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.

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.

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.

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.

ARM-Based Embedded System Platform and Its Portability Research

Taking ARM as the hardware platform, the embedded system is built from both hardware and software aspects with the application as the center. In the hardware design, build the hardware platform scheme, design the schematic diagram as well as PCB, complete the hardware debugging, and ensure the system hardware platform function;in the software design, optimize the three-stage pipeline structure of ARM instruction system, design the instruction set, install the embedded system on the virtual machine, build the cross-toolchain, and set up the correct NFS network file system. Finish the design of the ARM-based embedded system platform, combined with the hardware requirements of the experimental platform, transplant the powerful Uboot as the Bootloader of the system, and further transplant the Linux-2.6. 32 kernel to the system start the operation normally, and finally, build the root file to finish the study of its portability.

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.

Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

Key Technology of Embedded System Implementation for Software-based CNC System

The software-based computer numerical control（CNC） system includes three types of tasks： periodic real-time tasks, aperiodic real-time tasks, and non-real-time tasks. The tasks are characterized by concurrency, hybridization, and correlation, which make system implementation difficult. The conventional scheduling algorithm can not meet the demands of system implementation in the software-based CNC system completely. The uncertainty factors when running real-time tasks affect control performance by degrading manufacturing accuracy as a result of system resource and processor use restrictions. To address the technical difficulty of embedded system implementation, a novel fuzzy feedback scheduling algorithm based on output jitter of key real-time tasks for a software-based CNC system is proposed. Time characteristics, such as sampling jitter, input-output jitter, and non-schedulability are discussed, followed by quantification through simulations of the impact of time characteristics on manufacturing accuracy. On the basis of this research, the scheduler architecture is designed, and then the algorithm table is calculated. When the system resource changes, the key periodic real-time tasks meet their deadlines by means of dynamically adjusting the task period. The simulated results show that the machining precision rises by an order of magnitude for the proposed scheduler in resource-constrained software-based CNC systems. Moreover, unlike conventional feedback scheduling methods, the algorithm in this paper does not rely on the availability of task execution times and is easy to implement while incurring only a small overhead.

Embedded BCI Rehabilitation System for Stroke

In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

Modeling and Analysis of Scheduling for Distributed Real-time Embedded Systems

Aimed at the deficiencies of resources based time Petri nets （RBTPN） in doing scheduling analysis for distributed real-time embedded systems, the assemblage condition of complex scheduling sequences is presented to easily compute scheduling length and simplify scheduling analysis. Based on this, a new hierarchical RBTPN model is proposed. The model introduces the definition of transition border set, and represents it as an abstract transition. The abstract transition possesses all resources of the set, and has the highest priority of each resource; the cxecution time of abstract transition is the longest time of all possible scheduling sequences. According to the characteristics and assemblage condition of RBTPN, the refinement conditions of transition border set are given, and the conditions ensure the correction of scheduling analysis. As a result, it is easy for us to understand the scheduling model and perform scheduling analysis.

Research and Development of an Embedded Numerical Control System

This paper puts forward a new scheme of Embedded Numerical Control System based on ARM and DSP,which is at the base of research on traditional numerical control system and embedded technology.And the paper also describes the develop- ment of hardware and software platform.All the development and realization are based on the idea of module design.The embed- ded numerical control system,using ARM and DSP to construct the main control platform,realizes the real-time operation of sys- tem and improves the stability and reliability with the modular designing ideas of hardware and software and with the support of embedded real-time operating system(uc/os-Ⅱ).And the system could realize the multi-network supporting,which is also accord with the development of modularization,flexibility and latticing of numerical control system.

Research on Remote Video Monitoring System Used for Numerical Control Machine Tools Based on Embedded Technology

This paper designed an embedded video monitoring system using DSP (Digital Signal Processing ) and ARM (Ad- vanced RISC Machine).This system is an important part of self-service operation of numerical control machine tools,At first the analog input signals from the CCD(Charge Coupled Device) camera are transformed into digital signals,and then output to the DSP system,where the video sequence is encoded according to the new generation image compressing standard called H.264.The code will be transmitted to the ARM system through xBus,and then be packed in the ARM system and transmitted to the client port through the gateway.Web technology,embedded technology and image compressing as well as coding technology are integrated in the system,which can be widely used in self-service operation of numerical control machine tools and intelligent robot control areas.

AN EMBEDDED BOUNDARY METHOD FOR ELLIPTIC AND PARABOLIC PROBLEMS WITH INTERFACES AND APPLICATION TO MULTI-MATERIAL SYSTEMS WITH PHASE TRANSITIONS

The embedded boundary method for solving elliptic and parabolic problems in geometrically complex domains using Cartesian meshes by Johansen and Colella （1998, J. Comput. Phys. 147, 60） has been extended for elliptic and parabolic problems with interior boundaries or interfaces of discontinuities of material properties or solutions. Second order accuracy is achieved in space and time for both stationary and moving interface problems. The method is conservative for elliptic and parabolic problems with fixed interfaces. Based on this method, a front tracking algorithm for the Stefan problem has been developed. The accuracy of the method is measured through comparison with exact solution to a two-dimensional Stefan problem. The algorithm has been used for the study of melting and solidification problems.

Low-Cost Embedded Controller for Complex Control Systems

Because of limited resource of embedded platforms, the computational complexity of advanced control algorithms raises significant challenges for the use of embedded systems in complex control field. A Scilab/Scicos based embedded controller is developed on which various control software can be easily modeled, simulated, implemented, and evaluated to meet the ever-expanding requirements of industrial control applications. Built on the Cirrus Logic EP9315 ARM systems-on-chip board, this embedded controller is possible to develop complex embedded control systems that employ advanced control strategies in a rapid and cost-efficient fashion. Due to the free and open source nature of the software packages used, the cost of the embedded controller is minimized.

A discrete model for response estimation of soil-structure systems with embedded foundations

The need for simplified physical models representing frequency dependent soil impedances has been the motivation behind many researches throughout history. Generally, such models are generated to capture impedance functions in a wide range of excitation frequencies, which leads to relatively complex models. That is while there is just a limited range of frequencies that really influence the response of the structure. Here, a new methodology based on the response-matching concept is proposed, which can lead to the development of simpler discrete models. The idea is then used to upgrade an existing simple model of surface foundations to the case of embedded foundations. The applicability of the model in both frequency domain and time domain analyses of soil-structure systems with embedded foundations is discussed. Moreover, the accuracy of the results is compared with another existing discrete model for embedded foundations.

A Low-Cost Active Control Multi-Fan Turbulence Wind Tunnel with an Embedded System to Generate Natural Wind

This paper describes a new actively controlled multi-fan wind tunnel that generates natural wind as a type of turbulence wind tunnel at a reduced cost. The driving section of the wind tunnel has 100 PC cooling fans that are controlled by an original embedded system. The fluctuating velocity wind is successfully generated with a mean velocity of 7 m/s and two turbulent intensities of 2% and 3% based on Karman’s power spectrum density function. The case of 2% has the integral scales of 5 m, 10m and 20 m, and the case of 3% has the integral scales of 3 m, 6 m and 15 m with a turbulence grid. In particular, the wind with the turbulent intensity of 2% satisfies the Kolmogorov’s -5/3 multiplication rule of inertial subrange with the frequency range from 0.01 Hz to 2.0 Hz. Consequently, the new wind tunnel can be used for studying engineering technology and research regarding conditions with natural wind.

Analysis and Comparison of Five Kinds of Typical Device-Level Embedded Operating Systems

Today, the number of embedded system was applied in the field of automation and control has far exceeded a variety of general-purpose computer. Embedded system is gradually penetrated into all fields of human society, and ubiquitous embedded applications constitute the 'ubiquitous' computing era. Embedded operating system is the core of the em-bedded system, and it directly affects the performance of the whole system. Our Liaoning Provincial Key Laboratory of Embedded Technology has successfully developed five kinds of device-level embedded operating systems by more than ten years’ efforts, and these systems are Webit 5.0, Worix, μKernel, iDCX 128 and μc/os-II 128. This paper mainly analyses and compares the implementation mechanism and performance of these five kinds of device-level embedded operating systems in detail.

A State-of-the-Art Survey on Real-Time Issues in Embedded Systems Virtualization

Virtualization has gained great acceptance in the server and cloud computing arena. In recent years, it has also been widely applied to real-time embedded systems with stringent timing constraints. We present a comprehensive survey on real-time issues in virtualization for embedded systems, covering popular virtualization systems including KVM, Xen, L4 and others.

A Self‑Powered,Highly Embedded and Sensitive Tribo‑Label‑Sensor for the Fast and Stable Label Printer

Label-sensor is an essential component of the label printer which is becoming a most significant tool for the development of Internet of Things(IoT).However,some drawbacks of the traditional infrared label-sensor make the printer fail to realize the high-speed recognition of labels as well as stable printing.Herein,we propose a selfpowered and highly sensitive tribo-label-sensor(TLS)for accurate label identification,positioning and counting by embedding triboelectric nanogenerator into the indispensable roller structure of a label printer.The sensing mechanism,device parameters and deep comparison with infrared sensor are systematically studied both in theory and experiment.As the results,TLS delivers 6 times higher signal magnitude than traditional one.Moreover,TLS is immune to label jitter and temperature variation during fast printing and can also be used for transparent label directly and shows long-term robustness.This work may provide an alternative toolkit with outstanding advantages to improve current label printer and further promote the development of IoT.