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Increasingly, durable goods have embedded software and are technologically tethered to the vendor's server through this software, be it digital products such as smartphones or predominantly non-digital products such as automobiles and tractors. Tethering can enable the vendor to change the product's performance or features through software updates, with implications for optimal product policy. We study a monopolist technology vendor's decision to partially degrade or fully retire their installed base of tethered goods when they release their newer version. Our work presents several novel theoretical contributions with practical implications for industry. Installed base degradation can emerge in equilibrium. To obsolete the previous version, it is no longer necessary for the seller to produce an improved version. Firms may engage in economically suboptimal partial degradation for technical reasons. Firms sometimes engage in suboptimal product obsolescence despite it harming their profit, due to their inability to commit to a no-obsolescence policy. Under some conditions the seller's commitment problem could cause them to offer excessive durability. Our work suggests important policy guidelines: when releasing new models, firms should consider releasing updated versions of their older operating systems, and allow existing customers to opt out of significant software upgrades, other than essential security patches. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of the electric tricycle is to bring increased mobility to impaired persons. Presently, hand-powered tricycles are used by numerous members of the impaired community, but some current users of hand-powered tricycles do not have the physical strength or collaboration to propel themselves on the tricycle with their arms and hands. The aim of the proposed paper is to add electric power to the current hand-powered tricycle to provide tricycle users with improved mobility, providing them with more freedom and making a donation to the community. This paper develops an inclusive and cost-effective electric tricycle designed specifically for individuals with mobility challenges. The proposed tricycle is equipped with a 350-watt motor and has a cargo capacity of over 100 kg. Using IoT technology, the proposed system includes features similar to real-time position shadowing, on/off announcements through mobile and dispatch, and clear on/off suggestions. This innovative result addresses the unique requirements of hindered individuals, promoting availability, autonomy, and enhanced mobility in a socially conscious manner. [ABSTRACT FROM AUTHOR]

The cybertronics challenges evolve around the complexity of systems and increase in detail and data involved. Existing methods and tools can master some of the rising challenges, but a method being able to handle the challenges was not introduced yet. MBSE is a potential approach to solve these problems, but the available methods were developed for modeling physical systems and software, not cybertronics. Because of the diversity of implementation domains participating in cybertronics systems, the modeling methodology must support the holistic management of requirements, properties, and design data. This paper introduces some promising emerging technologies like Arcadia, SysML V2, SysMD and PMM, that have the potential to interact with other methods and tools, closing the gaps in requirements tracing, system modeling, documentation, and verification. One potential methodology is introduced through a design example from requirements to implementation interface. [ABSTRACT FROM AUTHOR]

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is the management of production facilities by integrating software production lines, continuous integration, and continuous delivery/continuous deployment (CI/CD). All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as has the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research examines numerous datasets from the United States of America and Washington State, based on which we estimate possible future changes in the automotive industry's sales. [ABSTRACT FROM AUTHOR]

Machine learning models play a crucial role in sports monitoring by effectively identifying various activities and tracking the number of repetitions during repetitive movements. However, creating models that accurately detect different types of exercises and provide feedback on movement adjustments for wearable devices remains a challenge. In this letter, we propose an alternative approach that addresses this issue by using the restricted Boltzmann machine (RBM) algorithm to learn, evaluate, and provide adjustment feedback based on inertial sensor data in real-time. Our experimental results show that by evaluating body segments individually, highly specialized models can be generated from a small set of movement repetitions. Moreover, these models have the capability to offer users precise recommendations on how to fine-tune the intensity, acceleration, and amplitude of the monitored segment. By using our proposed method, there is a great potential to enhance the accuracy and effectiveness of wearable devices used for sports monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern means of information exchange are mostly built as systems with. The process of functioning of such systems consists in the interaction of software and hardware components. The architecture of such systems corresponds to the Open System Interconnection Basic Reference Model (OSI), which defines certain levels in networks, gives them standard names and indicates what functions each level should perform. Violations of the functioning of such systems can be caused by random failures and defects of the hardware component, errors and failures of the software, or as a result of external influence. The complexity of assessing the technical condition (technical diagnosis) is due to the significant territorial diversity of the system elements, the variety of functional nodes and specifications of the equipment, as well as the use of dual-purpose equipment in the systems. Given the complexity of the selected object, the task of automating control is entrusted to computer-measurement systems. Analysis of the architecture of the selected object of control (availability of formalized - standardized levels) allows to offer solutions to technical diagnostics tasks (or physical integrity control tasks) through step-by-step (step-by-step) testing of individual levels of hardware and technical means. The presented verification technique is based on organizational measures (availability of daily monitoring of functioning) and the use of standardized sets of protocols of each level of the OSI model. At the same time, at each stage of the check, two components of the diagnostic parameter (time and energy) are registered. The availability of mathematical models of the aging processes of the hardware part and the physical data transmission environment made it possible to obtain reference values for the diagnostic parameter at any time of the operation of the control object, or to determine the change of the main parameters when modeling the behavior of the system for a certain time. The set of numerical values of both components of the diagnostic parameter allows to determine the technical condition of not only the hardware part of the control object and the correct functioning of the software part at different levels of the OSI model, but also the presence (or absence) of unauthorized influence (software or hardware) in the system. [ABSTRACT FROM AUTHOR]

In real-time systems, it is essential to verify the end-to-end constraints that regulate the external input/output (I/O) semantics of the head and tail tasks in each effect chain during the design phase and preserve them during implementation. The logical execution time (LET) model has been adopted by the industry due to the predictability and composability of its timed behavior. However, during the execution of LET-based effect chains, there are ineffective jobs whose outputs are redundant or unused and do not contribute to the external I/O behavior. This letter proposes an offline optimization method for deriving multiframe tasks that achieve the external timed I/O semantics of the LET-based effect chains with reduced utilization. The method first removes ineffective jobs from each effect chain and further explores the benefits of removing jobs for single and crossing effect chains by loosening the LET interval. The method is evaluated using synthetic benchmarks that mimic real-world automotive applications. [ABSTRACT FROM AUTHOR]

Typical training procedures involve read and write operations for weight updates during backpropagation. However, on-device training on microcontroller units (MCUs) presents two challenges. First, the on-chip SRAM has insufficient capacity to store the weight. Second, the large flash memory, which has a constraint on write access, becomes necessary to accommodate the network for on-device training on MCUs. To tackle these memory constraints, we propose a partial weight update technique based on gradient delta computation. The weights are stored in flash memory, and a part of the weight to be updated is selectively copied to the SRAM from the flash memory. We implemented this approach for training a fully connected network on an on-device MNIST digit classification task using only 20-kB SRAM and 1912-kB flash memory on an MCU. The proposed technique achieves reasonable accuracy with only 18.52% partial weight updates, which is comparable to state-of-the-art results. Furthermore, we achieved a reduction of up to 46.9% in the area-power-delay product compared to a commercially available high-performance MCU capable of embedding the entire model parameter, taking into account the area scale factor. [ABSTRACT FROM AUTHOR]

There are various tools for real-time data acquisition and analysis of high-frequency signals. Usually, the tools for data acquisition and analysis are incompatible with each other, expensive, or require the utilization of various technologies. Designing a unique toolset for real-time data acquisition and analysis of high-frequency signals presents a large challenge, both in terms of hardware and software design. Some of the main challenges include fulfilling strict real-time performance requirements, using cost-effective hardware components, and designing a software architecture with minimal software latency for data sampling on an embedded platform and data analysis on a PC machine. This paper presents a software architecture design methodology for embedded platforms, including an RTOS and an Ethernet interface, along with the application architecture on the host side. To demonstrate the advantages of the proposed methodology, a prototype consisting of the STM32H747 microcontroller, FreeRTOS operating system, and a GUI application based on the Qt framework has been created. It has been shown that using the proposed architecture design methodology makes it possible to preserve the maximal hardware sampling rate utilizing a 100 Mbps Ethernet link. In the experiments, sampling rates up to 4.57 MSps for 10-bit resolution and real-time transfer of data over Ethernet link and visualization on host PC have been achieved with this specific MCU. [ABSTRACT FROM AUTHOR]

Scade is a domain-specific synchronous functional language used to implement safety-critical real-time software for more than twenty years. Two main approaches have been considered for its semantics: (i) an indirect collapsing semantics based on a source-to-source translation of high-level constructs into a data-flow core language whose semantics is precisely specified and is the entry for code generation; a relational synchronous semantics, akin to Esterel, that applies directly to the source. It defines what is a valid synchronous reaction but hides, on purpose, if a semantics exists, is unique and can be computed; hence, it is not executable. This paper presents, for the first time, an executable, state-based semantics for a language that has the key constructs of Scade all together, in particular the arbitrary combination of data-flow equations and hierarchical state machines. It can apply directly to the source language before static checks and compilation steps. It is constructive in the sense that the language in which the semantics is defined is a statically typed functional language with call-by-value and strong normalization, e.g., it is expressible in a proof-assistant where all functions terminate. It leads to a reference, purely functional, interpreter. This semantics is modular and can account for possible errors, allowing to establish what property is ensured by each static verification performed by the compiler. It also clarifies how causality is treated in Scade compared with Esterel. This semantics can serve as an oracle for compiler testing and validation; to prototype novel language constructs before they are implemented, to execute possibly unfinishedmodels or that are correct but rejected by the compiler; to prove the correctness of compilation steps. The semantics given in the paper is implemented as an interpreter in a purely functional style, in OCaml. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present WEARDA, the open source WEARable sensor Data Acquisition software package. WEARDA facilitates the acquisition of human activity data with smartwatches and is primarily aimed at researchers who require transparency, full control, and access to raw sensor data. It provides functionality to simultaneously record raw data from four sensors--tri-axis accelerometer, tri-axis gyroscope, barometer, and GPS--which should enable researchers to, for example, estimate energy expenditure and mine movement trajectories. A Samsung smartwatch running the Tizen OS was chosen because of 1) the required functionalities of the smartwatch software API, 2) the availability of software development tools and accessible documentation, 3) having the required sensors, and 4) the requirements on case design for acceptance by the target user group. WEARDA addresses five practical challenges concerning preparation, measurement, logistics, privacy preservation, and reproducibility to ensure efficient and errorless data collection. The software package was initially created for the project "Dementia back at the heart of the community",2 and has been successfully used in that context. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices are mostly ubiquitous in this day and age, and it is hard to imagine a life without them, especially in the productive sectors (industry, agriculture, and automotive) and in our daily life activities (consumer electronics, home automation, and intelligent buildings). The high demand for these devices has created significant competition to provide them at the best price, at the right time, and with the best features. The trend in which these devices have increased their product features has resulted in their embedded software being more complex, leading to extended development and testing times. Consequently, as the types of advanced IoT products keep diversifying, the field maintenance of all the different models deployed grows more complicated. This paper proposes an embedded software development framework for IoT devices independent of the microcontroller architecture, the compiler, and the development environment. This framework allows having a common software baseline between different projects, which shortens the learning curve, development time, and module validation while allowing code reuse for embedded software professionals. A proof-of-concept evaluation is also presented to demonstrate the efficiency and reliability of the obtained embedded software code for a simple but representative IoT application. [ABSTRACT FROM AUTHOR]

Energy harvesting is increasingly used as a long-term energy supply for the Internet of Things, wireless sensor networks, and cyber-physical systems. However, the challenge of mitigating the variability of energy harvesting sources needs to be addressed before ubiquitous adoption can happen. Otherwise, an unreliable operation of devices with frequent shutdowns during times of energy scarcity would be encountered. One finds probabilistic performance metrics helpful in designing power management solutions for the long-term operation of energy harvesting nodes. These metrics include the probability of battery depletion, expected energy consumption, expected battery level, and similar. This article proposes a stochastic modeling technique and corresponding analysis which can provide such metrics. Our advanced analysis is based on Markov chains. By modeling harvested energy with random variables, new and existing energy management policies are analyzed and compared. We propose an adaptive energy management strategy inspired by mixed-criticality systems. In the proposed strategy, the system can degrade or drop less essential tasks in real time to ensure a graceful degradation of service in adverse harvesting conditions. We compare the proposed energy management approach to several existing alternatives. To this end, we conduct extensive simulations for indoor and outdoor environments, where our strategy matches or outperforms the state of the art. Initial results also validate the high precision of our stochastic model and analysis in comparison to simulations using real-world data. [ABSTRACT FROM AUTHOR]

Simulink is a widely used model-driven design environment for supporting the simulation and code generation of embedded applications. To improve the quality of the code generated from Simulink models, state-of-the-art code generators employ various high-level optimizations, like eliminating local variables. However, they overlook the compatibility between code and the low-level processor architecture, especially the instruction pipeline. Consequently, instruction pipeline stalls occur frequently, leading to additional delays in instruction execution, as well as limited efficiency for deployed the embedded software. In this article, we propose Mercury, an instruction pipeline aware code generator for Simulink models which utilizes data dependencies between actors to decrease the instruction pipeline stalls of the generated code. First, Mercury collects data dependencies through model dataflow traversal and records the property of each actor. Then, Mercury approximately estimates the execution latency of required instructions fetched from corresponding actors and uses a topology-based method to obtain candidate actors for code synthesis. Finally, Mercury adopts the least penalty priority to iteratively select the most suitable actor for code synthesis and releases data dependencies with its subsequent actors. We implemented and evaluated Mercury on benchmark Simulink models (Su et al., 2021) as well as a real industrial model. Compared to the official tool Simulink Embedded Coder and the state-of-the-art academic tool DFSynth, Mercury outperformed them by 9.7%–33.4% and 9.2%–59.4% in terms of the execution time of the generated code across different architectures, respectively. The statistics also demonstrate that the generated code of Mercury increases utilization of pipeline slots by 11.0%–37.1% and 10.6%–50.0%, respectively. [ABSTRACT FROM AUTHOR]

Recent research showcased several cyber-attacks against unmodified licensed vehicles, demonstrating the vulnerability of their internal networks. Many solutions have already been proposed by industry and academia, aiming to detect and prevent cyber-attacks targeting in-vehicle networks. The majority of these proposals borrow security algorithms and techniques from the classical ICT domain, and in many cases they do not consider the inherent limitations of legacy automotive protocols and resource-constrained microcontrollers. This paper proposes DAGA, an anomaly detection algorithm for in-vehicle networks exploiting $n-$ gram analysis. DAGA only uses sequences of CAN message IDs for the definition of the $n-$ grams used in the detection process, without requiring the content of the payload or other CAN message fields. The DAGA framework allows the creation of detection models characterized by different memory footprints, allowing their deployment on microcontrollers with different hardware constraints. Experimental results based on three prototype implementations of DAGA showcase the trade off between hardware requirements and detection performance. DAGA outperforms the state-of-the-art detectors on the most performing microcontrollers, and can execute with lower performance on simple microcontrollers that cannot support the vast majority of IDS approaches proposed in literature. As additional contributions, we publicly release the full dataset and our reference DAGA implementations. [ABSTRACT FROM AUTHOR]

In recent years, since edge computing has become more and more popular, its security issues have become apparent and have received unprecedented attention. Thus, the current research concentrates on security not only regarding devices such as PCs, smartphones, tablets, and IoTs, but also the automobile industry. However, since attack vectors have become more sophisticated than ever, we cannot just protect the zone above the system software layer in a certain operating system, such as Linux, for example. In addition, the challenges in IoT devices, such as power consumption, performance efficiency, and authentication management, still need to be solved. Since most IoT devices are controlled remotely, the security regarding system maintenance and upgrades has become a big issue. Therefore, a mechanism that can maintain IoT devices within a trusted environment based on localhost or over-the-air (OTA) will be a viable solution. We propose a mechanism called STBEAT, integrating an open-source project with ARM TrustZone to solve the challenges of upgrading the IoT system and updating system files more safely. This paper focuses on the ARMv7 architecture and utilizes the security stack from TrustZone to OP-TEE under the STM32 board package, and finally obtains the security key from the trusted application, which is used to conduct the cryptographic operations and then install the newer image on the MMC interface. To sum up, we propose a novel software update strategy and integrated ARM TrustZone security extension to beef up the embedded ecosystem. [ABSTRACT FROM AUTHOR]

LiDAR sensors are extensively used in autonomous vehicles and their optimal use is a critical concern. In this paper, we propose an embedded software architecture for multiple LiDAR sensors, i.e., a fusion system that acts as an embedded system for processing data from multiple LiDAR sensors. The fusion system software comprises multiple clients and a single server. The client and server are connected through inter-process communication. Multiple clients create processes to process the data from each LiDAR sensor via a multiprocessing method. Our approach involves a scheduling method for efficient multiprocessing. The server uses multithreading to optimize the internal functions. For internal communication within the fusion system, multiple clients and a single server are connected using the socket method. In sequential processing, the response time increases in proportion to the number of connected LiDAR sensors. By contrast, in the proposed software architecture, the response time decreases in inverse proportion to the number of LiDAR sensors. As LiDAR sensors become increasingly popular in the field of autonomous driving, the results of this study can be expected to make a substantial contribution to technology development in this domain. [ABSTRACT FROM AUTHOR]

Trends in the automotive industry confirm that the demand for testing of embedded systems, especially advanced driver assistance systems (ADAS), will grow dramatically in the near future. This paper proposes a new solution that automates the detection of software defects in embedded systems. The solution consists of a data-driven sampling algorithm to intelligently sample the testing space by sequentially generating test cases. Moreover, it segregates different defects from each other and identifies the signals that trigger each. The results are compared against other automated methods for defect identification and analysis, and it is found that this novel solution is able to identify defects more rapidly. In addition, it correctly separates defects and reliably reproduces each distinct defect. [ABSTRACT FROM AUTHOR]

Face recognition payment is a new type of payment method, with AI face recognition technology as the core, and its speed and convenience are more in line with the users' payment habits. However, the face is a biological feature with weak privacy, and the protection of user information security is particularly important. At present, face payment technology still has security risks, and the data transmitted during the transaction process are vulnerable to attacks. Aiming at the security problems in the payment process, a payment system that is jointly encrypted by the SM4 algorithm and the face liveness detection algorithm was proposed in this paper, which supports a variety of communication methods. The hardware platform adopts an octa-core 64-bit ARM processor with a main frequency of 1.8 GHz, which has powerful computing and processing capabilities. Based on the Android intelligent operating system, the development environment is more secure and convenient. It is also equipped with a liveness detection 3D structured light camera, which dynamically collects face information and accurately analyzes the characteristics of living bodies. Through the data encryption and decryption test and face performance index detection, the expected effect of the system was achieved, which greatly improved the performance of the face payment system currently studied. The SM4 encryption algorithm improved the running rate of encrypted data and the security of face transaction data transmission, the face detection algorithm improved the accuracy of living body feature recognition, and the payment system effectively improved the accuracy and security of face payment. [ABSTRACT FROM AUTHOR]

Devices that populate the Internet of Things (IoT) are typically constrained with respect to energy consumption. When the data that are processed, stored and/or communicated by these devices need to be secured, low-energy security mechanisms have to be designed and implemented. Related work mainly concentrates either on low-energy security algorithms and protocols, or on low-energy wireless communication. However, it is important for system developers to take into account the overall energy consumption of the IoT system when making design choices. Therefore, this work presents an in-depth analysis of the energy consumption of IoT devices that provide end-to-end secure communication and digital signatures. The paper follows a granular approach, profiling and measuring each individual contribution to the overall energy consumption, including the computation of cryptographic operations as well as the wireless transmission of messages in cryptographic protocols. The paper also calculates the minimal time period of a secure communication session in order to minimize the energy impact of the session's setup phase and thus minimize the overall average power consumption. The goal of this work is to provide assistance in the selection of a suitable wireless communication standard and cryptographic cipher suite for building end-to-end secure IoT applications. [ABSTRACT FROM AUTHOR]

Many embedded systems operate under real-time conditions. For them, the worst case execution time (WCET) is a crucial information to check whether the software implementation meets the requirements. In modern microcontrollers, this WCET can often no longer be evaluated statically since these processors include too many unpredictable components (cache, bus arbitration, $\ldots$ ,). Here, hybrid WCET estimation comes into play, where measured traces are used to empirically compute bounds for the WCET. For meaningful estimations, it is required to put each measured segment into a corresponding execution context. In this contribution, we present a method that allows to gather such context-sensitive statistics online in hardware. This enables arbitrary long measurement intervals with a precision that compares to state-of-the-art offline tools. [ABSTRACT FROM AUTHOR]

In the past decade, Deep Neural Networks (DNNs), e.g., Convolutional Neural Networks, achieved human-level performance in vision tasks such as object classification and detection. However, DNNs are known to be computationally expensive and thus hard to be deployed in real-time and edge applications. Many previous works have focused on DNN model compression to obtain smaller parameter sizes and consequently, less computational cost. Such methods, however, often introduce noticeable accuracy degradation. In this work, we optimize a state-of-the-art DNN-based video detection framework—Deep Feature Flow (DFF) from the cloud end using three proposed ideas. First, we propose Asynchronous DFF (ADFF) to asynchronously execute the neural networks. Second, we propose a Video-based Dynamic Scheduling (VDS) method that decides the detection frequency based on the magnitude of movement between video frames. Last,we propose Spatial Sparsity Inference, which only performs the inference on part of the video frame and thus reduces the computation cost. According to our experimental results, ADFF can reduce the bottleneck latency from 89 to 19 ms. VDS increases the detection accuracy by 0.6% mAP without increasing computation cost. And SSI further saves 0.2 ms with a 0.6% mAP degradation of detection accuracy. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS), an emerging area of digital advancement in shipping and shipbuilding industries, presents a different legal paradigm from that of existing ships. Existing maritime-related industries, including shipping, shipbuilding, and logistics, based on large hardware called ships, are rapidly changing into highly autonomous software-centered structures. This study is focused on analyzing the legal issues in preparation for MASS' commercial operations in the future by applying comparative methods centered on the Republic of Korea and the United Kingdom. The study's results contribute to the criteria for the manufacturing responsibility of autonomous ship-embedded software and a desirable legal policy improvement plan. Various legal issues and macro legal policy directions related to software product liability were identified. The study also presents concrete implementation strategies to achieve an ideological harmony and a balance between equitable damage relief and the advancement of related technologies to ensure maritime safety in the maritime industry. Based on the issues identified and their legal policy alternatives, it is hoped that the institutional ideal of product liability, which promotes technological advancement and protects consumer rights, is realized in the software domain as well. [ABSTRACT FROM AUTHOR]

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details. [ABSTRACT FROM AUTHOR]

Transiently-powered devices rely solely on energy harvesters. Such devices typically use capacitors to store the harvested energy, but recent systems employ supercapacitors to store energy for extended operations. In a supercapacitor-based transiently-powered system, the energy-efficient estimation of the state-of-charge (SoC) of a supercapacitor is critical for practical use of the system, due to its tight energy budget. Conventional voltage-based schemes for capacitors do not provide accuracy in SoC estimation for supercapacitors. Also, the supercapacitor-specific SoC estimation which exploits its charge redistribution characteristics provides an accuracy, but the scheme demands significant overhead, and thus, is not applicable to transiently-powered systems, such as wireless sensor nodes. In this article, we specify three requirements for estimating supercapacitor SoC that should be met to function in transiently-powered systems. We then propose a scheme that meets those requirements. The proposed scheme does not require additional hardware, has a low computation cost, and operates when the system is intermittently powered. Thus, the scheme fits the energy-efficient operation of transiently-powered systems. We implemented the proposed scheme in real hardware and evaluated its functionality and accuracy. The proposed scheme estimated the SoC of supercapacitors with high accuracy, in various configurations and use scenarios, while guaranteeing operations in a typical energy harvesting environment. [ABSTRACT FROM AUTHOR]

The increasing complexity of embedded systems renders software verification more complex, requiring monitoring and formal techniques, like model-checking. However, to use such techniques, system engineers usually need formal expertise to express the software requirements in a formal language. To facilitate the use of model-checking tools by system engineers, our approach uses a UML model interpreter through which the software requirements can directly be expressed in UML as well. Formal requirements are encoded as UML state machines with the transition guards written in a specific observation language, which expresses predicates on the execution of the system model. Each such executable UML specification can model either a Büchi automaton or an observer automaton, and is synchronously composed with the system, to follow its execution during model-checking. Formal verification can continue at runtime for all deterministic observer automata used during offline verification by deploying them on real embedded systems. Our approach has been evaluated on multiple case studies and is illustrated, in this paper, through the user interface model of a cruise-control system. The automata-based verification results are in line with the verification of the equivalent LTL properties. The runtime overhead during monitoring is proportional to the number of monitors. [ABSTRACT FROM AUTHOR]

The China Seismo‐Electromagnetic Satellite (CSES) space mission—also known as Limadou in Italian—is a scientific collaboration between China and Italy that aims to investigate the structure and dynamics of the iono/magnetosphere, and in particular to study the possible correlation of perturbations to the occurrence of high‐magnitude seismic events. The Chinese satellite houses the Italian high‐energy particle detector (HEPD), an apparatus composed of a silicon tracker and a calorimeter system, which detects electrons and protons in the energy ranges 3–100 and 30–200 MeV, respectively. The satellite was launched on February 2, 2018; after a commissioning phase dedicated to the setting of an optimal configuration, HEPD is fully operational since July 28, 2018. After a general overview of the CSES‐Limadou mission, this article presents the structure of the HEPD apparatus and describes the design and implementation of the software for the embedded computer system that is responsible for HEPD management. The onboard software runs on the digital signal processors of two electronic boards (CPU and data acquisition), monitors system status, handles instrument data acquisition, performs periodic calibration of the subdetectors, performs data compression, and communicates with the satellite. An overview of HEPD in‐flight operations and performance is given in the last section. [ABSTRACT FROM AUTHOR]

A trend, accompanying the change of automation systems and their architectures, is the virtualization of software components. Virtualization strengthens platform-independent development and the provision of secure and isolated applications. Virtualization introduces well-defined interfaces to strengthen modularity, which facilitates the scalability of applications. However, virtualization includes additional software components and layers and, thus, additional computing costs. This additional effort can conflict with the real-time requirements of automation processes. Current research lacks the investigation of the time behavior of container-based virtualizations concerning their use in real-time systems. An assessment concerning real-time applications is required to prepare it for use in industrial automation. This article examines the effects of virtualization on the time delays of a software component based on Docker containers by providing measurements on a hardware testbed in a realistic use case. The experiments indicate that Docker virtualization can meet soft real-time requirements and can be used in industrial automation. [ABSTRACT FROM AUTHOR]

Programmable logic controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This article gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security. [ABSTRACT FROM AUTHOR]

Engineering of modern embedded systems requires complex technical, managerial and operational processes. To cope with the complexity, modeling is a commonly used approach in the embedded software industry. The modeling approaches in embedded software vary since the characteristics of modeling such as purpose, medium type and life cycle phase differ among systems and industrial sectors. The objective of this paper is to detail the use of a characterization model MAPforES ("Modeling Approach Patterns for Embedded Software"). This paper presents the results of applying MAPforES in multiple case studies. The applications are performed in three sectors of the embedded software industry: defense and aerospace, automotive and transportation, and consumer electronics. A series of both structured and semi-structured interviews with 35 embedded software professionals were conducted as part of the case studies. The characterization model was successfully applied to these cases. The results show that identifying individual patterns provides insight for improving both individual behavior and the behavior of projects and organizations. [ABSTRACT FROM AUTHOR]

Coroutines will be added to C++ as part of the C++20 standard. Coroutines provide native language support for asynchronous operations. This letter evaluates the C++ coroutine specification from the perspective of embedded systems developers. We find that the proposed language features are generally beneficial, but that memory management of the coroutine state needs to be improved. Our experiments on an ARM Cortex-M4 microcontroller evaluate the time and memory costs of coroutines in comparison with alternatives, and we show that context switching with coroutines is significantly faster than with thread-based real-time operating systems. Furthermore, we analyzed the impact of these language features on prototypical Internet of Things sensor software. We find that the proposed language enhancements potentially bring significant benefits to programming in C++ for embedded computers, but that the implementation imposes constraints that may prevent its widespread acceptance among the embedded development community. [ABSTRACT FROM AUTHOR]

Automated controller synthesis methods for cyber–physical systems (CPSs) often require precise knowledge of the system’s state. Unfortunately, parts of the state may not be directly measurable, which limits the application of these methods. We present a design methodology for the co-design of software controllers and the required sensing capabilities. Our method leverages the knowledge of physical units in the model of a system to find ways of indirectly measuring parts of the system’s state which cannot be measured directly. The method contains a search procedure which uses dimensional analysis to explore the space of physically well-typed expressions and it generates as an intermediate result possible sensor combinations. The integration between the physical and software design for CPS that we present make automated controller synthesis techniques more widely applicable. We have implemented our method and applied it to the design of robotic manipulators. [ABSTRACT FROM AUTHOR]

The strict requirements on the timing correctness biased the modeling and analysis of real-time systems toward the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this article, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The dynamic voltage and frequency scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including LO-criticality ones, under any execution condition. We present a response time analysis for such systems under the nonpreemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (with respect to energy savings) and it reports up to 46% energy-saving. [ABSTRACT FROM AUTHOR]

Much of the previous research has been done on distributed software transactional memories targeting data centers in Internet clouds, which resulted in nondeterministic and nonrealtime middleware solutions mainly written in Java and C++. On the other hand, embedded systems based on the Internet of Things at the edge of the Internet, such as smart homes, cars, etc., need to operate in realtime, and should, therefore, be deterministic. In order to be smart, these systems use machine learning, and nowadays Python is becoming a leading language in this venue, too. This is the first paper that presents a distributed software transactional memory that is at the same time: (i) fault tolerant, (ii) deterministic, (iii) based on Python, and (iv) extended from a formally verified root. The presented solution consists of a pair of master-slave transaction coordinators and a set of replicated data servers and targets small-to-medium edge networks. Besides intelligent embedded systems, based on the Internet of Things, it can be also used in a wide range of application domains, from SCADA systems to large-scale simulations. The experimental results, presented in the paper, show a superlinear growth of the system throughput as the workload changes from write-only to read-only. [ABSTRACT FROM AUTHOR]

Program understanding is a time-consuming and tedious activity for software developers. Manually building abstractions from source code requires in-depth analysis of the code. Automatic extraction of such models is possible, but cannot derive meaningful abstractions that are not already contained in the code. The automated extraction even has problems to decide which aspects of the code are important and which are not. Therefore, interactive semi-automatic approaches are the compromise of choice. In this article, we describe how state machines that describe the behaviour of a function can be extracted from code. The approach includes interaction – the user decides which aspects of the identified potentially relevant information is really relevant and which is not. This helps to reduce the resulting state machines to an understandable degree. However, these state machines in their raw form have transition conditions that are very complex and thus not understandable for humans. Therefore, we also introduce a technique to reduce these guards to an understandable form. The technique is a combination of heuristic logic minimization, exploitation of infeasible paths, and using transition priorities. We evaluate the approach on industrial embedded C code, first in a case study with hundreds of extracted state machines, and then in two experiments with professional developers. The results show that the approach is highly effective in making the guards understandable, and that guards reduced by our approach and presented with priorities are easier to understand than guards without priorities. We also show that the overall approach is beneficial for program comprehension. The guard reduction approach itself is quite generic and can also be applied to other problems. We demonstrate that for the simplification of mode switch logic. [ABSTRACT FROM AUTHOR]

In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem. [ABSTRACT FROM AUTHOR]

Objective: We designed and validated a wearable, multimodal sensor brace for knee joint health assessment. Methods: An embedded-, two-microcontroller-based approach is used to sample high-throughput, multi-microphone joint acoustics (46.875 kHz) as well as lower-rate electrical bioimpedance (EBI) (1/46.17 s), inertial (100 – 250 Hz), and skin temperature (1 Hz) data, and these data are saved onto microSD cards. Additionally, a flexible, 3D-printed brace houses the custom circuit boards and sensors to enable wearable sensing. Results: The system achieves 9 hours of continuous joint sound recording, while the EBI, inertial, and temperature sensors can sample for 35 hours using 500 mAh batteries. Further, for the entirety of these continuous recordings, there were no dropped samples for any of the sensors. Lastly, proof-of-concept measurements were used to show the system’s efficacy for recording joint sounds and swelling data. Conclusion: This is, to the best of our knowledge, the first, completely untethered wearable system for multimodal knee health monitoring. Significance: The proposed smart brace may facilitate in-clinic or at-home measurements for joint health assessment. [ABSTRACT FROM AUTHOR]

The efforts spent on the development of assistive technologies has led researches to explore many existing techniques as computer vision, image processing, etc. and apply them as embedded solutions to help people with several types of disabilities, including visual impairment. Embedded navigation systems for visually impaired people (VIP) often use RGB-D cameras to retrieve depth information from surroundings and present them as gray images with depth represented by gray level or black pixels if depth couldn't be estimated, which can be fixed by mathematical morphology. Morphological filters must be efficient to solve the problem and fast to avoid impact on performance. This paper presents an approach for automatic generation and optimization of low complexity and low error morphological filters to fix depth image's unknown distances based on NSGA-II and Cartesian Genetic Programming. Experiments were performed using two different error metrics and results showed that the presented approach managed to generate and optimize feasible morphological filters that fit within embedded navigation systems for VIP. [ABSTRACT FROM AUTHOR]

A keyword spotting algorithm implemented on an embedded system using a depthwise separable convolutional neural network classifier is reported. The proposed system was derived from a high-complexity system with the goal to reduce complexity and to increase efficiency. In order to meet the requirements set by hardware resource constraints, a limited hyper-parameter grid search was performed, which showed that network complexity could be drastically reduced with little effect on classification accuracy. It was furthermore found that quantization of pre-trained networks using mixed and dynamic fixed point principles could reduce the memory footprint and computational requirements without lowering classification accuracy. Data augmentation techniques were used to increase network robustness in unseen acoustic conditions by mixing training data with realistic noise recordings. Finally, the system's ability to detect keywords in a continuous audio stream was successfully demonstrated. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. [ABSTRACT FROM AUTHOR]

This article explores the design space of secure communication in ultra-low-energy IoT devices based on Micro-Controller Units (MCUs). It tries to identify, benchmark, and compare security-related design choices in a Commercial-Off-The-Shelf (COTS) embedded IoT system which contributes to the energy consumption. We conduct a study over a large group of software crypto algorithms: symmetric, stream, hash, AEAD, MAC, digital signature, and key exchange. A comprehensive report of the targeted optimization attributes (memory, performance, and specifically energy) will be presented from over 450 experiments and 170 different crypto source codes. The article also briefly explores a few system-related choices which can affect the energy consumption of secure communication, namely, architecture choice, communication bandwidth, signal strength, and processor frequency. In the end, the article gives an overview of the obtained results and the contribution of all. Finally, it shows, in a case study, how the results could be utilized to have a secure communication in an exemplary IoT device. This article gives IoT designers insight into ultra-low-energy security, helps them to choose appropriate cryptographic algorithms, reduce trial-and-error of alternatives, save effort, and hence cut the design costs. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects many tiny low-cost devices, so-called IoT nodes. Designers need to implement smart functionality with highly limited resources in terms of design effort, available on-chip memory, and computation power. This letter proposes a new method to reduce memory size, performance, and development effort for the device drivers. The driver behavior is developed with a new $C$ -like domain-specific language. Through register layout optimization targeting to combine accesses it achieves to reduce run time by 52% and code size by 22% for two RISC-V PULPino device drivers. [ABSTRACT FROM AUTHOR]

The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets. Applying these networks to images demands a high computational effort and pushes the use of state-of-the-art networks on real-time video data out of reach of embedded platforms. Many recent works focus on reducing network complexity for real-time inference on embedded computing platforms. We adopt an orthogonal viewpoint and propose a novel algorithm exploiting the spatio-temporal sparsity of pixel changes. This optimized inference procedure resulted in an average speed-up of 9.1X over cuDNN on the Tegra X2 platform at a negligible accuracy loss of < 0.1% and no retraining of the network for a semantic segmentation application. Similarly, an average speed-up of 7.0X has been achieved for a pose detection DNN and a reduction of 5X of the number of arithmetic operations to be performed for object detection on static camera video surveillance data. These throughput gains combined with a lower power consumption result in an energy efficiency of 511GOp/s/W compared to 70GOp/s/W for the baseline. [ABSTRACT FROM AUTHOR]

Image processing is becoming ubiquitous in many activities. This kind of systems use industry-standard libraries, such as OpenCV, and GPGPU techniques such as CUDA and OpenCL. Nowadays, these are being ported to many computing platforms, offering significant processing power even in devices with limited resources. However, the only model that is truly ubiquitous, is the web itself. Modern browsers feature quite complex internals and offer sophisticated development and profiling tools, in order to offer the best user experience. Introduction of HTML5 allows Realtime video and image manipulation, in browser space, without any plugin. In addition, Wasm (web assembly) Javascript execution engine provides fastest possible performance by means of highly customized compiler and runtime, in almost any browser, including embedded ones. This paper presents an image processing system, architected as a modular web application, using only Raspberry PIs with a compact but fast middleware server, that performs all image operations in browser space by means of web assemblies. All components, including database support, can run in a single board, providing image and video processing speeds that match, or surpass, their native compiled C counterparts on the same platform. This solution has a very low cost, that fits with emerging markets, making it ideal for LATAM scenarios. [ABSTRACT FROM AUTHOR]

Embedded system design is crucial for the development of industry, technology, and science, and it is an area that has significantly grown in recent years throughout Latin America, both in academia and in industry. Embedded System (ES) refers to electronic equipment with a computing core which, unlike a personal computer, is designed to meet a specific function and is usually optimized to satisfy strict requirements of processing time, reliability, power consumption, size, and cost. With the advancement of research on the domains of Internet of Things (IoT), Computing Edge (CE) and Cloud Computing (CC) and their endless application possibilities, ESs have gained new roles and assumed unquestionable importance in our daily lives. In this article, we make a brief review of the relevant literature in the area of embedded systems in the Latin America region during recent years. [ABSTRACT FROM AUTHOR]

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