Central Processing Unit Research Articles

Electronic cooling and energy harvesting using ferroelectric polymer composites

Thermal management emerges as a grand challenge of next-generation electronics. Efforts to develop compact, solid-state cooling devices have led to the exploration of the electrocaloric effect of ferroelectric polymers. Despite recent advances, the applications of electrocaloric polymers on electronics operating at elevated temperatures remain essentially unexplored. Here, we report that the ferroelectric polymer composite composed of highly-polarized barium strontium titanate nanofibers and electron-accepting [6,6] phenyl-C61-butyric acid methyl ester retains fast electrocaloric responses and stable cyclability at elevated temperatures. We demonstrate the effectiveness of electrocaloric cooling in a polymer composite for a pyroelectric energy harvesting device. The device utilizes a simulated central processing unit (CPU) as the heat source. Our results show that the device remains operational even when the CPU is overheated. Furthermore, we show that the composite functions simultaneously as a pyroelectric energy converter to harvest thermal energy from an overheated chip into electricity in the electrocaloric process. This work suggests a distinct approach for overheating protection and recycling waste heat of microelectronics.

Electromagnetic Compatibility Issues in 400-MHz-Band Wireless Medical Telemetry Systems and Their Management Using Simplified Methods for Safe Operation.

Wireless medical telemetry systems (WMTSs) are typical radio communication-based medical devices that monitor various biological parameters, such as electrocardiograms and respiration rates. In Japan, the assigned frequency band for WMTSs is 400 MHz. However, the issues accounting for poor reception in WMTS constitute major concerns. In this study, we analyzed the effects of electromagnetic interferences (EMIs) caused by other radio communication systems, the intermodulation (IM) effect, and noises generated from electrical devices on WMTS and discussed their management. The 400-MHz frequency band is also shared by other radio communication systems. We showed the instantaneous and impulsive voltages generated from the location-detection system for wandering patients and their potential to exhibit EMI effects on WMTS. Further, we presented the IM effect significantly reduces reception in WMTS. Additionally, the electromagnetic noises generated from electrical devices, such as light-emitting diode lamps and security cameras, can exceed the 400 MHz frequency band as these devices employ the switched-mode power supply and/or central processing unit and radiate wideband emissions. Moreover, we proposed and evaluated simple and facile methods using a simplified spectrum analysis function installed in the WMTS receiver and software-defined radio for evaluating the electromagnetic environment.

Progress in Additive Manufacturing of Magnesium Alloys: A Review

Magnesium alloys, renowned for their lightweight yet high-strength characteristics, with exceptional mechanical properties, are highly coveted for numerous applications. The emergence of magnesium alloy additive manufacturing (Mg AM) has further propelled their popularity, offering advantages such as unparalleled precision, swift production rates, enhanced design freedom, and optimized material utilization. This technology holds immense potential in fabricating intricate geometries, complex internal structures, and performance-tailored microstructures, enabling groundbreaking applications. In this paper, we delve into the core processes and pivotal influencing factors of the current techniques employed in Mg AM, including selective laser melting (SLM), electron beam melting (EBM), wire arc additive manufacturing (WAAM), binder jetting (BJ), friction stir additive manufacturing (FSAM), and indirect additive manufacturing (I-AM). Laser powder bed fusion (LPBF) excels in precision but is limited by a low deposition rate and chamber size; WAAM offers cost-effectiveness, high efficiency, and scalability for large components; BJ enables precise material deposition for customized parts with environmental benefits; FSAM achieves fine grain sizes, low defect rates, and potential for precision products; and I-AM boasts a high build rate and industrial adaptability but is less studied recently. This paper attempts to explore the possibilities and challenges for future research in AM. Among them, two issues are how to mix different AM applications and how to use the integration of Internet technologies, machine learning, and process modeling with AM, which are innovative breakthroughs in AM.

Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.

The Role of Turbulence in an Intense Tropical Cyclone: Momentum Diffusion, Eddy Viscosities, and Mixing Lengths

Abstract Improved representation of turbulent processes in numerical models of tropical cyclones (TCs) is expected to improve intensity forecasts. To this end, the authors use a large-eddy simulation (with 31-m horizontal grid spacing) of an idealized category 5 TC to understand the role of turbulent processes in the inner core of TCs and their role on the mean intensity. Azimuthally and temporally averaged budgets of the momentum fields show that TC turbulence acts to weaken the maximum tangential velocity, diminish the strength of radial inflow into the eye, and suppress the magnitude of the mean eyewall updraft. Turbulent flux divergences in both the vertical and radial directions are shown to influence the TC mean wind field, with the vertical being dominant in most of the inflowing boundary layer and the eyewall (analogous to traditional atmospheric boundary layer flows), while the radial becomes important only in the eyewall. The validity of the downgradient eddy viscosity hypothesis is largely confirmed for mean velocity fields, except in narrow regions which generally correspond to weak gradients of the mean fields, as well as a narrow region in the eye. This study also provides guidance for values of effective eddy viscosities and vertical mixing length in the most turbulent regions of intense TCs, which have rarely been measured observationally. A generalized formulation of effective eddy viscosity (including the Reynolds normal stresses) is presented. Significance Statement This study uses a turbulence-resolving simulation of a category 5 tropical cyclone to understand the role of turbulence in intense storms. Results show that turbulence clearly modulates storm structure and intensity. This study provides guidance for the values of turbulent quantities (which are usually parameterized in comparatively coarse operational TC forecast models) in scarcely observed regions of intense storms. Furthermore, a complete formulation of the effective eddy viscosities is proposed, incorporating contributions from typically ignored Reynolds normal stress terms.

Hybrid Spatial and Temporal Computing Histogrammer in Soft Processor Core of a FPGA Device

Hybrid Spatial and Temporal Computing Histogrammer in Soft Processor Core of a FPGA Device

High-performance MIM-type aluminum electrolytic capacitors with durable waterproof and wide temperature window

Capacitors are indispensable components of electronic circuits. Filter capacitors, mainly dominated by electrolytic capacitors, are critical for the accurate power supply of integrated circuits for central processors and storage devices, affecting the performance of advanced and sophisticated electronic equipment. However, electrolytic capacitors are restricted in working temperatures (<150 °C) and humidity conditions due to the inherent characteristics of MnO2 or polymer cathodes that tend to deteriorate at high temperatures and moisture. Here, high temperature resistant and conductivity SnO2 cathode and MIM-like (SnO2/AAO/Al) structures are introduced into aluminum electrolytic capacitors via ALD technology. First achieved in a higher temperature window (-60 °C∼330 °C), the capacitor maintains a stable capacity (114.5 ± 3.6 μF/cm2) and phase angles (-89.5 ± 0.2°) at 120 Hz. The tight stacking and conformability of ALD even allows unencapsulated devices to exhibit durable waterproof, showing stable performance for immersion in water within 90 days. Moreover, a thin amorphous Al2O3 (A) as buffer layer is introduced at electrode/dielectric interface, aiming to barrier the interfacial atomic diffusion at the SnO2/AAO. A stable vdW SnO2/A/AAO interfaces is obtained, proven by MD and DFT calculations. Impressively, the capacitor exhibits high reliability with high breakdown field strength (5.5 MV/cm), low leakage current (7.2 × 10–7 A/cm2 at 1 V) and low loss (2% at 120 Hz). Applied in circuits, the capacitor filters 100 kHz signal with a Vripple of only 15 mV and shows excellent charging and discharging behaviors. The work may pave the way for the practical application of electrolytic capacitors in harsh working environments.

Comparative analysis of the impact in societies of silicon technology against that of steel

This paper will first introduce silicon and steel then deeply discuss the impact to the society. Furthermore, I will also estimate the effect to the future about these two materials. Silicon is one of the most significant and useful elements which have been used in huge amounts of fields. For instant, silicon is the primary material to make semiconductor and optical fiber even to synthesize metal-cermet, which is a core material for the spacecraft. The central processing unit (CPU) is also made by silicon, so it is a common and important element now a day. Although silicon is discovered in early 18 centuries even earlier than steel, it doesnt have a big effect in the second industrial revolution. Difference with silicon, steel is the fundamental material of cars, rail and most of the machine invented in industrial revolution because the durability and plasticity. However, this valuable material cant be efficiently produced until Bessemer converter comes out. This machine developed the first process for manufacturing steel inexpensively.

A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects

With advancements in chip technology, the number of cores in modern commercial processors continues to rise, leading to increased complexity in interconnects and on-chip networks. This complexity, however, exposes significant security vulnerabilities, primarily in the form of side-channel and covert-channel exploits. Unlike other microarchitectural side-channel attacks, those leveraging on-chip interconnects utilize unique characteristics, allowing attackers to develop novel methods that can bypass existing effective defenses. In this paper, we present a comprehensive survey of current side-channel and covert-channel attacks based on processor-on-chip interconnects. We categorize these attacks into three types: contention-based, distance-based, and layout-based, according to the specific interconnect characteristics they exploit, and discuss corresponding countermeasures for each. Finally, we provide an outlook on future development trends in processor interconnect side channels. This survey is the first to specifically focus on interconnect-based side channels in processors.

A longitudinal study during scientific teacher training: the association between affective and cognitive dimensions

Learning processes are undoubtedly linked to affective factors. Motivation, interest, curiosity and emotions have been already detected as the core of learning processes: affective domain modulates learning. In this work, a quantitative longitudinal study (three courses) has been conducted during primary school teacher training. An initial diagnosis of affective variables (emotions and self-efficacy) and scientifical knowledge revealed that prospective teachers claim feeling negative emotions toward physics and chemistry contents whereas these students report low scientific knowledge. Therefore, a metacognitive and emotional program was implemented in order to modify these emotions and to improve learning. After three courses, emotions, self-efficacy and scientific knowledge were assessed again. The results obtained suggest that the implementation of active methodologies for teaching science could promote positive emotions, it could lead prospective teachers to improve their self-efficacy’ perception and promotes their learning. The relationships between the different variables considered in the research were studied, and a significant relationship was found between the affective variables (self-efficacy and emotions) and cognitive variables. Prospective teachers involved in this research reported more positive emotions, an increase in their self-efficacy perception and an improvement in their scientific knowledge. These findings may lead to a deeper understanding of the affective role in science learning and could be extended to other content that also elicits negative emotions.

Multi-model fusion of DSFD and Zero-DCE for low-light face detection

Due to the wide application of face detection, face recognition task under extreme lighting conditions inevitably becomes one of the challenges. Under low-light conditions, the face detection task becomes more complex and difficult due to the presence of more noise and lower visibility in the image. For this extreme environment, this paper proposes a multi-model-assisted solution that combines image enhancement and face detection models to handle this task. The DarkFace dataset was used in this study's validation of a number of distinct face identification models and picture enhancement techniques. The experimental findings demonstrate that, on this dataset, the DSFD (Dual Shot Face Detector) and Zero-DCE (Zero-Reference Deep Curve Estimation) methods perform noticeably better than the other techniques, achieving a recall of 24.61%. Therefore, these two algorithms are selected as the core processing flow in this paper to improve the facial detection system's low-light performance. This combination offers a workable answer to the problem of face detection in low light by effectively increasing the visibility of the picture and raising the accuracy of face detection.

High accuracy line following intelligent car based on infrared sensor

This research aims to describe the design and construction of a line-following intelligent automobile using the TM4C123GH6PZ microcontroller as the central processing unit. The vehicle uses two MG310P20_7 motors, one motor driver circuit and a 12-gray sensor for propulsion, motor control and line detection. The sensors made the high-accuracy angler movement in tests, the vehicle is 91.85% stabile to follow the line, taking an average of 3.44 seconds to complete a 3-meter road. Therefore, this concept demonstrates how infrared sensors and sophisticated control algorithms can be used to be more stable in creating driverless vehicles.

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Abstract Parallel-phase processing enables rapid phase extraction from off-axis digital holograms. To achieve fast and accurate results, the phase reconstruction processes were parallelized using improved filter algorithms and optimized programming strategies. First, an adaptive filtering method based on the Chan-Vese (CV) model which better suits parallelism was designed to extract the +1 term spectrum. We selected suitable computer unified device architecture (CUDA) libraries according to the characteristics of the key phase reconstruction steps. Acceleration technologies, such as virtual memory and shared memory, were used to improve the computational efficiency. Furthermore, we combined an improved 4f optical imaging system with an embedded graphic processing unit (GPU) platform to design a low-cost phase reconstruction system for off-axis digital holography. To verify the feasibility of our method, the reconstructed quality of the CV filtering method was estimated, and the run times of phase retrieval on the central processing unit (CPU) and embedded GPU were compared for off-axis holograms with different pixel sizes. Additionally, the dynamic fluctuation phase maps of water droplet evaporation were retrieved to demonstrate the real-time capability of the method.

The intelligent evaluation in ice and snow tourism based on LSTM network

In order to augment the efficacy of the intelligent evaluation model for assessing the suitability of ice and snow tourism, this study refines the model by incorporating the Long Short-Term Memory (LSTM) network within the framework of the Internet of Things (IoT). The investigation commences with an elucidation of the application of IoT technology in environmental detection. After this, an analysis is conducted on the structure of LSTM and its merits in the realm of time series prediction. Ultimately, a novel model for appraising the suitability of ice and snow tourism is formulated. The efficacy of this model is substantiated through empirical experiments. The results of these experiments reveal that the refined model exhibits exceptional performance across diverse climatic conditions, encompassing mild, cold, humid, and arid climates. In regions characterized by mild climates, the predictive accuracy of the refined model progressively ascends from 88% in the initial quarter to 94% in the fourth quarter, surpassing the capabilities of conventional models. Consistently robust performance is demonstrated by the refined model throughout each quarter. In terms of operational efficiency, comparative analysis indicates that the refined model attains a moderate level, manifesting a 30–33 s runtime and maintaining a Central Processing Unit (CPU) usage rate between 40 and 43%. This observation implies that the refined model adeptly balances precision against resource consumption. Consequently, this study holds significance as a scholarly reference for the integration of IoT and LSTM networks in the domain of tourism evaluation.

Convolutional neural network model compression method

In the field of deep learning, Convolutional Neural Networks (CNN) has become a focal point due to its multi-layered structure and wide application. The success of deep learning is due to the model has more layers and more parameters, which gives it a stronger nonlinear fitting ability. Traditionally, CNNs are primarily run on Central Processing Units (CPUs) and Graphics Processing Units (GPUs). However, CPUs have lower computational power, and GPUs consume a lot of energy. In contrast, Field-Programmable Gate Arrays (FPGAs) offer high parallelism, low power consumption, flexible programming, and rapid development cycles. These combined advantages make FPGAs more suitable for the forward inference processes of deep learning compared to other platforms. However, CNNs has the characteristics of parameter redundancy, the storage cost of deploying it on FPGA is too high. In order to apply CNNs to FPGA, we need to optimize CNNs for compression. Because after compression, This paper analyzes several specific cases of model compression for convolutional neural networks, and summarizes and compares the efficient methods of model compression. The results show that the model compression methods are mainly divided into the structure change of convolutional neural network and the quantization of parameters. The two compression methods can be cascaded at the same time to achieve a better optimization effect.

Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness.

Understanding 3D seismic data visualization with C++, OpenGL and GLSL

Seismic data visualization in 3D space is a valuable interpretation tool. Several open-source visualization tools are available. However, little explanation is provided about the inner working of visualization process. The current work discusses a “hello world” equivalent source code for 3D seismic data visualization using Graphical Processing Units (GPUs) with OpenGL and the OpenGL Shading Language (GLSL) programming languages. Rendering is the core process generating 2D image that we see on the screen from the 3D data structures being visualized. Texture mapping-based rendering commonly applied to seismic data starts with creating an OpenGL object called texture. The texture is then mapped over a rectangular object to display a seismic line. The work presented here is performed using OpenGL, GLSL, C++ and Qt toolkit. Here Qt provides the application GUI framework, C++ is used for data I/O, filtering, and sorting, and OpenGL and GLSL are used for 3D rendering. This paper describes the data flow through the application, and two implementations of vertex and fragment GLSL shaders. Visualization is critical for seismic data processing and interpretation. The low-level details of the process presented here will hopefully help readers in obtaining better understanding of the visualization concept and inner working principle and facilitate further development.

CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture

The concept of “all cores are created equal” has been popular for several decades due to its simplicity and effectiveness in CPU (Central Processing Unit) design. The more cores the CPU has, the higher performance the host owns and the higher the power consumption. However, power-saving is also one of the key goals for servers in data centers and embedded devices (e.g., mobile phones). The big.LITTLE multicore architecture, which contains high-performance cores (namely big core) and power-saved cores (namely little core), has been developed by ARM (Advanced RISC Machine) and Intel to trade off performance and power efficiency. Facing the new heterogeneous computing architecture, the traditional lock algorithms, which are designed to run on homogeneous computing architecture, cannot work optimally as usual and drop into the performance issue for the difference between big core and little core. In our preliminary experiment, we observed that, in the big.LITTLE multicore architecture, all these lock algorithms exhibit sub-optimal performance. The FIFO-based (First In First Out) locks experience throughput degradation, while the performance of competition-based locks can be divided into two categories. One of them is big-core-friendly, so their tail latency increases significantly; the other is little-core-friendly. Not only does the tail latency increase, but the throughput is also degraded. Motivated by this observation, we propose a Core-Aware Lock for the big.LITTLE multicore architecture named CAL, which keeps each core having an equal opportunity to access the critical section in the program. The core idea of the CAL is to take the slowdown ratio as the matric to reorder lock requests of these big and little cores. By evaluating benchmarks and a real-world application named LevelDB, CAL is confirmed to achieve fairness goals in heterogeneous computing architecture without sacrificing the performance of the big core. Compared to several traditional lock algorithms, the CAL’s fairness has increased by up to 67%; and Its throughput is 26% higher than FIFO-based locks and 53% higher than competition-based locks, respectively. In addition, the tail latency of CAL is always kept at a low level.

Epigenetic control of metabolic identity across cell types.

Constraint-based network modelling is a powerful tool for analysing cellular metabolism at genomic scale. Here, we conducted an integrative analysis of metabolic networks reconstructed from RNA-seq data with paired epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC). Applying a state-of-the-art contextualisation algorithm, we reconstructed metabolic networks across 1,555 samples corresponding to 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.

Constructivism Effects of the Project Planning Discourses Used in the Maasai Pastoral Community

In literature, the Maasai community has been identified as the dominating ethnic group among the multiple pastoral communities in Tanzania. The Maasai socio-cultural practice is under external pressure in meaning negotiation and interpretation. The literature identifies globalization and modernity elements as the most influential external factors. Furthermore, project planning is considered as inseparable from globalization and modernity elements as all are focused on social change processes. Different literatures consider discourses as the core of the change process, and that pastoral development projects are associated with discursive practices through its communicative dimensions that entail social interactions whereby discourses are produced, distributed, and consumed. Although studies in development projects are fundamental in understanding discursive practices of such projects, this paper found a very limited body of knowledge on discursive practices in development planning specifically in the Maasai community. Therefore, this paper uses Critical Discourse Analysis (CDA) to underscore constructive effects of the used discourses in project documents targeted to the Maasai community on its wider socio-cultural context. This paper employed Fairclough three dimensional model of CDA as its underpinning theoretical and methodological approach. This model guided analyses of the data retrieved from eight (8) project strategic documents that were targeted to the Maasai community. Analyses involved three analytical levels which are textual analysis, discursive practices analysis and socio-cultural practices analysis level as depicted in the used model. Analysis was eclectic in nature as it involved forth and back analyses between the three levels, whereby the first two levels informs socio-cultural constructivism effects of the used discourses. Generally, the study revealed construction of Maasai’s identity, social relations and varying ideologies as the major three categories of constructivism effects of the underscored project planning discourses in the Maasai community. Furthermore this paper found devastating misconceptions and domination of negative portrayals of the Maasai community within the analyzed documents. Finally this paper recommends radical shift on the conveyed meanings and misrepresentation of the indigenous communities specifically the Maasai community in the global and national strategic and developmental process or frameworks.