (HESS), it is important to have an accurate and reliable model of the system. But still, no model can fully represent the nonlinearity of the system. So model-free controllers are designed to operate without an explicit model, using feedback from sensors to adjust the control inputs. This article presents an LCC series-series network-based WPT-HESS model, supported by literature. The HESS system consists of a supercapacitor (SC) and a battery connected to the WPT system through a bidirectional DC–DC buck-boost converter. Three types of machine learning-based model-free controllers, i.e., an artificial neural network controller, a fuzzy logic controller, and a reinforcement learning-based deep Q-network controller, are designed for the HESS DC–DC converters aimed at tracking optimal power. During charging, the supercapacitor is given priority due to its faster charging time, and current references are generated based on the state-of-charge (SoC) of the supercapacitor and battery. The proposed controllers for WPT-HESS are simulated in MATLAB/Simulink and hardware validation is carried out by doing a hardware-in-loop experiment with the C2000 Delfino™ and the MCU F28379D LaunchPad, which has a TMS320F28379D dual-core CPU and runs at 200 MHz.

Efficient Hardware Accelerator Design of Non-Linear Optimization Correlative Scan Matching Algorithm in 2D LiDAR SLAM for Mobile Robots.

Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications.

The demonstration device for remote light control via the Internet by using MQTT protocol and Dual-Chip ESP32

In this article, the primary purpose is to design and implement a remote light control example that makes use of the Arduino IDE platform for programming, the MQTT protocol, and an ESP32 microcontroller, which has a dual-core CPU as well as Wi-Fi and Bluetooth capabilities. This aspect is related to the Internet of Things (IoT), which allows any fundamental device to be linked and become a smart device via the application of the Internet. As a result, these devices can be operated from a significantly long-distance, eliminating the need to physically interact with the switches to turn them on or off. The first part of the paper is a discussion of the fundamental theory that is provided to examine the potential of remote control methods, followed by a short overview of the Arduino integrated development environment (IDE), MQTT communication protocol, and PWM technique to regulate the brightness of lights. The second section is devoted to hands-on work, such as setting up an environment connection over the Internet, compiling and designing a printed circuit board (PCB).

Optimization of Magnetoplasmonic ε-Near-Zero Nanostructures Using a Genetic Algorithm.

Magnetoplasmonic permittivity-near-zero (-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the -near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic -near-zero nanostructures’ capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic -near-zero sensing platform with a sensitivity higher than and a figure of merit in the order of . These last results, higher than any previous magnetoplasmonic -near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer).

MLFTCache: Multilevel Fault Tolerance Scheme for Write-Back L2 Cache Under Irradiation

Cache is prone to soft errors under irradiation, and the incidence rate of multi-bit soft errors in cache memory are becoming larger for modern embedded systems. To improve the cache reliability, we propose a multi-level fault tolerance scheme for write-back L2 cache (MLFTCache). In the workflow of MLFTCache, data bits of cache clean line and dirty line are processed separately. For clean line data bits, 2-way interleaved parity is adopted, and the error is detected and recovered from the next level of memory. For dirty line data bits, the processing is further divided into two layers. The first layer adopts single error correction double error detection (SECDED) code, and the second layer uses multi-bit low redundancy error control with parity sharing (MLREPS) code to detect and correct errors. To reduce the overhead and improve the efficiency of fault tolerance on dirty lines, we adopt two write-back mechanisms. Early write-back mechanism is used to reduce the occurrence of temporal multi-bit soft errors, and emergency write-back mechanism is adopted to deal with the soft error after it is detected. The alpha radiation experiments of Xilinx Zynq-7010 SoC show that cache is prone to multi-bit upsets and the MLFTCache is also dedicated to solving the problem of multi-bit upsets. The cache fault injection and tolerance experiments show that the fault tolerance rate of the MLFTCache proposed in this paper exceeds 90%, while the benchmark schemes are less than 50%. Besides, MLFTCache also has a good performance on dual-core CPU, and its fault tolerance rate on average is 91.34%.

Multiprocessing implementation for MCNP using Python

Monte Carlo N-Particle (MCNP) is a widely-used code in nuclear engineering, but it needs high computation times due to the tracking of every single particle and interaction event. This causes shielding optimization using trial-and-error to take long times to complete. It can be solved by using multiprocessing, but this requires the MCNP source code which can be difficult to obtain. Therefore, this paper aims to suggest a solution on how Python can be used to run multiple instances of the code in shielding optimization. Two hardware setups were tested: one with a dual-core CPU, and one with a six-core CPU. Each of them would run several repeated simulations with and without multiprocessing enabled. Comparisons were made between each case of the same setup to observe the improvements in completion time. A tutorial of the Python algorithm is also provided in the methodology.

Versatile and Concurrent FPGA-Based Architecture for Practical Quantum Communication Systems

This article presents a hardware and software architecture, which can be used in those systems that implement practical quantum key distribution (QKD) and quantum random-number generation (QRNG) schemes. This architecture fully exploits the capability of a System on a Chip (SoC), which comprehends both a field-programmable gate array (FPGA) and a dual-core CPU unit. By assigning the time-related tasks to the FPGA and the management to the CPU, we built a flexible system with optimized resource sharing on a commercial off-the-shelf (COTS) evaluation board, which includes an SoC. Furthermore, by changing the dataflow direction, the versatile system architecture can be exploited as a QKD transmitter, QKD receiver, and QRNG control-acquiring unit. Finally, we exploited the dual-core functionality and realized a concurrent stream device to implement a practical QKD transmitter, where one core continuously receives fresh data at a sustained rate from an external QRNG source, while the other operates with the FPGA to drive the qubit transmission to the QKD receiver. The system was successfully tested on a long-term run proving its stability and security. This demonstration paves the way toward a more secure QKD implementation, with fully unconditional security as the QKD states are entirely generated by a true random process and not by deterministic expansion algorithms. Eventually, this enables the realization of a standalone quantum transmitter, including both the random numbers and the qubit generation.

The time-dependent diffusion equation: An inverse diffusivity problem

We find a solution of an unknown time-dependent diffusivity a(t) in a linear inverse parabolic problem by a modified genetic algorithm. At first, it is shown that under certain conditions of data, there exists at least one solution for unknown a(t) in (a(t), T (x, t)), which is a solution to the corresponding problem. Then, an optimal estimation for unknown a(t) is found by applying the least-squares method and a modified genetic algo rithm. Results show that an excellent estimation can be obtained by the implementation of a modified real-valued genetic algorithm within an Intel Pentium (R) dual-core CPU with a clock speed of 2.4 GHz.

Robust and Efficient RGB-D SLAM in Dynamic Environments

Simultaneous localization and mapping (SLAM) using an RGB-D camera is a key enabling technique for many augmented reality (AR) applications. However, most existing RGB-D SLAM methods could fail in dynamic scenarios due to non-trivial pose estimation errors arising from moving objects. In this study, we present an accurate and robust RGB-D SLAM system for dynamic scenarios which can run real-time on a single dual-core CPU. The core of our system is a robust and efficient dynamic keypoint exclusion method which consists of three steps: 1) grouping spatially and appearance related pixels of a keyframe into regions; 2) identifying dynamic regions by checking motion consistency of keypoints in every region; 3) excluding keypoints in the identified dynamic regions as well as the matching points in the 3D local map. The dynamic keypoint exclusion method can be easily integrated into any keypoint based RGB-D SLAM system for improving the accuracy and robustness in dynamic scenes with trivial time increase (16.6ms per frame). Experimental results on the TUM dataset demonstrates that our method which runs on an Intel i7-4900 CPU is even 2.3X faster than the state-of-the-art method DS-SLAM [1] which runs parallel on a P4000 GPU and a comparable CPU. In addition, our system outperforms the state-of-the-art methods [1]–[4] in terms of smaller absolute trajectory errors (ATE). We also apply our system to a real AR application and live experiments with a hand-held RGB-D camera demonstrate the robustness and generalizability of our method in practical scenarios. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> A demo video is provided on <uri>https://github.com/cc-qy/Dynamic-RGB-D-SLAM</uri> </fn>

Covariate-adjusted species response curves derived from long-term macroinvertebrate monitoring data using classical and contemporary model-based ordination methods

Species response curves describe the change in species abundance or probability of occurrence along an environmental gradient and can be useful tools for understanding the consequences of environmental change. I compare species (taxa) response curves to electrical conductivity derived by two multivariate methods—one based on a principal coordinates analysis (the “classical” method) and the other on latent variable models (the “contemporary” method)—from long-term (33-years) of aquatic macroinvertebrate and water quality data collected at six sites along 2200 km of the Murray River, Australia. In both cases the models included other environmental variables, and the response curves to electrical conductivity were adjusted for these covariates. The response curves were very similar by the two methods, but the contemporary method has many advantages. Unlike the classical method, the contemporary method explicitly accounts for the statistical properties of the data, and in particular the mean-variance relationship, thus enabling valid inference, and it provides interval estimates for the parameters of the model, allowing inferences to be drawn regarding the statistical significance of an individual taxon's relationship with electrical conductivity. For the data set used here with 104 taxa and 269 samples, the classical method, with 999 permutations to compute percentile confidence bands, took 12 min to execute on a laptop running a 2.8 GHz i7-7600U dual core CPU with 16 GB RAM, whereas the contemporary method (using the gllvm R package) took 6 min.

Research on Substation Protection Measurement and Control Integrated Configuration Device Based on SoC Chip

Aiming at the development status of relay protection in intelligent substation, an integrated configuration device of substation protection measurement and control based on SoC chip is proposed. SoC chip integrates dual-core ARM Cortex-A9 and FPGA, which makes full use of the powerful data interaction ability of FPGA and dual-core CPU and realizes the basic function of substation protection and interval measurement and control. With the high integration advantage of SoC chip, it simplifies the hardware plug-in of the device and realizes high integration of substation protection measurement and control functions on a single device.

1.2 Mfps standalone X-ray detector for Time-Resolved Experiments

We present a standalone and autonomous X-ray detector capable of operation with the speed of up to1.2 Mfps. The detector utilizes UFXC32k hybrid pixel detectors for sensing X-rays, Spartan-6 LX45 FPGA placed in commercially available sbRIO 9628 controller for data acquisition and processing including a compression with zero-suppression algorithm. A Linux-RT system working on the 400 MHz Dual-Core CPU is used for FPGA control and data streaming to the higher-level system over 1 Gbps Ethernet connection. 1.2 M frames per second is achieved in so-called burst mode of operation while in zerodead-time mode 70 kfps is possible. Due to efficient data compression in FPGA there's no need of using high-speed transceivers and Frame-Grabber cards on the data server side and the detector can stream the data infinitely over standard 1 Gbps network connection. Operation modes were tested at Advanced Photon Source Synchrotron at Argonne National Laboratory.

Airflow modeling based on zonal method for natural ventilated double skin façade with Venetian blinds

A zonal method based on the airflow model is developed to predict the temperature and airflow of the natural ventilated double skin facade (DSF) with Venetian blinds. Ventilation theories such as the Bernoulli equation, thermal pressure, and airflow network method are used to establish the airflow model. It is validated by the experimental data. The validation results show that the simulated results agree well with the experimental data. In addition, some factors that influence the temperature and airflow rate are discussed. It is found that the slat angle, air cavity thickness, and DSF height have different influences on the temperature and airflow rate of the natural ventilated DSFs with Venetian blinds. Meanwhile, changing the vent area has little influence on the thermal performance. This model is suitable for the dynamic modeling of natural ventilated DSFs with Venetian blinds, and renders with fast speed. A typical city in hot summer and cold zone (HSCW) zone in China - Changsha is taken to demonstrate the evaluation of the annual energy performance of the natural ventilated DSF in this model. The comparative results show that the annual energy demand of the natural ventilated DSF is close to the well thermally insulated fabric. The calculation time is about 51 min under Win 7 system on a standard laptop with 2.90 GHz dual-core CPU.

A parallel branch-and-bound algorithm to compute a tighter tardiness bound for preemptive global EDF

In this paper we present a parallel exact algorithm to compute an upper bound to tardiness of preemptive global EDF (G-EDF) schedulers, named harmonic bound, which has been proved to be up to 30% tighter than previously proposed bounds. Tightness is a crucial property of tardiness bounds: a too loose bound may cause a feasible soft real-time application to be mistakenly deemed unfeasible. Unfortunately, no polynomial-time algorithm is known to date to compute the harmonic bound. Although there is no proof of hardness of any sort either, the complex formula of the bound apparently provides no hints to devise algorithms with sub-exponential worst-case cost. In this paper we address this issue by proposing a parallel, exact, branch-and-bound algorithm to compute the harmonic bound, called harm-BB, which proves to be extremely fast in a large number of experiments. More specifically, we compare its execution times with those of existing polynomial-time algorithms for other known tardiness bounds on 630,000 random task sets. harm-BB outperforms, or is comparable to, the competitor algorithms in all scenarios but the ones with the highest number of processors (7 and 8) and tasks ( $$\sim $$ 50). In the latter scenarios harm-BB is indeed slower than the other algorithms; yet, it was still feasible, as it takes only about 2.8 s to compute the bound on a commodity dual-core CPU. Even better, we show that harm-BB has a high parallel efficiency, thus its execution time may be largely cut down on highly-parallel platforms.

SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot

A new self-assembly modular robot (SMR) SambotII is developed based on SambotI, which is a previously-built hybird type SMR that is capable of autonomous movement and self-assembly. As is known, SambotI only has limited abilities of environmental perception and target recognition, because its STM-32 processor cannot handle heavy work, like image processing and path planning. To improve the computing ability, an x86 dual-core CPU is applied and a hierarchical software architecture with five layers is designed. In addition, to enhance its perception abilities, a laser-camera unit and a LED-camera unit are employed to obtain the distance and angle information, respectively, and the color-changeable LED lights are used to identify different passive docking surfaces during the docking process. Finally, the performances of SambotII are verified by docking experiments.

Joint state-parameter estimation for a control-oriented LES wind farm model

Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models.

Lane departure warning for mobile devices based on a fuzzy representation of images

The use of driver assistance systems is a current trend in the car industry. However, most driver assistance systems require the use of multiple sensors, increasing the cost of their implementation in cars and making them inaccessible to many users. In this paper, we present an “efficient” Lane Departure Warning system for mobile devices, which is real-time, accurate and accessible to most users. On the one hand, the accuracy of the system relies on Line Detection based on Hough Transforms, a combination that has proven to be reliable for this goal in many approaches. On the other hand, the reduction of the computational time, to achieve real-time processing, is due mainly to a proposed fuzzy representation of images. Finally, we also present various tests that show that the system proposed runs at up to 20 FPS on a mobile device (with a dual-core CPU at 1.0 GHz) with a resolution of 320×240px.

Evaluation of the deflated preconditioned CG method to solve bubbly and porous media flow problems on GPU and CPU

SummaryIn both bubbly and porous media flow, the jumps in coefficients may yield an ill‐conditioned linear system. The solution of this system using an iterative technique like the conjugate gradient (CG) is delayed because of the presence of small eigenvalues in the spectrum of the coefficient matrix. To accelerate the convergence, we use two levels of preconditioning. For the first level, we choose between out‐of‐the‐box incomplete LU decomposition, sparse approximate inverse, and truncated Neumann series‐based preconditioner. For the second level, we use deflation. Through our experiments, we show that it is possible to achieve a computationally fast solver on a graphics processing unit. The preconditioners discussed in this work exhibit fine‐grained parallelism. We show that the graphics processing unit version of the two‐level preconditioned CG can be up to two times faster than a dual quad core CPU implementation. John Wiley &amp; Sons, Ltd.

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.

Covariance tracking: architecture optimizations for embedded systems

Covariance matching techniques have recently grown in interest due to their good performances for object retrieval, detection, and tracking. By mixing color and texture information in a compact representation, it can be applied to various kinds of objects (textured or not, rigid or not). Unfortunately, the original version requires heavy computations and is difficult to execute in real time on embedded systems. This article presents a review on different versions of the algorithm and its various applications; our aim is to describe the most crucial challenges and particularities that appeared when implementing and optimizing the covariance matching algorithm on a variety of desktop processors and on low-power processors suitable for embedded systems. An application of texture classification is used to compare different versions of the region descriptor. Then a comprehensive study is made to reach a higher level of performance on multi-core CPU architectures by comparing different ways to structure the information, using single instruction, multiple data (SIMD) instructions and advanced loop transformations. The execution time is reduced significantly on two dual-core CPU architectures for embedded computing: ARM Cortex-A9 and Cortex-A15 and Intel Penryn-M U9300 and Haswell-M 4650U. According to our experiments on covariance tracking, it is possible to reach a speedup greater than ×2 on both ARM and Intel architectures, when compared to the original algorithm, leading to real-time execution.