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.

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.

Stable water droplets on composite structures formed by embedded water into fully hydroxylated β-cristobalite silica

Using molecular dynamics simulations, we have revealed a novel wetting phenomenon with a droplet on composite structures formed by embedded water into(111) surface of β-cristobalite hydroxylated silica. This can be attributed to the formation of a composite structure composed of embedded water molecules and the surface hydroxyl(–OH) groups,which reduces the number of hydrogen bonds between the composite structure and the water droplet above the composite structure. Interestingly, a small uniform strain(±3%) applied to the crystal lattice of the hydroxylated silica surface can result in a notable change of the contact angles(> 40°) on the surface. The finding provides new insights into the correlation between the molecular-scale interfacial water structures and the macroscopic wettability of the hydroxylated silica surface.

Technique of Embedding Depth Maps into 2D Images

This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform （DCT） data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.

On Embedding of m-Sequential k-ary Trees into Hypercubes

In this paper, we present an algorithm for embedding an m-sequential k-ary tree into its optimal hypercube with dilation at most 2 and prove its correctness.

On Some Embedment of Groups into Wreath Products

In this paper, we showed how groups are embedded into wreath products, we gave a simpler proof of the theorem by Audu (1991) (see [1]), also proved that a group can be embedded into the wreath product of a factor group by a normal subgroup and also proved that a factor group can be embedded inside a wreath product and the wreath product of a factor group by a factor group can be embedded into a group. We further showed that when the abstract group in the Universal Embedding Theorem is a p-group, cyclic and simple, the embedding becomes an isomorphism. Examples were given to justify the results.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

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.

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.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

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.

Embedding-based approximate query for knowledge graph

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.

TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

“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.

Thin-Shell Wormholes Admitting Conformal Motions in Spacetimes of Embedding Class One

This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while the surface pressure is negative, resulting in , thereby signaling a violation of the null energy condition, a necessary condition for holding a wormhole open. For a Morris-Thorne wormhole, matter that violates the null energy condition is referred to as “exotic”. For the thin-shell wormholes in this paper, however, the violation has a physical explanation since it is a direct consequence of the embedding theory in conjunction with the assumption of conformal symmetry. These properties avoid the need to hypothesize the existence of the highly problematical exotic matter.

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。