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With the rapid development of information technology, rural financial management is facing the great challenge of digitalization and intelligentization. This study aims to explore the application of embedded systems combined with blockchain technology in rural financial management to provide more efficient and secure solutions. Firstly, the main problems in rural financial management are analyzed, including low data processing efficiency and poor data security. It then explores the potential applications of embedded systems in financial management, as well as the main characteristics and classifications of blockchain technology. Based on this, a model integrating embedded system and blockchain technology is designed, and the effectiveness of the model is verified by experiments. The experimental results show that the system not only significantly improves the data processing speed, but also enhances the data security and improves the user satisfaction. In general, this study provides theoretical and practical support for the technical innovation of rural financial management, and shows a broad prospect in practical application. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

With the emergence of RISC-V architecture in embedded devices, its inherent low-power features have propelled its extensive adoption across various industrial settings. Displacement sensors leveraging Hall sensors and magnetic flux measurement present notable benefits including cost-effectiveness and compact design. This study undertakes the porting of Hall sensors onto RISC-V architecture embedded devices, validating their functionality within displacement sensors. Empirical investigations substantiate that the ported system consistently delivers comparable outcomes to those obtained from x86 architecture systems employing PM-MFM methods, affirming its reliability and performance in practical applications. [ABSTRACT FROM AUTHOR]

Aiming to address the issues of low data execution efficiency and slow storage speed on traditional tourism cloud service platforms, a rural tourism management cloud service platform based on an embedded system is proposed. The embedded device components, such as the ARM processor, NOR Flash memory, and RTL network chip, are utilized to optimize the hardware structure of the cloud service platform. This optimization aims to enhance data processing and storage efficiency, as well as improve the reliability and durability of system operation. The unstructured feature grasping algorithm is employed to gather rural tourism resource data for target demand characterization. Associated algorithms are then utilized to develop data analysis, service recommendation, and tourism management modules, creating a comprehensive tourism cloud service platform. Additionally, the shortest path algorithm is used to enhance database storage speed, improve business docking efficiency, establish a big data monitoring center, comprehensively monitor changes in the scenic area and related industries, and enhance platform service efficiency. It has been proven through experiments that the latency of the tourism management cloud service platform, based on the embedded system, is reduced by almost 46.7% compared to the traditional approach, and the throughput is increased to 0.69 b/s. The practical application effect is positive, as it contributes to the development of tourism resources and the economic growth of rural areas. It also brings about economic and social benefits. [ABSTRACT FROM AUTHOR]

Cloud computing and mobile connectivity has continued to reposition interactive access to data. Audio contents have also evolved in its production, and consumption due to the rise in the use of streaming platforms. Sales of physical audio copies continue to decline significantly especially with the mandatory streaming as contents are reinvented using these platforms services as a key part of its business development. Online streaming of contents enable access to a vast collection in commercial and governmental databases, without need for a permanent transfer of data to and from a user's device. Eliminating the issues of security and content misuse by users. Study aims to select features for audio content consumption via streaming platforms in relation to diversity in content formats particularly studying the intention to adopt premium (paid) versions of an audio content in streaming service and recommend them. Measuring diversity of content format yields some issues. Traditional tools are not effective as power irregularities cohabit the birth of new dominant players. Results shows the developed system is able to recognize the various content diversity, stream the audio content for the user and ensure security of the audio content. [ABSTRACT FROM AUTHOR]

With an upsurge of data by global brands to interact/reach prospective clients, the birth of the Internet has today bridged the information gap. Virtualization techniques are today utilized as means to bridge the various lapses in our human processing endeavors. The adoption of tech to perform a variety of functions has since become imperative to ease our daily living as well as seamlessly allow transformations of various kinds to be impacted on our society. Study proposes a virtual key card access with cost-effective and cheap solution for managing access to areas within a facility. We have successfully integrated IoTs, virtual key card access, web-access control, solenoid lock integration, and ESP32-controller to create a comprehensive access control system. Its benefits over traditional key includes better security, user data privacy, system efficiency, and user convenience. The system also provides real-time monitor and control capabilities that will allow administrators to track and manage access to the facility remotely. And in turn, enhancing system's security and efficiency. [ABSTRACT FROM AUTHOR]

Recently, graph neural networks (GNNs) have been widely used for e-commerce review fraud detection by aggregating the neighborhood information of nodes in various relationships to highlight the suspiciousness of nodes. However, existing GNN-based detection methods are susceptible to sample class imbalance and fraud camouflage problems, resulting in poor quality of constructed graph structures and inability to learn reliable node embeddings. To address the above problems, we propose a novel e-commerce review fraud detection method based on self-paced graph contrast learning (SPCL-GNN). Firstly, the method constructs a subgraph by initially selecting nodes through a labeled balanced extractor. Secondly, the subgraph connections are filtered and complemented by combining self-paced graph contrast learning and an adaptive neighbor sampler to obtain an optimized graph structure. Again, an attention mechanism is introduced in intra- and inter-relationship aggregation to focus on the importance of aggregation under different relationships. Finally, the quality of the node embedding representation is further improved by maximizing the mutual information between the local and global representations. Experimental results on the Amazon and YelpChi datasets show that SPCL-GNN significantly outperforms the baseline. [ABSTRACT FROM AUTHOR]

Batteryless, self-sustaining embedded sensing devices are key enablers for scalable and long-term operations of Internet of Things (IoT) applications. While advancements in both energy harvesting and intermittent computing have helped pave the way for building such batteryless IoT devices, a present challenge is a system design that can utilize intermittent energy to meet data requirements from IoT applications. In this paper, we take the requirement of periodic data sensing and describe the hardware and software of a batteryless IoT device with its model, design, implementation, and evaluation. A key finding is that, by estimating the non-linear hardware charging and discharging time, the device software can make scheduling decisions that both maintain the selected sensing period and improve transmission goodput. A hardware–software prototype was implemented using an MSP430 development board and LoRa radio communication technology. The proposed design was empirically compared with one that does not consider the non-linear hardware characteristics. The result of the experiments illustrated the nuances of the batteryless device design and implementation, and it demonstrated that the proposed design can cover a wider range of feasible sensing rates, which reduces the restriction on this parameter choice. It was further demonstrated that, under an intermittent supply of power, the proposed design could still keep the device functioning as required. [ABSTRACT FROM AUTHOR]

This article describes constructing an embedded system for a painting art and style presentation platform, achieving the automatic integration of digital painting art with traditional art design. The frontend components are designed using the Bootstrap framework, with Django as the web development framework and TensorFlow architecture integrated into the code. Furthermore, the Inception module and residual connections are introduced to optimize the visual geometry group (VGG) network for recognizing and analyzing image texture features. Compared to other models, experimental results indicate that the proposed model demonstrates a 2.6% increase in image style classification accuracy, reaching 87.34% and 95.33% in architectural and landscape image classification, respectively. The system’s operational outcomes reveal that the proposed platform alleviates the burden on the logical function modules of the system, enhances scalability, and promotes the automated fusion of digital painting art with traditional art design expression. [ABSTRACT FROM AUTHOR]

With the development of the embedded system and improvement of the requirements from customers, embedded GUI which was used to communicate between users and embedded system became to be a key in researching the embedded system. This article introduced what is embedded system and what is embedded GUI. It described the porting of an embedded GUI on ARM Linux platform, including the establishment of compile environment, configuring and modifying of compile options and setting of runtime parameters. Above all, this article introduces the development of Embedded Linux GUI, describes the GUI development environment of Qt/Embedded and Qt, one procedure of the app location development based on Qt/Embedded is described in detail. This system adopts the B/S mode and uses the ARM11 development board with S3C6410 as the core. Sensors are used to obtain information, and the obtained data is judged and analyzed. When the preset conditions are not met, an alarm function is triggered. At the same time, data transmission is carried out through the Zigbee wireless module, and the receiving end analyzes and displays the received data. Each device is connected to the Zigbee module, which distinguishes it by assigning different network addresses and constantly transmits the status of the monitoring area to the receiving end. The receiving end distinguishes the monitoring status of different locations based on the different network addresses, increasing mobility. [ABSTRACT FROM AUTHOR]

e present the implementation of the hardware ANS compressor in FPGAs. The main goal of the design was to propose a solution suitable to low-cost, low-energy embedded systems. We propose the streaming-rANS algorithm of the ANS family as a target for the implementation. Also, we propose a set of algorithm parameters that substantially reduce the use of FPGA resources, and we examine what is the influence of the chosen parameters on compression performance. Further, we compare our design to the lossless codecs found in literature, and to the streaming-rANS codecs with arbitrary parameters. [ABSTRACT FROM AUTHOR]

In this paper, we have researched implementing convolutional neural network (CNN) models for devices with limited resources, such as smartphones and embedded computers. To optimize the number of parameters of these models, we studied various popular methods that would allow them to operate more efficiently. Specifically, our research focused on the ResNet-101 and VGG-19 architectures, which we modified using techniques specific to model optimization. We aimed to determine which approach would work best for particular requirements for a maximum accepted accuracy drop. Our contribution lies in the comprehensive ablation study, which presents the impact of different approaches on the final results, specifically in terms of reducing model parameters, FLOPS, and the potential decline in accuracy. We explored the feasibility of implementing architecture compression methods that can influence the model’s structure. Additionally, we delved into post-training methods, such as pruning and quantization, at various model sparsity levels. This study builds upon our prior research to provide a more comprehensive understanding of the subject matter at hand. [ABSTRACT FROM AUTHOR]

Embedded systems find extensive applications in various aspects of daily life and their presence in the highly connected world of today is growing. With a large number of remote devices and sensors, there is a growing need for remote data processing. Microcontrollers, often integral components of such devices, can enable remote data manipulation through the implementation of a web server called embedded web servers. The implementation of a server on such a system poses challenges, such as limited resources, so it is crucial for the web server implementation to be compatible with the available resources. This paper focuses on the requirements for the implementation of a web server. Also, an embedded web server has been designed for specific application of monitoring air parameters. The implementation utilizes the LwIP stack and allows for remote access to measurement results within a local network. The paper will delve into the operational principles of both the web server and the entire system. [ABSTRACT FROM AUTHOR]

In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system. [ABSTRACT FROM AUTHOR]

In recent years, the concept of the Internet of Things (IoT), its different models and emerging conceptual frameworks, have offered new opportunities for the integration of technologies into everyday life. Although initially based on the notion of the link between a physical device, its counterpart in a virtual environment and the technological aspects of that relationship, IoT-based wearable devices now offer multiple contributions to all types of users, from professionals and organizations to ordinary people. However, there are inaccuracies, abundant definitions, limited clarity in terms of trends, benefits, components, challenges and future lines of research. In this sense, a mixed literature review study was conducted. The mixed design, of dominant type (CUAN-qual), was composed of a quantitative analysis of bibliometric type and a qualitative analysis of thematic type and embedded in the first one. Data are provided on the main trends observed in the bibliometric indicators studied, a synthesis of some qualitative indicators of particular relevance and pertinence. In addition, future lines of research are presented to address strengths and challenges in this field. [ABSTRACT FROM AUTHOR]

The bogies of railway cars are important components of the rolling stock system. The test stand solution that we have developed and proposed is used for testing car bogies under load to inspect and highlight the features that ensure operational safety and energy efficiency. This article presents the development and manufacturing of a prototype test stand for railcar bogies, incorporating specific elements such as hydraulic pressure cylinders, force transducers, displacement transducers, a hydraulic pumping system, a data computation and processing system, as well as a control and command unit. The technical topic of developing the test stand is aimed at testing bogies under a controlled, variable and uniformly distributed load, closely simulating the operational conditions of railcar bogies. The accurate and complete determination of the bogie's parameters assures the railroad operator that they are safely interchangeable—Moreover, decreasing the time spent on testing tasks and increasing the testing process efficiency through process automation is leading to a cutback in energy consumption and better predictability of the time intervals required for the repair of railway cars. The prototype consists of three hydraulic pressure cylinders: a centrally located hydraulic cylinder exerting a maximum pressing force of 20 tons-force and two lateral hydraulic cylinders, each applying a maximum pressing force of 10 tons-force. The hydraulic cylinders operate independently from each other, also supporting asymmetric load distributions that recreate the operating conditions as faithfully as possible. The data processing system monitors the magnitudes of the forces and the displacement of the hydraulic cylinders for reporting the specific characteristics of the bogie. To ensure rapid response in the control of the testing process, a control logic unit was developed, entirely dedicated to the control of this technological process. The graphical user interface of the embedded computer system was designed in the LabVIEW programming environment. [ABSTRACT FROM AUTHOR]

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Embedded systems have attracted more attention and have become more critical due to the recent computer technology advancements and applications in various areas, such as healthcare, transportation, and manufacturing. Traditional software design approaches and the finite state machine cannot provide sufficient support due to two major reasons: the increasing need for more functions in designing an embedded system and sequential controls in the implementation. This deficiency particularly discourages inexperienced engineers who use conventional methods to design embedded software. Hence, we proposed a design method, the Synthesis-Based Stateful Software Design Approach (SSSDA), which synthesizes two existing methods, the Synthesis-Based Software Design Framework (SSDF) and Process and Artifact State Transition Abstraction (PASTA), to remedy the drawback of conventional methods. To show how to conduct our proposed design approach and investigate how it supports embedded system design, we studied an industrial project developed by a sophomore student team. Our results showed that our proposed approach could significantly help students lay out modules, improve testability, and reduce defects. [ABSTRACT FROM AUTHOR]

The control flow of a program represents valuable and sensitive information; in embedded systems, this information can take on even greater value as the resources, control flow, and execution of the system have more constraints and functional implications than modern desktop environments. Early works have demonstrated the possibility of recovering such control flow through power-based side-channel attacks in tightly constrained environments; however, they relied on meaningful differences in computational states or data dependency to distinguish between states in a state machine. This work applies more advanced machine learning techniques to state machines which perform identical operations in all branches of control flow. Complete control flow is recovered with 99% accuracy even in situations where 97% of work is outside of the control flow structures. This work demonstrates the efficacy of these approaches for recovering control flow information; continues developing available knowledge about power-based attacks on program control flow; and examines the applicability of multiple standard machine learning models to the problem of classification over power-based side-channel information. [ABSTRACT FROM AUTHOR]

The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgery‐prone on‐farm sorting and grading activities may lead to musculoskeletal disorders. The main objective of this study was to develop an automatic farm‐friendly machine for real‐time citrus fruit washing, image‐based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady‐state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into "accept" and "reject" classes. The custom lightweight CNN model "SortNet" has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation. [ABSTRACT FROM AUTHOR]

Filters are the crucial element of many modern electronic systems used in control engineering, wireless communication systems, and embedded systems designs. Filters in one form or another, are considered an important subsystem in developing communication systems. The ideal filter cannot be designed through any electronic circuit and the only way is to realize using simulations tools like MATLAB. In this paper, the simulation and analysis of the ideal low pass filter is presented. The MATLAB software is used to generate and perform analysis of the step response of ideal low-pass filter (Si function) and impulse response of ideal low-pass filter (Sinc function) in time and frequency domains. The aim of the work is to provide engineering students an easier understanding of the filter design process in an appropriate way with the help of simple programming. Where, it is easy to generate signals and analyze the ideal low pass filter response. To verify achievement of learning objectives, a survey is conducted from 45 engineering students of electronic and telecommunication which elicited students' observations and interest in the learning process. Overall about 75% of students gave positive feedback, which indicates that such type of experimentation had a significant impact on the student academic outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi‐case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software‐intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high‐level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case. [ABSTRACT FROM AUTHOR]

In order to explore the mapping recognition of arc welding molten pool characterisation and penetration state, according to the idea of embedded system construction, this article adopts the idea of software and hardware co‐design to find the zero‐crossing point of the second derivative in welding image edge detection, and give a threshold. When the absolute value of the first‐order derivative exceeds the threshold and has a different sign with the first‐order derivative of the previous edge, it is judged as a valid edge. The welding current adopts a symmetrical pulsed AC square wave, and the proportion of heat flow input is high. At the base current, the arc light is darker, so a clear image is obtained. This article designs a simulation experiment to verify the effect of the embedded system in this article. From the experimental research, it can be known that the embedded system constructed in this article can play a certain role in the mapping recognition of the arc welding molten pool characterisation and penetration state. [ABSTRACT FROM AUTHOR]

There is a growing interest in developing Computer-Based Assistive Technology (CAT) systems able to help the Visually Impaired (VI) in their daily needs and integrate well within society. One aspect that has not been well addressed is helping the visually impaired is the identification of colors for daily activities. Color recognition and perception is very important in interacting with the society and the surrounding environment. This paper presents a proof-of-concept design of a real-time embedded system that can help the visually impaired recognize colors, interact, and take decisions based on their perception of colors. Our approach is based on conveying color information, from the full color space, using a unique set of synthesized sound signals. The hardware part of the system is a pen-like device, which can detect color and generate a language-independent auditory signal representing the HSV values of the identified color. Numerous experiments have been performed using the new system with both the visually impaired and some blindfolded (BLD) participants. The system was proven to be very efficient in relation to training time and leads to high accuracy in color detection, classification, and matching tests. These experiments confirmed that the developed sonification scheme is effective yet simple in achieving color perception for the visually impaired. The proof-of-concept achieves about 93 % recognition accuracy using off-the-shelf components, it is cheap to implement, robust, and requires a much shorter time for training when compared to existing systems. [ABSTRACT FROM AUTHOR]

It is suggested that the use an ensemble of deep neural networks can determine the spatial position of the operator using keypoints with a multicamera sensor system. The advantage of the algorithm is the use of a multicamera system that allows keypoints to be linked to the local coordinate system of an industrial robotic complex. The testing of this work was made on the basis of modern embedded computing hardware and software. The effectiveness of the proposed approach is demonstrated even when only a subset of key points is found in the frame, as well as when they partially overlap. A software module in Python has been developed for detecting and localizing key points of the operator and industrial manipulator. The proposed approach will make it possible to plan the robot's trajectories for the safe execution of joint operations in one workspace. The developed algorithm will be used to predict the operator's actions in the workspace and detect abnormal situations and possible intersections in the trajectories of the collaborative robot. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have been widely deployed in the fields of artificial intelligence and computer vision. In these applications, the CNN part is the most computationally intensive. When these applications are run in an embedded device, the embedded processor can hardly handle the processing. This paper implements loop tiling to explain how one can construct a lightweight, low-power, and efficient CNN hardware accelerator for embedded computing devices. This method breaks a large CNN engine into small CNN engines and calculates them by low hardware resources. Finally, the results of small CNN engines are added and concatenated to construct the large CNN output. Using this method, a small accelerator can be configured to run a wide range of large CNNs. A small accelerator with one layer is designed to evaluate our methodology. Our initial investigations show that based on our methodology, the constructed accelerator can run a modified version of MobileNetV1, 70 times per second. [ABSTRACT FROM AUTHOR]

Nowadays, the complexity of embedded computing systems is increasing due to applications with increasing functional and nonfunctional requirements, e.g., performance, reliability, and energy efficiency. Organic computing has emerged as a research area for handling complex distributed embedded computer systems and is inspired by the organization of biological entities. It provides improved adaptability, robustness and has reduced development efforts. However, organic computing has drawbacks in terms of determinism, composability, and dependability, which are key features for safety-critical applications. Distributed computing systems using time-triggered communication networks have these characteristics and therefore offer distinct advantages for safety-critical systems. By combining organic computing, including artificial DNA and hormone features, with time-triggered communications, we can make these systems more safe, reliable, and suitable for safety-critical applications. Therefore, in this paper, we present a time-triggered organic computing architecture in combination with a simulation tool, in which tasks of an application are executed according to a predefined schedule. Our paper introduces multiple use cases to demonstrate the concepts and the system model. The obtained results from simulation show improved temporal predictability and fault containment of the system. [ABSTRACT FROM AUTHOR]

This article discusses an experimental seeing machine and its spatial, sensorial, and technical implications. By examining this real-time motion tracking and visualization system and reflecting on how it engendered embodied experiences of sharing movement across physical and virtual bodily spheres, the author raises questions about the construction of identity, visibility regimes, technical glitches, indeterminacy, and error. The mediating space, emerging through the coexistence of and interference between these distinct technologies/bodies brought together in common points of view, also suggests another mode of perception, perhaps unsettling what it means to "see." [ABSTRACT FROM AUTHOR]