Wireless sensors; Real-time support; Wireless communication; XBee modules; Predictability

The ZigBee standard is focused on low-cost, low-power, wireless mesh networking, having a wide applicability mainly in the field of wireless sensor networks. A growing number of such applications require real-time behavior, both at the wireless communication and at the sensor levels. This paper proposes a solution to the problem of providing sensor-level real-time support for wireless platforms using ZigBee-based devices such as the XBee module. The discussion of the experimental results proves the predictable behavior of the XBee sensor platform used as a case study.

Data acquisition; Signal conditioning; Operational amplifiers; Analog multiplexers; Sample and hold; Digital-to-analog converters; Analog-to-digital converters; DAQ systems

This work focuses on the data acquisition and signal conditioning systems, allocating up to three sections to discuss each of the system components. Thus, the main topics of the workbook are: operational amplifiers, analog multiplexers/de-multiplexers, sample and hold circuits, digital-to-analog converters, analog-to-digital converters, and the structure, operation, programming principles and modes of use of a fully-equipped data acquisition and conditioning system. Two distinct sections are allocated for each of these topics. The theoretical section focuses on the architecture and operating principles of the respective class of circuits, with particular emphasis on the presentation and discussion of the main catalog parameters, with real-life exemplifications. The practical section consists of a set of practical (experimental) workshops and applications, specifically designed to study, test and measure each of the main parameters of the respective class of circuits. The measurement results will also be compared with the catalog specifications. Each workshop defines the parameter of interest, the graphical schematics of the electronic setup of the experiment, the required measurement and control equipment, and the necessary steps of the experiment. The audience of this workbook is intended to contain, besides the students of the DAQ Systems course, also professionals or even amateurs which are interested in this very attractive field of engineering.

Battery; Large-scale battery pack; Reconfigurable battery management system; State of health; Wear leveling

As the market and the application areas of high capacity battery energy storage systems are rapidly increasing, there is a correspondingly high interest in the topic of minimizing battery state of health degradation in battery packs. In this article, a novel method for battery management in large-scale battery packs is introduced, aiming to minimize battery degradation by enforcing a special wear leveling (WL) policy, adapted from the flash memory arrays. Using this method in conjunction with a hybrid mathematical-electrochemical battery model, a reconfigurable battery management system (BMS) is proposed and evaluated. The results of the performance analysis and in-depth comparisons with other state-of-the-art solution shows that the proposed method achieves significantly longer operating times for the battery packs-for example, 415% improvement over the classical BMS in the load current variation scenario. As the computing and memory requirements are relatively low, the new battery WL method can also be implemented on embedded systems with limited resources.

Battery management system; Gated recurrent unit; Lithium-ion battery; Long-short term memory; Online state prediction; Recurrent neural networks; State of health

Online state of health {SOH) prediction of lithium-ion batteries remains a very important problem in assessing the safety and reliability of batterypowered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short-term memory {LSTM) and gated recurrent unit {GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation {Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception ofthe battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters {about 25% fewer), thus making it a very suitable candidate for embedded implementations.

Cyber physical systems; Real-time scheduling; Mixed criticality systems; Multiple processing units

Cyber physical systems (CPSs) are a fast-evolving technology based on a strong synergy between heterogeneous sensing, networking, computation and control modules. When coping with critical applications that require real-time performance and autonomous operation in uncertain conditions, the design of such complex systems is still facing significant difficulties. A particular challenge in this respect derives from the software intensive nature of these systems - the need to develop flexible and specifically tailored task scheduling techniques. In our view, an appropriate line of thinking is to take advantage of mixed criticality concepts following the lessons learned from avionics and automotive domains, where complexity, safety, determinism and real-time constraints are extreme. From this perspective, our work aims at facilitating the integration of mixed criticality systems-based strategy in cyber physical systems by identifying the particularities of the latter and their influence on scheduling mechanisms, by describing the standard mixed-criticality task model in the cyber physical systems context, and by analyzing and proposing the most suitable scheduling algorithms to be implemented in cyber physical systems. Moreover, the perspectives on future developments in this area are discussed, as new horizons in research arise with the integration of mixed criticality concepts in the cyber physical systems context.

Battery state estimation; State of health; Remaining useful life; Battery model; Embedded application; Battery fuel gauge

Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off-the-shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper.

Wireless sensor networks; Real-time systems; Wireless communication; Access protocol; Time measurement

Low-level communication protocols and their timing behavior are essential to developing wireless sensor networks (WSNs) able to provide the support and operating guarantees required by many current real-time applications. Nevertheless, this aspect still remains an issue in the state-of-the-art. In this paper we provide a detailed analysis of a recently proposed MAC-level communication timing model and demonstrate its usability in designing real-time protocols. The results of a large set of measurements are also presented and discussed here, in direct relation to the main time parameters of the analyzed model.

Foreground extraction; Videoconference; Optical flow; Graph cut; Motion segmentation; Real-time video processing

Immersive videoconferences have added a new dimension to remote collaboration by bringing participants together in a common virtual space. To achieve this, the conferencing system must extract in real-time the foreground from each incoming video stream and translate it into the shared virtual space. The method presented in this paper differentiates itself in the sense that no prior training or assumptions on the video content are used during foreground extraction. A temporally coherent mask is created based on motion cues obtained from the video stream and is used to provide a set of hard constraints. Based on these constraints, a graph cut algorithm is employed to produce the pixel-accurate foreground segmentation. The obtained results are evaluated using a state-of-the-art perceptual metric to provide an objective assessment of the method accuracy and reliability. Furthermore, the presented approach makes use of parallel execution in order to achieve real-time processing capabilities.

Battery management; Battery-powered device; Nickel-metal hydride (Ni-MH); State-of-health (SoH) prediction

Battery-powered embedded systems have known a rapid evolution in recent years, as nickel-metal hydride (Ni-MH) battery technology has enabled important reductions in size and proportional increases in total capacity over the older nickel-cadmium (Ni-Cd) and lead-acid battery types. This paper addresses the problem of state-of-health (SoH) estimation and prediction for use in resource-constrained Ni-MH-battery-powered embedded systems. We propose a novel SoH prediction methodology, presenting both a theoretical analysis of the estimation algorithm and the detailed description of hardware and software implementation. Two versions of estimation algorithms are proposed, along with the analysis of their performances in terms of prediction accuracy and required processing power, as the SoH prediction is designed to run online, being part of an embedded battery management system.

Communication protocols; Hard real-time (HRT); Predictability; Serial peripheral interface (SPI); Smart sensors; Time triggered

This paper studies the problem of data communication protocols for multiprocessor smart sensors and embedded applications with hard real-time (HRT) or critical requirements. We propose a time-triggered communication interface and set of protocols, called Predictable ARchitecture for Sensor Communication Systems (PARSECS), specifically designed to sustain, at low costs and complexity, the predictable operation of such HRT systems. The general interface architecture, data format, and communication protocols are discussed, along with a case study-the implementation of PARSECS on the full-duplex serial peripheral interface for the COllaborative Robotic Environment-the Timisoara eXperiment (CORE-TX) smart sensors platform. Its predictability, timeliness, and overall performance evaluation and validation are presented in detail based on experimental results and measurements. A comparative study with some of the most prominent systems in the field is also provided.

Collaborative system; Distance measurement; Indoor communication; Mobile robots; Sonar

This paper focuses on the problem of autonomous distance calculation between multiple mobile robots in collaborative systems. We propose and discuss two distinct methods, specifically developed under important design and functional constraints, such as the speed of operation, accuracy, energy and cost efficiency. Moreover, the methods are designed to be applied to indoor robotic systems and are independent of fixed landmarks. The measurement results, performed on the CORE-TX case study, show that the proposed solutions meet the design requirements previously specified.

Genetic algorithms; Job-shop scheduling; Initial populations; Chromosomes

This paper presents an efficient method of enhancing genetic algorithms (GAs) for solving the Job-Shop Scheduling Problem (JSSP), by generating near optimal initial populations. Since the choice of the initial population has a high impact on the speed of the evolution and the quality of the final results, we focused on generating its individuals using genetically evolved priority dispatching rules. Our experiments show a significant increase in quality and speed of scheduling with GAs, and in some cases the evolved priority rules alone determined better solutions then the GA itself. The analyzed reference GA uses Giffler & Thompson (GT) heuristic and priority lists. To speed up the generation of priority rules, we have used a weighted sum of priority rules formula that revealed significantly better performances than Genetic Programming (GP). For evaluation of the proposed algorithm, the well known benchmark data sets from Fisher & Thompson (F&T) and Laurence Kramer (LA) have been used.

Power consumption; Multi-threading; Multi-core; Mobile applications; Power profiling

One constant in computing which is true also for mobile computing is the continue requirement for greater performance. Every performance advance in mobile processors leads to another level of greater performance demands from newest mobile applications. However, on battery powered devices performance is strictly limited by the battery capacity, therefore energy efficient applications and systems have to be developed. The power consumption problem of mobile systems is in general a very complex one and remained very actual for quite a long time. In this paper we aim to define a software execution framework for mobile systems in order to characterize the power consumption profile of multi-threading mobile applications. Study results for different thread libraries, multi-core processors and multithreaded parallelized applications are also presented.