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The human voice is notoriously variable, and conventional measurement paradigms are weak in terms of providing evidence for effects of treatment and/or training of voices. New methods are needed that can take into account the variability of metrics and types of phonation across the voice range. The “voice map” is a generalization of the Voice Range Profile (a.k.a. the phonetogram), with the potential to be used in many ways, for teaching, training, therapy and research. FonaDyn is intended as a proof-of concept workbench for education and research on phonation, and for exploring and validating the analysis paradigm of voice-mapping. Version 3.1 of the FonaDyn system adds many new functions, including listening from maps; displaying multiple maps and difference maps to track effects of voice interventions; smoothing/interpolation of voice maps; clustering not only of EGG shapes but also of acoustic and EGG metrics into phonation types; extended multichannel acquisition;24-bit recording with optional max 140 dB SPL; a built-in SPL calibration and signal diagnostics tool; EGG noise suppression; more Matlab integration; script control; the acoustic metrics Spectrum Balance, Cepstral Peak Prominence and Harmonic Richness Factor (of the EGG); and better window layout control. Stability and usability are further improved. Apple M-series processors are now supported natively., QC 20240214

A ship’s communication system serves as a vital infrastructure to host real-time applications,facilitating the transmission of crucial data, including sensor readings and control commands. Theexpansive dimensions of a ship necessitate a complex network of cables in an analogue hardwiredsetup, stretching from the bridge to the machinery. However, this reliance on multiple cables introduces logistical challenges and vulnerabilities within the system architecture. This thesis establishesa systematic approach for transitioning ship control systems from traditional analogue hardwiredconfigurations to more efficient Controller Area Network (CAN) bus-based systems. CAN is amulti-master communication protocol that enables multiple controllers to communicate with eachother without a host computer, making it particularly suitable for complex environments. While alternative networking options such as Ethernet or wireless networks are available, CAN emerges as acost-effective and robust communication solution suited for maritime environments. The transitionto CAN-based systems not only prolongs the ship’s operational lifespan but can also enhance fuelefficiency and reduce environmental impact. Ensuring a seamless transition requires careful consideration of safety and functionality equivalent to legacy systems. Extensive research into suitablecommunication buses, CAN protocols, and scheduling algorithms is essential. By exploring Controller Area Network with Flexible Data-Rate (CAN FD) as a complement to CAN 2.0, the tool offersmultiple solutions, with the optimal choice contingent upon various parameters. A comprehensive tool has been developed to facilitate the safe migration from hardwired systems to CAN-basedsolutions. This tool enables the mapping of signals to messages, priority assignment using various algorithms, including Audsley’s optimal priority assignment algorithm, and real-time analysis.This work will assist in the transition by presenting diverse solutions, clea

Optical spectroscopy is widely used for quantitative detection of small molecules in complex gas matrices. Employing a laser as a light source enables highly sensitive, selective, and accurate trace gas analysis in exhaled breath without the need for frequent calibration. Many volatile species with direct physiological relevance, especially small molecules such as CO2, CO, NO, CH4, NH3, HCN, C2H4, and their isotopoloques, can be measured with high time resolution using compact optical analyzers. Thus, laser-based sensors are an important complement to other analytical platforms and contribute to establishing breath gas analysis in the clinical practice. This chapter introduces the basics of optical spectroscopy and describes the most relevant techniques currently used in the field, such as nondispersive, laser absorption, and photoacoustic spectroscopy. Successful applications of optical methods are presented, and the future prospects are discussed. Owing to the advent of novel light sources, such as quantum cascade lasers and optical frequency combs, spectroscopy will continue to play a significant role in breath gas analysis.

Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an "online" fashion. Widely used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data-in terms of alignment changes, sequence addition or removal-present common scenarios that can benefit from online inference.

Lipid membrane nanotubes (NTs) are a widespread template forin vitrostudies of cellular processes happening at high membrane curvature. Traditionally NTs are manufactured one by one, using sophisticated membrane micromanipulations, while simplified methods for controlled batch production of NTs are in growing demand. Here we propose a lab-on-a-chip (LOC) approach to the simultaneous formation of multiple NTs with length and radius controlled by the chip design. The NTs form upon rolling silica microbeads covered by lipid lamellas over the pillars of a polymer micropillar array. The array's design and surface chemistry set the geometry of the resulting free-standing NTs. The integration of the array inside a microfluidic chamber further enables fast and turbulence-free addition of components, such as proteins, to multiple preformed NTs. This LOC approach to NT production is compatible with the use of high power objectives of a fluorescence microscope, making real-time quantification of the different modes of the protein activity in a single experiment possible.

Rising costs and the pressure for quality in the mining industry demand a significant optimising of raw material recovery. On the one hand, the immediate resources must be utilised to the maximum and on the other hand, the quality of raw materials in further mineral processing must be kept constant as much as possible. As a consequence, the monitoring of the quality of raw material processing becomes very important. If the quality control is carried out with conventional sampling and laboratory analysis, then it it will be a major challenge for the operator of the processing plant. The principle of NIR-analysis is based upon a highly sensitive detector in contrast to conventional online-analysis. This makes it possible to measure compounds rather then just the concentration of elements apart from using a lower energy source. In this article, the near-infrared technology is compared to the on-the-market available real-time analysis and their applications are discussed in the following industries: aluminium, cement, coal, gold, iron, calcium carbonate, manganese, nickel/copper/platinum, phosphate, potash, Refuse-Derived-Fuel(RDF) and zinc., Rising costs and the pressure for quality in the mining industry demand a significant optimising of raw material recovery. On the one hand, the immediate resources must be utilised to the maximum and on the other hand, the quality of raw materials in further mineral processing must be kept constant as much as possible. As a consequence, the monitoring of the quality of raw material processing becomes very important. If the quality control is carried out with conventional sampling and laboratory analysis, then it it will be a major challenge for the operator of the processing plant. The principle of NIR-analysis is based upon a highly sensitive detector in contrast to conventional online-analysis. This makes it possible to measure compounds rather then just the concentration of elements apart from using a lower energy source. In this article, the near-infrared technology is compared to the on-the-market available real-time analysis and their applications are discussed in the following industries: aluminium, cement, coal, gold, iron, calcium carbonate, manganese, nickel/copper/platinum, phosphate, potash, Refuse-Derived-Fuel(RDF) and zinc.

Målen med denna studie är att implementera en testmiljö för streaming telemetry samt jämföra två alternativ för att möjliggöra realtidsanalys av det insamlade datat. Dessa två alternativ är Python-biblioteken PyKafka och Confluent-Kafka-Python. Bedömningskritierna för jämförselsen var dokumentation, kodmängd och minnesanvändning. Testmiljön för streaming telemetry använder en router med Cisco IOS XR programvara som skickar data till en Cisco Pipeline collector, som vidare sänder datat till ett Kafka-kluster. Jämförelsen av Python-biblioteken utfördes med språket Python. Resultaten av jämförelsen visade att båda biblioteken hade välskriven dokumentation och liten skillnad i kodmängd, dock använde Confluent-Kafka-Python mindre minne. Studien visar att streaming telemetry med realtidsanalys kan fungera bra som ett komplement till eller en ersättning av SNMP. Studien rekommenderar användning av Confluent-Kafka-Python för implementering i produktionsmiljöer med ett stort antal nätverksenheter med tanke på den lägre minnesanvändningen., The goals of this study are to implement a test environment for streaming telemetry and compare two alternatives for analysing the collected data in realtime. The two alternatives are the Python libraries PyKafka and Confluent-Kafka-Python. The comparison focused mainly on three areas, these being documentation, amount of code and memory usage. The test environment for streaming telemetry was set up with a router running IOS XR software that is sending data to a Cisco Pipeline collector, which in turn sends data to a Kafka-cluster. The comparison of the two libraries for interfacing with the cluster was made with the language Python. The results of the comparison showed that both libraries had well-written documentation and showed a negligible difference in amount of code. The memory usage was considerably lower with the Confluent-Kafka-Python library. The study shows that streaming telemetry together with real-time analysis makes a good complement to or a replacement of SNMP. The study further recommends the use of Confluent-Kafka-Python in real-world implementations of streaming telemetry, particularly in large networks with a large amount of devices.

Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair., Pattern Recognition and Bioinformatics, Biomechatronics & Human-Machine Control

Blood flow and ventilatory flow strongly influence the concentrations of volatile organic compounds (VOCs) in exhaled breath. The physicochemical properties of a compound (e.g., water solubility) additionally determine if the concentration of the compound in breath reflects the alveolar concentration, the concentration in the upper airways, or a mixture of both. Mathematical modeling based on mass balance equations helps to understand how measured breath concentrations are related to their corresponding blood concentrations and physiological parameters, such as metabolic rates and endogenous production rates. In addition, the influence of inhaled compounds on their exhaled concentrations can be quantified and appropriate correction formulas can be derived. Isoprene and acetone, two endogenous VOCs with very different water solubility, have been modeled to explain the essential features of their behavior in breath. This chapter introduces the theory of physiological modeling of exhaled VOCs, with examples of isoprene and acetone.

The integration of autonomous robots in real applications is a challenge. It needs that the behaviour of these robots is proved to be safe. In this paper, we focus on the real-time software embedded on the robot, and that supports the execution of safe and autonomous behaviours. We propose a methodology that goes from the design of component-based software architectures using a Domain Specific Language, to the analysis of the real-time constraints that arise when considering the safety of software applications. This methodology is supported by a code generation toolchain that ensures that the code eventually executed on the robot is consistent with the analysis performed. This methodology is applied on a ground robot exploring an area. Categories (2), (3)

This article examines the hypothesis according to which the increasing use of nada as a pragmatic marker is the recent outcome of a grammaticalization process, mainly observed in youth language. In addition to providing an overview of its polyfunctionality, it examines in detail the functional and structural changes that the poorly studied marker has undergone in contemporary Spanish. The stage of grammaticalization reached by nada is carefully examined through a diachronic corpus analysis, quantitatively and qualitatively tracking its behavior during four contemporary time periods (1970s, 1980s, 1990s, >2000), and documented in various databases of spoken language. The study further argues in favor of combining different methodologies in the analysis of (recent) language change, and the development of pragmatic markers in particular, namely a real-time analysis, an apparent-time analysis and a crossover between both.

Both in design and production of nanoparticles and nanocomposites it is of vital importance to have information about their size and concentration. During the formation of nanoparticles, real-time monitoring of particle size and concentration during bottom-up synthesis in liquids allows for a detailed study of nucleation and growth. This provides valuable insights into the formation of nanoparticles that can be used for process optimization and scale up. In the production of nanoparticles, real-time monitoring enables intervention to minimize the number of off-spec batches. In this paper we will qualify an ultrasound nanoparticle sizer (UNPS) as a real-time monitor for the growth of nanoparticles (or sub-micro particles) in the 100 nm-1 μm range. Nanoparticles affect the speed and attenuation of ultrasonic waves in the dispersion. The size of the change depends, amongst other things, on the size and concentration of the nanoparticles. This dependency is used in the UNPS method. The qualification of the UNPS was undertaken in two successful experiments. The first experiment consisted of static measurements on commercially available silica particles, and the second experiment was real-time monitoring of the size and concentration during the growth of silica nanoparticles in Stöber synthesis in a water-alcohol mixture starting from the molecular precursor tetraethyl orthosilicate. The results of the UNPS were verified by measurements of a dynamic light scattering device and a transmission electron microscope., ImPhys/Acoustical Wavefield Imaging

Both in design and production of nanoparticles and nanocomposites it is of vital importance to have information about their size and concentration. During the formation of nanoparticles, real-time monitoring of particle size and concentration during bottom-up synthesis in liquids allows for a detailed study of nucleation and growth. This provides valuable insights into the formation of nanoparticles that can be used for process optimization and scale up. In the production of nanoparticles, real-time monitoring enables intervention to minimize the number of off-spec batches. In this paper we will qualify an ultrasound nanoparticle sizer (UNPS) as a real-time monitor for the growth of nanoparticles (or sub-micro particles) in the 100 nm-1 μm range. Nanoparticles affect the speed and attenuation of ultrasonic waves in the dispersion. The size of the change depends, amongst other things, on the size and concentration of the nanoparticles. This dependency is used in the UNPS method. The qualification of the UNPS was undertaken in two successful experiments. The first experiment consisted of static measurements on commercially available silica particles, and the second experiment was real-time monitoring of the size and concentration during the growth of silica nanoparticles in Stöber synthesis in a water-alcohol mixture starting from the molecular precursor tetraethyl orthosilicate. The results of the UNPS were verified by measurements of a dynamic light scattering device and a transmission electron microscope.

To tailor the properties of nanoparticles and nanocomposites, precise control over particle size is of vital importance. Real-time monitoring of particle size during bottom-up synthesis in liquids would allow a detailed study of particle nucleation and growth, which provides valuable insights in the mechanism of formation of the nanoparticles. Furthermore, it facilitates a rational scale-up, and would enable adequate intervention in the production process of nanoparticle dispersions to minimize the number of off-spec batches. Since real-time monitoring requires particle size measurements on dispersions in flow, conventional dynamic light scattering (DLS) techniques are not suited: they rely on single scattering and measure the Brownian motion of particles dispersed in a liquid. Here, we present a set-up that allows accurate measurements in real-time on flowing dispersions using a DLS technique based on modulated 3D cross-correlation. This technique uses two simultaneous light scattering experiments performed at the same scattering vector on the same sample volume in order to extract only the single scattering information common to both. We connected the reactor to a flow-cell in the DLS equipment using a tailor-made analysis loop, and successfully demonstrated the complete set-up through monitoring of the size of spherical silica nanoparticles during Stöber synthesis in a water-alcohol mixture starting from the molecular precursor tetraethyl orthosilicate.

Electrochemical CO2 reduction (EC-CO2R) has seen a resurgence in interest over the past several years; however, the means of analyzing catalytically produced products continues to rely on decades-old methods such as gas chromatography, high-performance liquid chromatography, and nuclear magnetic resonance. Real-time analysis of the gaseous and liquid products of this reaction is highly desirable; however, few analytical techniques have been developed thus far to meet this need. Here, we demonstrate the first use of selected-ion flow-tube mass spectrometry (SIFT-MS) as an analytical tool capable of measuring in real time both the gas- and liquid-phase products of EC-CO2R in aqueous solution. SIFT-MS uses well-understood ion–molecule reactions to enable the analysis of similar multicomponent mixtures by preventing substantial fragmentation of the analyte. We lay out the framework in which to evaluate the tool's capabilities and show that the C1–C3 hydrocarbon, alcohol, and aldehyde products of CO2R should be quantitatively detectable.

GWAC (Ground Wide Angle Camera) poses huge challenges in large-scale catalogue storage and real-time processing of quick search of transients among wide field-of-view time-series data. Firstly, this paper proposes the concept of using databases’ capabilities of fast data processing and parallelism, which will improve system performance and availability through the integration of data storage and computing platform. To understand the feasibility of Column-store MonetDB in vast catalogue management, we carry out a variety of pilot experiments of key technologies. We conduct TPC-H benchmark, data loading benchmark and optimization, and key algorithm testing of astronomical source association, all compared with the traditional row store database. Then, we use MonetDB to realize cross-match Zone algorithm. UDF function is developed for customizable data loading. Tests results show t MonetDB database has a remarkable performance in large amounts of data management and is efficient in real-time data process, thus has the ability to deal with 2.5T catalog data. In the end we propose a wide field of view massive time serial observation data processing solution using the in-memory column store database MonetDB. The experimental results show that the feasibility of the scheme. The design plan of MonetDB-based massive catalogue data processing solution, is an efficient astronomical database solution that combines data processing and data management.

The purpose of this article is to provide a definition of intrusion learning, identify its distinctive aspects, and provide recommendations for advancing intrusion learning as a practice domain. The authors define intrusion learning as the collection of online network algorithms that learn from and monitor streaming network data resulting in effective intrusion-detection methods for enabling the security and resiliency of enterprise systems. The network algorithms build on advances in cyber-defensive and cyber-offensive capabilities. Intrusion learning is an emerging domain that draws from machine learning, intrusion detection, and streaming network data. Intrusion learning offers to significantly enhance enterprise security and resiliency through augmented perimeter defense and may mitigate increasing threats facing enterprise perimeter protection. The article will be of interest to researchers, sponsors, and entrepreneurs interested in enhancing enterprise security and resiliency.

Cellular receptor systems are expected to present complex ligand interaction patterns that cannot beevaluated assuming a simple one ligand:one receptor interaction model. We have previously evaluatedheterogeneous interactions using an alternative method to regression analysis, called Interaction Map(IM). IM decomposes a time-resolved binding curve into its separate components. By replacing the reductionistic,scalar kinetic association rate constant ka and dissociation rate constant kd with a two-dimensionaldistribution of ka and kd, it is possible to display heterogeneous data as a map where each peakcorresponds to one of the components that contribute to the cumulative binding curve. Here we challengethe Interaction Map approach by artificially generating heterogeneous data from two known interactions,on either LigandTracer or Surface Plasmon Resonance devices. We prove the ability of IM toaccurately decompose these man-made heterogeneous binding curves composed of two different interactions.We conclude that the Interaction Map approach is well suited for the analysis of complex bindingdata and forecast that it has a potential to resolve previously uninterpretable data, in particular thosegenerated in cell-based assays.

Ubiquitous deployment of embedded systems is having a substantial impact on our society, since they interact with our lives in many critical real-time applications. Typically, embedded systems used in safety or mission critical applications (e.g., aerospace, avionics, automotive or nuclear domains) work in harsh environments where they are exposed to frequent transient faults such as power supply jitter, network noise and radiation. They are also susceptible to errors originating from design and production faults. Hence, they have the design objective to maintain the properties of timeliness and functional correctness even under error occurrences. Fault-tolerance plays a crucial role towards achieving dependability, and the fundamental requirement for the design of effective and efficient fault-tolerance mechanisms is a realistic and applicable model of potential faults and their manifestations. An important factor to be considered in this context is the random nature of faults and errors, which, if addressed in the timing analysis by assuming a rigid worst-case occurrence scenario, may lead to inaccurate results. It is also important that the power, weight, space and cost constraints of embedded systems are addressed by efficiently using the available resources for fault-tolerance. This thesis presents a framework for designing predictably dependable embedded real-time systems by jointly addressing the timeliness and the reliability properties. It proposes a spectrum of fault-tolerance strategies particularly targeting embedded real-time systems. Efficient resource usage is attained by considering the diverse criticality levels of the systems' building blocks. The fault-tolerance strategies are complemented with the proposed probabilistic schedulability analysis techniques, which are based on a comprehensive stochastic fault and error model.

Conceptual design is the starting point of the design process. The conceptual design stage comprises the formation of several ideas or design concepts to meet the imposed constraints. StructuralComponents is a software application that attempts to provide the designing engineer with a suitable set of tools for this stage. It facilitates the exploration of the early design space with the proper balance between accuracy and the ability to explore. Using parametric-associative modelling, a structural design concept can be composed of parameters and object relations. Built-in design and analysis knowledge returns immediate output to guide the design process. The major development is a redevelopment of the software architecture as a client-server system in support of emphasising design exploration and a system of design plurality and directed optimisation., Structural Engineering, Civil Engineering and Geosciences

We propose a model- and component-based approach for communications- oriented development of Distributed Real-time Embedded (DRE) systems with a support for legacy communication protocols, legacy nodes and Holistic Response Time Analysis (HRTA). Because an end-to-end timing model should be available to perform HRTA, we also present a method to extract such models from component-based DRE systems. Moreover, we extend the existing analysis of Controller Area Network to support mixed messages in the system with priority- and FIFO-queued nodes. A mixed message represents a common transmission pattern implemented by some high-level protocols used in the industry. We also provide a proof of concept by extending the existing industrial model, i.e., Rubus Component Model, implementing the HRTA along with the extended analysis in an industrial tool suite, i.e., Rubus-ICE, and conducting an automotive-application case study.

In today’s research and market, IT applications for health-care are gaining huge interest of both IT and medical researchers. Cardiovascular diseases (CVDs) are considered the largest cause of death for both men and women regardless of ethnic backgrounds. More efficient treatments and most importantly efficient methods of cardiac diagnosis that examine heart diseases are desired. Electrocardiography (ECG) is an essential method used to diagnose heart diseases. However, diagnosing any cardiovascular disease based on the 12-lead ECG printout from an ECG machine using human eye might seriously impair analysis accuracy. To meet this challenge of today’s ECG analysis methodology, a more reliable solution that can analyze huge amount of patient’s data in real-time is desired. The software solution presented in this article is aimed to reduce the risk while diagnosing cardiovascular diseases (CVDs) by human eye, computation of large-scale patient’s data in real-time at the patient’s location and sending the required results or summary to the doctor/nurse. Keeping in mind the importance of real-time analysis of patient’s data, the software system has built upon small individual algorithms/modules designed for multi-core architecture, where each module is supposed to be processed by an individual core/processor in parallel. All the input and output processes to the analysis system are made automated, which reduces operator’s interaction to the system and thus reducing the cost. The outputs/results of the processing are summarized to smaller files in both ASCII and binary formats to meet the requirement of exchanging the data over Voice and Data Networks.

Server-based architectures have recently generated more interests and are currently considered for usage for communication in networks. In parallel, switched Ethernet technology has been widely adopted and used in lots of networked systems. Current requirements of networks for supporting real-time guarantees while being flexible at the same time have made the network designers to consider addition of some features to common switches. The FTT-Enabled Ethernet switch is a switch that has been developed to support the FTT (Flexible Time Triggered) paradigm. Recently, servers have been added in these types of switches in order to efficiently manage their allocated bandwidth to different types of messages. A hierarchical network of Ethernet switches might be designed in different ways according to the overall goals and properties of the network. In this thesis, after a study on different design solutions, an architecture has been proposed based on FTT-enabled switches, motivated by their support of real-time constraints and server-based communication features. After having created the architecture, a protocol for bandwidth reservation for this hierarchically composed Ethernet switch architecture is developed. Behavior of the designed protocol is described in detail and it has been modeled using Uppaal. Moreover, the temporal behavior (timing) of the network is presented.

FlexRay is a communication protocol heavily promoted on the market by a large group of car manufacturers and automotive electronics suppliers. However, before it can be successfully used for safety-critical applications that require predictability, timing analysis techniques are necessary for providing bounds for the message communication times. In this paper, we propose techniques for determining the timing properties of messages transmitted in both the static and the dynamic segments of a FlexRay communication cycle. The analysis techniques for messages are integrated in the context of a holistic schedulability analysis that computes the worst-case response times of all the tasks and messages in the system. We have evaluated the proposed analysis techniques using extensive experiments. We also present and evaluate three optimisation algorithms that can be used to improve the schedulability of a system that uses FlexRay. © 2007 Springer Science+Business Media, LLC.

The interest in high performance chip architectures for biomedical applications is gaining a lot of research and market interest. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine. However, ECG analysis still faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and it is centered on the parallelization of the ECG computation kernel. Our Hardware- Software (HW/SW) Multi-Processor System-on-Chip (MPSoQ design improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final HW/SW architecture is the focus of this paper. We explore the design space by considering a number of hardware and software architectural variants, and deploy industrial components to build up the system.

The growing amount and diversity of functions to be implemented by the current and future embedded applications (like, for example, in automotive electronics) have shown that, in many cases, time-triggered and event-triggered functions have to coexist on the computing nodes and to interact over the communication infrastructure. When time-triggered and event-triggered activities have to share the same processing node, a natural way for the execution support can be provided through a hierarchical scheduler. Similarly, when such heterogeneous applications are mapped over a distributed architecture, the communication infrastructure should allow for message exchange in both time-triggered and event-triggered manner in order to ensure a straightforward interconnection of heterogeneous components. This thesis studies aspects related to the analysis and design optimisation for safety-critical hard real-time applications running on hierarchically scheduled distributed embedded systems. It first provides the basis for the timing analysis of the activities in such a system, by carefully taking into consideration all the interferences that appear at run-time between the processes executed according to different scheduling policies. Moreover, due to the distributed nature of the architecture, message delays are also taken into consideration during the timing analysis. Once the schedulability analysis has been provided, the entire system can be optimised by adjusting its configuration parameters. In our work, the entire optimisation process is directed by the results from the timing analysis, with the goal that in the end the timing constraints of the application are satisfied. The analysis and design methodology proposed in the first part of the thesis is applied next on the particular category of distributed systems that use FlexRay as a communication protocol. We start by providing a schedulability analysis for messages transmitted over a FlexRay bus, and then by proposing a b

The interest in high performance chip architectures for biomedical applications is on the rise. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine, which faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and is centered on the parallelization of the ECG computation kernel. It improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final hardware/software architecture, modeling, and simulation is the focus of this paper. Our system model is based on industrial components. The architectural template we employ is scalable and flexible., QC 20120130

Many products are built from more or less independently developed subsystems. For instance, a car consists of subsystems for transmission, braking, suspension, etc. These subsystems are frequently controlled by an embedded computer system. In the automotive industry, as well as in other application domains, there is currently a trend from an approach where subsystems have dedicated computer hardware and other resources (a federated approach) to an approach where subsystems share hardware and other resources (an integrated approach). This is motivated by a strong pressure to reduce product cost, at the same time as an increasing number of subsystems are being introduced. When integrating subsystems, it is desirable that guarantees valid before integration are also valid after integration, since this would eliminate the need for costly reverifications. The computer network is a resource that is typically shared among all subsystems. Hence, a central issue when integrating subsystems is to provide an efficient scheduling of message transmissions on the network. There are essentially three families of schedulers that can be used: priority-driven schedulers that assign priorities to messages, time-driven schedulers that assign specific time-slots for transmission of specific messages, and share-driven schedulers that assign shares of the available network capacity to groups of messages. This thesis presents a framework for share-driven scheduling, to be implemented and used in embedded networks, with the aim to facilitate subsystem integration by reducing the risk of interference between subsystems. The framework is applied in the automotive domain. The initial parts of the thesis give an overview of systems, subsystems and network technologies found and used in the automotive domain. Then, the share-driven scheduling framework is presented, analytically investigated and proven, as well as evaluated in a simulation study. Finally it is shown how the framework is to be co

This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.

Http://archive.org/details/realtimeengageme109459775, Captain, United States Army, Approved for public release; distribution is unlimited.

As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantly better results in some cases, when compared to the standard DCA without segmentation. Therefore, we conclude that the segmentation is applicable to the DCA for the purpose of real-time analysis.

As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantly better results in some cases, when compared to the standard DCA without segmentation. Therefore, we conclude that the segmentation is applicable to the DCA for the purpose of real-time analysis.

As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, to continuously detect misuses as soon as they occur. Consequently, the analysis process performed by an intrusion detection system must operate in real-time or near-to real-time. The analysis process of the DCA is currently performed offline, therefore to improve the algorithm's performance we suggest the development of a real-time analysis component. The initial step of the development is to apply segmentation to the DCA. This involves segmenting the current output of the DCA into slices and performing the analysis in various ways. Two segmentation approaches are introduced and tested in this paper, namely antigen based segmentation (ABS) and time based segmentation (TBS). The results of the corresponding experiments suggest that applying segmentation produces different and significantly better results in some cases, when compared to the standard DCA without segmentation. Therefore, we conclude that the segmentation is applicable to the DCA for the purpose of real-time analysis.