EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Oxygen functionalization-assisted anionic exchange toward unique construction of flower-like transition metal chalcogenide embedded carbon fabric for ultra-long life flexible energy storage and conversion

The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storage and conversion application.The NiCCZ was then oxygen functionalized,facilitating the next step of stoichiometric sulfur anion diffusion during hydrothermal sulfurization,generating a flower-like metal hydroxysulfide structure(NiCCZOS)with strong partial implantation inside CC.Thus obtained NiCCZOS shows an excellent capacity when tested as a supercapacitor electrode in a three-electrode configuration.Moreover,when paired with the biomass-derived nitrogen-rich activated carbon,the asymmetric supercapacitor device shows almost 100%capacity retention even after 45,000 charge–discharge cycles with remarkable energy density(59.4 Wh kg^(-1)/263.8μWh cm^(–2))owing to a uniquely designed cathode.Furthermore,the same electrode performed as an excellent bifunctional water-splitting electrocatalyst with an overpotential of 271 mV for oxygen evolution reaction(OER)and 168.4 mV for hydrogen evolution reaction(HER)at 10 mA cm−2 current density along with 30 h of unhinged chronopotentiometric stability performance for both HER and OER.Hence,a unique metal chalcogenide composite electrode/substrate configuration has been proposed as a highly stable electrode material for flexible energy storage and conversion applications.

Catalytic properties of Ru nanoparticles embedded on ordered mesoporous carbon with different pore size in Fischer-Tropsch synthesis

A series of 3 wt% Ru embedded on ordered mesoporous carbon （OMC） catalysts with different pore sizes were prepared by autoreduction between ruthenium precursors and carbon sources at 1123 K. Ru nanoparticles were embedded on the carbon walls of OMC. Characterization technologies including power X-ray diffraction （XRD）, nitrogen adsorption-desorption, transmission electron microscopy （TEM）, and hydrogen temperature-programmed reduction （H2-TPR） were used to scrutinize the catalysts. The catalyst activity for Fischer-Tropsch synthesis （FTS） was measured in a fixed bed reactor. It was revealed that 3 wt% Ru-OMC catalysts exhibited highly ordered mesoporous structure and large surface area. Compared with the catalysts with smaller pores, the catalysts with larger pores were inclined to form larger Ru particles. These 3 wt% Ru-OMC catalysts with different pore sizes were more stable than 3 wt% Ru/AC catalyst during the FTS reactions because Ru particles were embedded on the carbon walls, suppressing particles aggregation, movement and oxidation. The catalytic activity and C5＋ selectivity were found to increase with the increasing pore size, however, CH4 selectivity showed the opposite trend. These changes may be explained in terms of the special environment of the active Ru sites and the diffusion of products in the pores of the catalysts, suggesting that the activity and hydrocarbon selectivity are more dependent on the pore size of OMC than on the Ru particle size.

Evaluation of the Predicted Particle Properties (P3) Microphysics Scheme in Simulations of Stratiform Clouds with Embedded Convection

To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with two P3 versions,P3-nc and P3-2ice,were evaluated against rain gauge,radar,and aircraft observations.A series of sensitivity experiments were conducted with different collection efficiencies between ice and cloud droplets.The comparison of the precipitation evolution between P3-nc and P3-2ice suggested that both P3 versions overpredicted surface precipitation along the Taihang Mountains but underpredicted precipitation in the localized region on the leeward side.P3-2ice had slightly lower peak precipitation rates and smaller total precipitation amounts than P3-nc,which were closer to the observations.P3-2ice also more realistically reproduced the overall reflectivity structures than P3-nc.A comparison of ice concentrations with observations indicated that P3-nc underestimated aggregation,whereas P3-2ice produced more active aggregation from the self-collection of ice and ice-ice collisions between categories.Efficient aggregation in P3-2ice resulted in lower ice concentrations at heights between 4 and 6 km,which was closer to the observations.In this case,the total precipitation and precipitation pattern were not sensitive to riming.Riming was important in reproducing the location and strength of the embedded convective region through its impact on ice mass flux above the melting level.

Aligned Elongation of Ag Nanoparticles Embedded in Silica Irradiated with High Energy Ni Ions

Metallic nanoparticle （NP） shapes have a significant influence on the property of composite embedded with metallic NPs. Swift heavy ion irradiation is an effective way to modify shapes of metallic NPs embedded in an amorphous matrix. We investigate the shape deformation of Ag NPs with irradiation fluence, and 357 MeV Ni ions are used to irradiate the silica containing Ag NPs, which are prepared by ion implantation and vacuum annealing. The UV-vis results show that the surface plasmon resonance （SPR） peak from Ag NPs shifts from 400 to 377nm. The SPR peak has a significant shift at fluence lower than 1 × 10^14 ions/cm2 and shows less shift at fluence higher than 1 × 10^14 ions/cm2. The TEM results reveal that the shapes of Ag NPs also show significant deformation at fluence lower than 1 × 10^14 ions/cm2 and show less deformation at fluence higher than 1 × 10^14 ions/cm2. The blue shift of the SPR peak is considered to be the consequence of defect production and Ag NP shape deformation, Based on the thermal spike model calculation, the temperature of the silica surrounding Ag particles first increases rapidly, then the region of Ag NPs close to the interface of Ag/silica is gradually heated. Therefore, the driven force of Ag NPs deformation is considered as the volume expansion of the first heated silica layer surrounding Ag NPs.

Substrate Effect on Plasmon Resonance of a Gold Nanoparticle Embedded Amorphous BaTiO₃Film

Two sets of gold nanoparticles (NP) embedded in amorphous BaTiO3 films were prepared by sol-gel method using spin coating. Sample (1) is having BaTiO3 sol with 0.025 gm of Chloroauric acid dissolved in 10 ml of propan-2-ol, while sample (2) is having 0.086 gm of Chloroauric acid in the same amount of propan-2-ol. The films have been deposited on various substrates like borosilicate glass and fused silica. TEM images show that the particles are of 5 and 10 nm in size for the two set of samples, and some are having elongated morphology. Optical absorption properties of these films reveal the substrate and size effect on localised surface plasmon resonance (SPR). It shows a marginal red shift in the plasmon resonance peak from 414 nm to 420 nm in the case of sample (1) and 566 nm to 568 nm for sample (2) as the substrate changed from borosilicate glass to fused silica. It also shows red shift in Plasmon peak as the size increases from 5 to 10 nm and coincides with Mie explanation for the shift with size.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

“Instruction-Category” Approach of Test Suite Construction for Oven Embedded System

Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.

Exploration of the Integration of Software Engineering Thinking into Embedded System Design Course

Engineering practice is the key bridge between college education and actual work in the industry.In order to deliver qualified talents with engineering quality to the industry,this paper explores integrating software engineering thinking into the Embedded System Design course.A practical and effective teaching mode is designed consisting of immersive learning,case-based learning,progressive practice,interactive learning,and autonomous learning.Through this teaching mode,multi-levels of closed-loop have been established including final project cycle closed-loop,testing cycle closed-loop,and product cycle closed-loop.During this process,students gradually transition to putting forward product requirements,carrying out design and development,thinking and solving problems,collaborating,and assuring quality from the perspective of software engineering.The practice results show that students’engineering quality has been significantly improved.

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

一个基于Qt/Embedded的嵌入式Linux应用程序的实现

本文介绍了嵌入式L inux GUI的发展概况,描述了Qt/Embedded 2.3.4版的GUI的开发环境,并结合应用程序PhotoA l-bum阐述了此环境下应用程序的一种开发流程,分析了该应用程序在向项目目标硬件上移植过程中出现的问题,并给出了解决此问题时的几种解决方法。

Qt/Embedded及嵌入式Linux在智能监控系统控制中的应用

主要介绍了基于S3C2440 ARM微处理器的实验室环境监控系统的设计方案,结合项目实例重点研究了智能监控系统中基于Qt/Embedded的图形用户界面(GUI)应用程序和各类传感器Linux驱动程序开发过程及应用疑难点,实现Qt/Embedded与Linux-C系统驱动编程的结合。

Embedded Internet环境下TCP/IP协议簇的约简

基于 Em bedded Internet技术下 ,为了实现非 Internet设备信息的共享和访问 ,作者对 TCP/ IP协议簇进行系统的研究 ,提出在设备中嵌入 thin TCP/ IP协议栈并使其能够实现与网络的透明互联 .对 TCP/ IP协议簇进行尽最大可能的约简而不使其失去标准和相应的机制 ,从而可以利用 TCP/ IP协议在设备与 Internet之间建立通讯联系 ,真正把设备纳入到网络的管理范围 ,实现设备的网络化和智能化管理 .设备中嵌入 TCP/

基于Windows XP Embedded嵌入式车辆导航系统设计与实现

介绍了INS/DR/MM车辆组合导航系统以及系统的部件组成,对系统的软硬件结构进行了分析。结合目前各主流嵌入式平台的性能,利用Windows XP Embedded(XPE)嵌入式操作系统体积小和易扩展的优势,选用Windows XP Embedded作为车辆组合导航系统的开发平台。为了满足车辆导航实时、快速的要求,设计了基于模糊逻辑的地图匹配算法。经过多次实际跑车试验证明,该嵌入式车辆导航系统可以正确实时地将地图匹配后的位置信息显示在电子地图上。

基于ARM9平台上Qt/Embedded的移植与开发

目前,嵌入式Linux操作系统快速发展,并在手持设备、移动设备中得到广泛应用。因此,拥有一个实用友好的图形用户界面就显得尤为重要。文章通过交叉编译源码库和应用程序的方式详细说明了Qt/Embedded在嵌入式ARM9上的移值流程,解决了应用程序跨平台无法运行的问题,并结合实例对应用的程序开发流程进行了阐述。

基于嵌入式Linux系统的Qt/Embedded图像处理界面开发

根据ARM9开发平台与GUI软件开发环境Qt/Embedded,针对嵌入式图像处理系统的开发阐述了基于静态图像处理的嵌入式Linux图形界面的开发流程及编程方法,主要包括编译环境的建立、源文件中的关键函数分析及调试中关键问题的解决方法。试验结果验证了用Qt/Embedded开发的图形用户界面应用于嵌入式图像处理系统上效果良好。

Qt/Embedded在嵌入式Linux系统中的应用

分析和讨论 Qt/Embedded 的主流版本3.x 系列的底层实现技术;结合 2.x 版本系列和 3.x 版本系列,在两种不同的硬件平台(Intel PXA255 开发系统与笔者自行设计的 Motorola MC9328 MX1开发系统)上的移植过程,讨论 Qt/Embedded 的底层设备接口与应用移植技术。

Qt/Embedded在S3C2440平台上的移植与开发

介绍了Qt/Embedded图形用户界面(GUI)的软硬件开发平台,结合具体实例,详细阐述了Qt/Embedded在S3C2440平台上移植的方法和应用程序开发的过程,并在开发平台上经过验证。