The vehicle-to-vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for vehicular ad hoc networks (VANETs). Extensive research efforts have been made to develop V2V channel models to be implemented in advanced VANET system simulators for performance evaluation. The impact of shadowing caused by other vehicles has, however, largely been neglected in most of the models, as well as in the system simulations. In this paper we present a shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios. The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non-line-of-sight due to buildings, with the help of video information recorded during the measurements. It is observed that vehicles obstructing the LOS induce an additional average attenuation of about 10 dB in the received signal power. An approach to incorporate the LOS/OLOS model into existing VANET simulators is also provided. Finally, system level VANET simulation results are presented, showing the difference between the LOS/OLOS model and a channel model based on Nakagami-m fading.

Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption. © 2019 Elsevier B.V.

The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.

In the field of structural operational semantics (SOS), there have been several proposals both for syntactic rule formats guaranteeing the validity of algebraic laws, and for algorithms for automatically generating ground-complete axiomatizations. However, there has been no synergy between these two types of results. This paper takes the first steps in marrying these two areas of research in the meta-theory of SOS and shows that taking algebraic laws into account in the mechanical generation of axiomatizations results in simpler axiomatizations. The proposed theory is applied to a paradigmatic example from the literature, showing that, in this case, the generated axiomatization coincides with a classic hand-crafted one. © 2013 Springer-Verlag Berlin Heidelberg.

High-efficiency photocatalysts are crucial for the removal of organic pollutants and environmental sustainability. In the present work, we report on a new low-temperature hydrothermal chemical method, assisted by ultrasonication, to synthesize disruptive plasmonic ZnO/graphene/Ag/AgI nanocomposites for solar-driven photocatalysis. The plasmonic nanocomposites were investigated by a wide range of characterization techniques, confirming successful formation of photocatalysts with excellent degradation efficiency. Using Congo red as a model dye molecule, our experimental results demonstrated a photocatalytic reactivity exceeding 90% efficiency after one hour simulated solar irradiation. The significantly enhanced degradation efficiency is attributed to improved electronic properties of the nanocomposites by hybridization of the graphene and to the addition of Ag/AgI which generates a strong surface plasmon resonance effect in the metallic silver further improving the photocatalytic activity and stability under solar irradiation. Scavenger experiments suggest that superoxide and hydroxyl radicals are responsible for the photodegradation of Congo red. Our findings are important for the fundamental understanding of the photocatalytic mechanism of ZnO/graphene/Ag/AgI nanocomposites and can lead to further development of novel efficient photocatalyst materials. © 2019 Elsevier B.V.

This paper presents some results within Pedatronics; a fusion between pedagogics and mechatronics. Our research interest is to study what emerges in the play with robotic toys. Field-experimental studies of 67 year old children’s purposeless play with robotic toys created a self-developmental sphere, as well as evoked young girl’s technological interest. Both girls and boys prolonged and intensified their interest according to the amount of gadgets involved. The results disclose a learning potential, indicating the importance to develop strategies at an early stage in order to encourage girls to choose technological and engineering educations. The study recommend engineers and toy designers, in cooperation with children, to move towards ’Integrated Play Systems’. Due to an ethological method, the results differ from other studies of children’s play with technological advanced artefacts. © 2002 IEEE

Drive-thru-Internet is a scenario in cooperative intelligent transportation systems (C-ITSs), where a road-side unit (RSU) provides multimedia services to vehicles that pass by. Performance of the drive-thru-Internet depends on various factors, including data traffic intensity, vehicle traffic density, and radio-link quality within the coverage area of the RSU, and must be evaluated at the stage of system design in order to fulfill the quality-of-service requirements of the customers in C-ITS. In this paper, we present an analytical framework that models downlink traffic in a drive-thru-Internet scenario by means of a multidimensional Markov process: the packet arrivals in the RSU buffer constitute Poisson processes and the transmission times are exponentially distributed. Taking into account the state space explosion problem associated with multidimensional Markov processes, we use iterative perturbation techniques to calculate the stationary distribution of the Markov chain. Our numerical results reveal that the proposed approach yields accurate estimates of various performance metrics, such as the mean queue content and the mean packet delay for a wide range of workloads. © 2019 IEEE.

We report on a tool prototype for model-based testing of cyber-physical systems. Our starting point is a hybrid-system model specified in a domain-specific language called Acumen. Our prototype tool is implemented in Matlab and covers three stages of model-based testing, namely, test-case generation, test-case execution, and conformance analysis. We have applied our implementation to a number of typical examples of cyber-physical systems in order to analyze its applicability. In this paper, we report on the result of applying the prototype tool on a DC-DC boost converter. © Springer International Publishing Switzerland 2015

Cyber-physical systems (CPSs) are the result of the integration of connected computer systems with the physical world. They feature complex interactions that go beyond traditional communication schemes and protocols in computer systems. One distinguished feature of such complex interactions is the tight coupling between discrete and continuous interactions, captured by hybrid system models.

Due to the complexity of CPSs, providing rigorous and model-based analysis methods and tools for verifying correctness of such systems is of the utmost importance. Model-based testing (MBT) is one such verification technique that can be used for checking the conformance of an implementation of a system to its specification (model).

In this chapter, we first review the main concepts and techniques in MBT. Subsequently, we review the most common modeling formalisms for CPSs, with focus on hybrid system models. Subsequently, we provide a brief overview of conformance relations and conformance testing techniques for CPSs. © 2017 Elsevier Inc. All rights reserved.

In embedded computer and communication system the capacity demand for interconnection networks is increasing continuously in order to achieve high-performance systems. Recent breakthroughs show that by using reconfigurability inside a single chip substantial performance gains can be added. However, in this thesis the focus is on system level reconfigurability (between chips or modules) and the performance gains that potentially can be achieved by having support for runtime reconfigurability on the system level.This thesis addresses the field of runtime system level reconfigurability with the use of optics in switches and routers for data- and telecommunications, and in multi-processor systems used for embedded signal processing. Several reconfigurable systems for switching and routing with support to adapt for asymmetric traffic patterns are proposed and compared to identify how design choices affect flexibility, performance etc. The proposed solutions are characterized by their multistage optical interconnection networks with reconfigurable shuffle patterns, where the reconfigurability is provided by micro-optical-electrical mechanical systems. More specifically, application-specific bottlenecks can be resolved by reconfiguring the interconnection network according to the current application demands. The benefits of the architectural solutions are confirmed by simulations that clearly show that the architectures can achieve high performance for both symmetric application characteristics and for several classes of asymmetric application characteristics. The final architectural solution is characterized by electronic packet-switches interconnected through an optical backplane, which is reconfigurable. Moreover, the thesis presents how several signal processing applications can be mapped to run concurrently in a time-shared scheme on a single reconfigurable multi-processor system that has high flexibility to adapt for the application currently at hand. The interconnection network is then adapted (reconfigured) according to the demands of the currently executed application in each time instance. The analysis shows that it is feasible to build such a system with today’s components.

This paper presents a way of building modular systems with a powerful optical interconnection network. Each module, placed on a Printed Circuit Board (PCB), has a generic optical communication interface with a simple electronic router. Together with optical switching using micro-electromechanical system (MEMS) technology, packet switching over reconfigurable topologies is possible. The interconnection system gives the possibility to integrate electronics with optics without changing existing PCB technology. Great interest from industry is therefore expected and the cost advantages are several: reuse of module designs, module upgrades without changing the PCB, low-cost conventional PCB technology, etc. In the version described in this paper, the interconnection system has 48 bidirectional optical channels for intra-PCB communication on each board. For inter-PCB communication, a backplane with 192 bidirectional optical channels supports communication between twelve PCBs. With 2.5 Gbit/s per optical channel in each direction, the aggregated intra-PCB bit rate is 120 Gbit/s full duplex (on each PCB) while the aggregated inter-PCB bit rate is 480 Gbit/s full duplex. A case study shows the feasibility of the interconnection system in a parallel processing system for radar signal processing.

An optoelectronic router with a shuffle exchange network is presented and enhanced by the addition of micro-optoelectromechanical systems (MOEMS) in the network to add the ability to reconfigure the shuffle network. The MOEMS described here are fully connected any-to-any crossbar switches. The added reconfigurability provides the opportunity to adapt the system to different common application characteristics. Two representative application models are described: The first has symmetric properties, and the second has asymmetric properties. The router system is simulated with the specified applications and an analysis of the results is carried out. By use of MOEMS in the optical network, and thus reconfigurability, greater than 50% increased throughput performance and decreased average packet delay are obtained for the given application. Network congestion is avoided throughout the system if reconfigurability is used.

Application specific architectures are highly desirable in embedded parallel computing systems at the same time as designers strive for using one embedded parallel computing platform for several applications. If this can be achieved, the cost can be decreased in comparison to using several different embedded parallel computing systems. This paper presents a novel approach of running several high-performance applications concurrently on one single parallel computing system. By using a reconfigurable backplane interconnection system, the applications can be run efficiently with high network flexibility since the interconnect network can be adapted to fit the application that is being processed for the moment. More precisely, this paper investigates how the space time adaptive processing (STAP) radar algorithm and the stripmap synthetic aperture radar (SAR) algorithm can be mapped on a multi-cluster processing system with a reconfigurable optical interconnection system realized by a micro-optical-electrical mechanical system (MOEMS) crossbars. The paper describes the reconfigurable platform, the two algorithms and how they individually can be mapped on the targeted multiprocessor system. It is also described how these two applications can be mapped simultaneously on the optical reconfigurable platform. Implications and requirements on communication bandwidth and processor performance in different critical points of the two applications are presented. The results of the analysis show that an implementation is feasible with today's MOEMS technology, and that the two applications can be successfully run in a time-sharing scheme, both at the processing side and at the access for interconnection bandwidth.

The performance requirements on data and telecommunication switches and routers are continuously increasing and it is evident that new ideas and architectures must come to light to satisfy these new demands. In this paper, a runtime reconfigurable modular design approach is presented, using state-of-the-art microoptical-electrical mechanical system (MOEMS) components. The paper introduces a novel field of reconfigurability, where reconfiguration is made on the system level instead of, e.g. fine-granularity reconfigurable logic. Different reconfigurable system solutions with support to adapt for asymmetric traffic patterns are proposed and compared to see how design choices affect flexibility, performance etc. The proposed solutions are characterized by their multistage networks with reconfigurable shuffle patterns.

A reconfigurable high performance multistage router architecture is presented and simulated. The router backbone network is an optical shuffle exchange network that has the power of reconfigurability through the use of micro-optical-electrical mechanical systems (MOEMS). The router is subjected to different application classes. The application classes have different characteristics in terms of symmetric/asymmetric traffic properties. We compare our reconfigurable shuffle-pattern for all three application classes for the specified router architecture.

A near-unity unselective absorption spectrum is desirable for high-performance photovoltaics. Nanowire arrays are promising candidates for efficient solar cells due to nanophotonic absorption resonances in the solar spectrum. The absorption spectra, however, display undesired dips between the resonance peaks. To achieve improved unselective broadband absorption, we propose to enclose distributed Bragg reflectors (DBRs) in the bottom and top parts of indium phosphide (InP) nanowires, respectively. We theoretically show that by enclosing only two periods of In_0.56Ga_0.44As/InPDBRs, an unselective 78% absorption efficiency (72% for nanowires without DBRs)is obtained at normal incidence in the spectral range from 300 nm to 920 nm. Under oblique light incidence, the absorption efficiency is enhanced up to about 85% at an incidence angle of 50º. By increasing the number of DBR periods from two to five, the absorption efficiency is further enhanced up to 95% at normal incidence. In this work we calculated optical spectra for InP nanowires, but the results are expected to be valid for other direct band gap III-V semiconductor materials. We believe that our proposed idea of integrating DBRs in nanowires offers great potential for high-performance photovoltaic applications. ©2018 Håkan Pettersson et al., published by De Gruyter, Berlin/Boston.

Engineering optical absorption in nanowires, over a desire range of wavelengths is of importance to design high-performance nanowire-based photovoltaics. To this end, we integrate the nanowires with distributed Bragg reflectors to enhance absorption spectra of the nanowires and relate the consequent enhancement to increasing the optical path lengths of the modes. © 2017 The Japan Society of Applied Physics.

Geometrically designed III-V nanowire arrays are promising candidates for optoelectronics due to their possibility to excite nanophotonic resonances in absorption spectra. Strong absorption resonances can be obtained by proper tailoring of nanowire diameter, length and pitch. Such enhancement of the light absorption is, however, accompanied by undesired resonance dips at specific wavelengths. In this work, we theoretically show that tilting of the nanowires mitigates the absorption dips by exciting strong Mie resonances. In particular, we derive a theoretical optimum inclination angle of about 30 degrees at which the inclined nanowires gain 8% in absorption efficiency compared to vertically standing nanowires in a spectral region matching the intensity distribution of the sun. The enhancement is due to engineering the excited modes inside the nanowires regarding the symmetry properties of the nanowire/light system without increasing the absorbing material. We expect our results to be important for nanowire-based photovoltaic applications. © 2017 SPIE.

Geometrically designed III-V nanowire arrays are promising candidates for disruptive optoelectronics due to the possibility of obtaining a strongly enhanced absorption resulting from nanophotonic resonance effects. With normally incident light on such vertical nanowire arrays, the absorption spectra exhibit peaks that originate from excitation of HE1m waveguide modes in the constituent nanowires. However, the absorption spectra typically show dips between the absorption peaks. Conventionally, such weak absorption has been counteracted by either making the nanowires longer or by decreasing the pitch of the array, both alternatives effectively increasing the volume of absorbing material in the array. Here, we first study two approaches for compensating the absorption dips by exciting additional Mie resonances: 1) oblique light incidence on vertical InP nanowire arrays and 2) normal light incidence on inclined InP nanowire arrays. We then show that branched nanowires offer a novel route to achieve broadband absorption by taking advantage of simultaneous excitations of Mie resonances in the branches and guided HE1m modes in the stem. Finite element method calculations show that the absorption efficiency is enhanced from 0.72 for vertical nanowires to 0.78 for branched nanowires under normal light incidence. Our work provides new insight for the development of novel efficient photovoltaics with high efficiency and reduced active material volume.

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson's disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD. © 2020 Somayeh Aghanavesi et al.

Bluetooth Low Energy (BLE) is one of the most important technologies that feed the growing field of Internet of Things and Wireless Sensor Networks. Due to its flexibility and unique low power-consumption, an increasing number of industrial devices, household appliances and wearables are being designed using it. However, the real-time demands of these networks such as timing and Quality of Service are not fully covered by the protocol itself. To help improve and offer some control over these characteristics, this paper presents a time slot transmission scheme with packet prioritization. It is based on the division and allocation of the connection interval to two types of messages: real-time and ordinary. The goal is to offer the lowest packet loss and time guarantees for real-time messages, while providing acceptable throughput for ordinary ones. Since the probability of a BLE connection to close increases with the number of packets sent through it, the position where a real-time packet is being sent as well as the number of ordinary messages in a connection represent key factors. The use of the first and last slot for real-time packets with ordinary flow restricted to the space between them decreases the transmission delay uncertainty and allows probability tuning based on the number of ordinary messages. Simulations were performed using the proposed scheme and a reduction of more than 100 times in the delay variance was observed for real-time transmissions. Regarding reliability, around 5% of the packets were lost for a bit error rate of 10⁻³. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

We present a survey of the recent research efforts in integrating model learning with model-based testing. We distinguished two strands of work in this domain, namely test-based learning (also called test-based modeling) and learning-based testing. We classify the results in terms of their underlying models, their test purpose and techniques, and their target domains. © Springer International Publishing AG

Conditional Poisson Sampling and Pareto pips Sampling designs are sampling methods with fixed sample size and with inclusion probabilities proportional to given size measures.. Algorithms were introduced to calculate first and second exact inclusion probabilities for both schemes. Methods were also provided to adjust the parameters to get predetermined inclusion probabilities. In this paper, the Fortran procedures are introduced and documented. Moreover, guidelines are provided for their use as well as examples and the programs codes commented.

The advancement of digital technologies is driving traditional product oriented businesses to move from selling product to selling solutions. In order to exploit business potentials from technology, it is important to understand the potential of the technology and how it influences the value creation in a digital environment where it is developed and to be deployed. To understand this changing environment, I have studied remote diagnostics as an example of digital technology within the vehicle industry as a source of new value for business. The findings show that digitized products not only helps to solve existing problems in providing services but also provides potential to generate value for new products and services in the networked environment. An implication from the findings for product oriented firms is that value creation with digitized products requires new business processes. Based on the findings, a discussion is presented about how generativity influences value creation via value in design, governance and networking in a digital ecosystem.

The advancement in digital technologies drives industries to change their core businesses which present number of challenges. One of them is transformation of manufacturing industries towards servitization. This paper reports empirical findings from an on-going project related to servitization in the age of digital innovation. The research study is in the context of the vehicle industry and concerns e-maintenance services based on remote diagnostics technologies. It explores the servitization of vehicle industry in the age of digital innovation. Such innovations embark challenges to innovation environment. A study in this regard has been conducted and it revealed the challenges related to the design, organizational strategy and organizational transformation.

The advancement of digital technologies is driving traditional product oriented businesses to move from selling product to selling solutions. In order to exploit business potentials from technology, it is necessary to understand the technological capacity and how it influences the value creation in the environment where it is deployed. I study remote diagnostics as an example of digital technology within the vehicle industry. I found that this technology provides potential to generate new value. To create this value, traditional product oriented organizations are required to create value in different way than traditional mode of creating value. In this paper, I show that how generative capacity of remote diagnostics technology creates value in digital ecosystem.

Advancement in digital technology is rapidly changing the contemporary landscape of business and associated networks for manufacturing firms. Many traditional physical products are now being embedded with digital components, providing them digital capability to become digitized products. The digitization of physical products has become an important driver for digital service innovation within manufacturing industries. Such digital service innovation transforms value networks of manufacturing firms in various industries. While digitization of products and digital service innovation can be observed in many manufacturing industries, this thesis focuses on the transformation of value networks within the vehicle industry.

This thesis is a collection of papers and a cover paper. The thesis reports from a collaborative project in the vehicle industry. The project explored new digital services for vehicles based on remote diagnostics technology. The exploration and conceptualization of digital services is investigated in a collaborative manner with participants from the vehicle industry. The results reflect that there is a paradigm shift for manufacturing firms digitizing their products, and stretching the business scope from product to solution oriented business.

This thesis contributes to the existing literature on digital innovation with insights on the transformation of value networks in the vehicle industry. The research question addressed in this thesis is: How are value networks of manufacturing firms transformed by digital service innovation? To answer the question, this thesis conceptualizes how the value creating pattern of digitized products transforms value networks of manufacturing firms. A model is presented that reflects how the symbiotic value relationship between the digitized product and digital services transforms the roles, relationships and exchanges in the value networks of manufacturing firms. The model can serve as an analytical tool to further advance the knowledge on business aspects in digital innovation. This thesis contributes to practice by providing an understanding of how manufacturing firms can leverage value of digitized products and digital services in value networks.

Incumbent manufacturing firms face challenges when expanding their product focus with digital services. Such expansion creates tensions in organizations in the servitization process. While management visions and conceptualizes new service oriented businesses, the actual practice of implementing these service concepts is influenced by the product paradigmatic way of thinking in the organization. This dominant thinking creates tensions between business visions and business practice. We use the case of remote diagnostics services to provide insights into a manufacturing firm’s attempt to transform the dominant oriented business models into a new networked environment. We suggest that such acts that may or may not lead to transition are lingered by dominant logics related to the product focus. This indicates that firms are required to embed new logics into their existing practice in order to exploit the full potential of digital technology. © 2014 IEEE.

This paper reports from preparations in an ongoing research study concerning how digital service innovation transforms value networks in manufacturing industries. The research study is in the context of the vehicle industry and concerns digital e-maintenance services based on remote diagnostics systems. This digital service innovation in particular is of great importance since manufacturing industries have great potential to expand their business and found new and extended boundaries and relationships with other stakeholder in a network they are attached to. Core challenges and opportunities for digital service innovation will lead us to the study of its influence on the business and innovation environment i.e. the value network. This paper presents a framework to study how digital service innovation transforms value networks based on literature reviews on value network, digital innovation and transformation of value networks.

This paper reports from preparations in an ongoing research study concerning how digital service innovation transforms value networks in the vehicle industry. The research study concerns digital services based on remote diagnostics systems. This digital service innovation in particular is of great importance since the vehicle industry has great potential to expand its business and found new and extended boundaries and relationships with other stakeholder in the networks they are attached to. Core challenges and opportunities for digital service innovation will lead us to the study of its influence on the business and innovation environment i.e. the value network. In this paper, we propose three propositions to study transformation from product oriented value networks to digital service oriented value networks.

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, i.e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack of annotated point cloud data, in particular for the road segments, we introduce an auto-labeling process which transfers automatically generated labels from the camera to LiDAR. We also explore the role of imagelike projection of LiDAR data in semantic segmentation by comparing BEV with spherical-front-view projection and show that SalsaNet is projection-agnostic. We perform quantitative and qualitative evaluations on the KITTI dataset, which demonstrate that the proposed SalsaNet outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. Our code and data are publicly available at https://gitlab.com/aksoyeren/salsanet.git. 

Deep Neural Networks have long been considered black box systems, where their interpretability is a concern when applied in safety critical systems. In this work, a novel approach of interpreting the decisions of DNNs is proposed. The approach depends on exploiting generative models and the interpretability of their latent space. Three methods for ranking features are explored, two of which depend on sensitivity analysis, and the third one depends on Random Forest model. The Random Forest model was the most successful to rank the features, given its accuracy and inherent interpretability. 

The three dimensional structure tensor algorithm (3D-STA) is often used in image processing applications to compute the optical flow or to detect local 3D structures and their directions. This algorithm is computationally expensive due to many computations that are required to calculate the gradient, the tensor, and to smooth every pixel of the image frames. Therefore, it is important to parallelize the implementation to achieve high performance. In this paper we present two parallel implementations of 3D-STA; namely moderately parallelized and highly parallelized implementation, on a massively parallel reconfigurable array. Finally, we evaluate the performance of the generated code and results are compared with another optical flow implementation. The throughput achieved by the moderately parallelized implementation is approximately half of the throughput of the Optical flow implementation, whereas the highly parallelized implementation results in a 2x gain in throughput as compared to the optical flow implementation. © 2012 IEEE.

This master thesis is about providing the implementation of synthesis, detection of spiral symbols and estimating the pan/tilt angle and position by using camera calibration. The focus is however on the latter, the estimation of parameters of localization.

Spiral symbols are used to be able to give an object an identity as well as to locate it. Due to the spiral symbol´s characteristic shape, we can use the generalized structure tensor (GST) algorithm which is particularly efficient to detect different members of the spiral family. Once we detect spirals, we know the position and identity parameters of the spirals within an apriori known geometric configuration (on a sheet of paper). In turn, this information can be used to estimate the 3D-position and orientation of the object on which spirals are attached using a camera calibration method.

This thesis provides an insight into how automatic detection of spirals attached on a sheet of paper, and from this, automatic deduction of position and pose parameters of the sheet, can be achieved by using a network camera. GST algorithm has an advantage of running the processes of detection of spirals efficiently w.r.t detection performance and computational resources because it uses a spiral image model well adapted to spiral spatial frequency characteristic. We report results on how detection is affected by zoom parameters of the network camera, as well as by the GST parameters; such as filter size. After all spirals centers are located and identified w.r.t. their twist/bending parameter, a flexible technique for camera calibration, proposed by Zhengyou Zhang implemented in Matlab within the present study, is performed. The performance of the position and pose estimation in 3D is reported.

The main conclusion is, we have reasonable surface angle estimations for images which were taken by a WLAN network camera in different conditions such as different illumination and different distances. 

The acquisition of data from mobile phones have been a mainstay of criminal digital forensics for a number of years now. However, this forensic acquisition is getting more and more difficult with the increasing security level and complexity of mobile phones (and other embedded devices). In addition, it is often difficult or impossible to get access to design specifications, documentation and source code. As a result, the forensic acquisition methods are also increasing in complexity, requiring an ever deeper understanding of the underlying technology and its security mechanisms. Forensic acquisition techniques are turning to more offensive solutions to bypass security mechanisms, through security vulnerabilities. Common Criteria mode is a security feature that increases the security level of Samsung devices, and thus make forensic acquisition more difficult for law enforcement. With no access to design documents or source code, we have reverse engineered how the Common Criteria mode is actually implemented and protected by Samsung's secure bootloader. We present how this security mode is enforced, security vulnerabilities therein, and how the discovered security vulnerabilities can be used to circumvent Common Criteria mode for further forensic acquisition. © 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS.

This paper describes the implementation of a cross-platform software application to aid ambulance paramedics during CPR (Cardio-Pulmonary Resuscitation). It must be able to work both on iOS and Android devices, which are the leading platforms in the mobile industry. The goal of the application is to guide paramedics in the different processes and expected medication to be administered during a cardiac arrest, a scenario that is usually stressful and fast-paced, thus prone to errors or distractions. The tool must provide timely reminders of the different actions to be performed during a cardiac arrest, and in an appropriate order, based on the results of the previous actions. A timer function will also control the duration of each step of the CPR procedure. The application is implemented in React Native which, using JavaScript as programming language, allows to deploy applications that can run both in iOS and Android native languages. Our solution could also serve as a record of events that could be transmitted (even in real-time) to the hospital without demanding explicit verbal communication of the procedures or medications administered to the patient during the ambulance trip. This would provide even higher efficiency in the process, and would allow automatic incorporation of the events to the medical record of the patient as well. © 2021 IEEE.

Capturing physical phenomena such as node mobility or wave propagation is challenging in current network simulators, and is mostly achieved through crude abstractions. Despite being operationally efficient, such abstractions adversely affect simulation credibility. To realize more accurate modeling, we are currently developing a simulation environment integrating a hybrid modeling language into a mainstream network simulator. This paper gives a preliminary overview of our efforts. For illustration, an example simulation scenario with some basic mobility is described. © 2014 IEEE.

Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

Human activity recognition as an engineering tool as well as an active research field has become fundamental to many applications in various fields such as health care, smart home monitoring and surveillance. However, delivering sufficiently robust activity recognition systems from sensor data recorded in a smart home setting is a challenging task. Moreover, human activity datasets are typically highly imbalanced because generally certain activities occur more frequently than others. Consequently, it is challenging to train classifiers from imbalanced human activity datasets. Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. This paper proposes a data level perspective combined with a temporal window technique to handle imbalanced human activities from smart homes in order to make the learning algorithms more sensitive to the minority class. The experimental results indicate that handling imbalanced human activities from the data-level outperforms algorithms level and improved the classification performance. © The Author(s) 2020

Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-care, smart home monitoring, and surveillance. Existing approaches for activity recognition in smart homes have achieved promising results. Most of these approaches evaluate real-time recognition of activities using only sensor activations that precede the evaluation time (where the decision is made). However, in several critical situations, such as diagnosing people with dementia, “preceding sensor activations” are not always sufficient to accurately recognize the inhabitant's daily activities in each evaluated time. To improve performance, we propose a method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made. For this, the proposed method uses multiple incremental fuzzy temporal windows to extract features from both preceding and some oncoming sensor activations. The proposed method is evaluated with two temporal deep learning models (convolutional neural network and long short-term memory), on a binary sensor dataset of real daily living activities. The experimental evaluation shows that the proposed method achieves significantly better results than the real-time approach, and that the representation with fuzzy temporal windows enhances performance within deep learning models. © Copyright 2020 IEEE

Chattering is a fundamental phenomenon that is unique to hybrid systems, due to the complex interaction between discrete dynamics (in the form of discrete transitions) and continuous dynamics (in the form of time). In practice, simulating chattering hybrid systems is challenging in that simulation effectively halts near the chattering time point, as an infinite number of discrete transitions would need to be simulated. In this paper, formal conditions are provided for when the simulated models of hybrid systems display chattering behavior, and methods are proposed for avoiding chattering “on the fly” in runtime. We utilize dynamical behavior analysis to derive conditions for detecting chattering without enumeration of modes. We also present a new iterative algorithm to allow for solutions to be carried past the chattering point, and we show by a prototypical implementation how to generate the equivalent chattering-free dynamics internally by the simulator in the main simulation loop. The concepts are illustrated with examples throughout the paper. © 2016 IEEE.

A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25 Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10's of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10-100's ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceiver's responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors. © IEEE

This work presents the temporal analysis for a distributed differential control system for a mobile robot deployed on a wireless network using the SunSPOT platform. The system is programmed in Java and each of its part is executed in a distinct processor which cooperates via a wireless network. The communication between the network nodes is made by remote procedure calls, which are implemented by a minimum version of the Java RMI (mRMI), presented in this work. Simulation results are compared to experimental data acquired by the deployment of the system on real devices, the SunSPOTs. The comparisons reveal that the distributed solution presents fairly good results besides the inserted errors due to the wireless communication. © 2011 IEEE.

Information Visualization applications are dealing with fundamental difficulties related to overlap in cognitive models between designers and users, goal ambiguity, and accuracy in search strategies These difficulties are more obvious in applications aimed at reducing information overload by general users, than in applications aimed at visualising scientific data. General users have very likely quite different cognitive reference for approaching an abstract complex task, than designers. This can result in designs which can unintentionally increase information overload by users. In visualisation of scientific data the overlap of cognitive reference between specialists and designs is very likely much higher, but we need methods which can facilitate data exploration in real-time interaction. One possibility to facilitate exploration in a more or less systematic way is by means of metaphors which can support human perception in searching for patterns.

Embedded Intelligent Systems (EIS) is the joint research field of the four collaborating laboratories at the School of Information Science, Computer and Electrical Engineering (IDE) at Halmstad University. The research of the four labs is integrated into a strong concerted research environment within embedded systems (EIS) - with a perspective reaching from the enabling technology via new system solutions and intelligent applications to end user aspects and business models. It is an expanding research area with many applications, not least ones that exist in everyday life.EIS is an important research environment contributing to the regional Triple Helix innovation system Healthcare Technology which the region has pointed out as a prioritised development sector. With its strong connections to both established and new, expanding firms hived off from the university, the research environment is active in the Healthcare Technology Alliance, a network of around sixty companies, counties and health care providers in south-western Sweden with the aim of developing the region into a leading arena for the development of health technology products and services. Several projects together with these participants concern both research and technology transfer.An integrated gender and gender equality perspective in innovations within the health technology area is necessary in order to be able to meet the needs of an ageing population with quality innovations. The relevancy of a gender perspective is clear in relation to the fact that about 70% of all those older than 75 years are women. Older women are on average cared for in hospital twice as long as men, partly due to differing disease panoramas, but also because men are more often cared for in the home by a woman while the women who live longer more often live alone. With the expansion of home-help and home nursing new needs follow and it is likely that a gender perspective will become necessary for the development of products and services that can make daily life easier for the elderly. The gender perspective also has relevance from the point of view of care staff. New technology is developed for application within the health and care sector where the larger professional groups consist mainly of women. The technology, most often designed by men, is used by women. With this in mind it is clear that an important aspect of good innovations is that the end users are involved in the innovation process.Based on an awareness of the need for a more articulated gender perspective within the research environment, in order to meet the needs expressed above, an application for a gender inclusive R&D project was handed in to the VINNOVA programme Applied Gender Research in Strong Research and Innovation Environments. The G-EIS project (Gender Perspective on Embedded Intelligent Systems - Application in Healthcare Technology) was approved and started in 2009. The project involves researchers from the EIS research environment as well as representatives from companies and the public sector.The project participants are on the whole agreed on the need for a gender perspective in the R&I environment, but struggle with the meeting of two epistemologically opposed theories of science. The understanding within gender studies that research and production both create reality and are informed by it is not always accepted within the areas of natural science. Engineering and other technological sciences not only consider aspects of science to be separate from reality, but also seek positivistic proof in research, something not always possible in the more qualitative research of the social sciences. Researching how these two perspectives meet within this specific project is the topic of this paper.

Ubiquitous and real-time person authentication has become critical after the breakthrough of all kind of services provided via mobile devices. In this context, face technologies can provide reliable and robust user authentication, given the availability of cameras in these devices, as well as their widespread use in everyday applications. The rapid development of deep Convolutional Neural Networks (CNNs) has resulted in many accurate face verification architectures. However, their typical size (hundreds of megabytes) makes them infeasible to be incorporated in downloadable mobile applications where the entire file typically may not exceed 100 Mb. Accordingly, we address the challenge of developing a lightweight face recognition network of just a few megabytes that can operate with sufficient accuracy in comparison to much larger models. The network also should be able to operate under different poses, given the variability naturally observed in uncontrolled environments where mobile devices are typically used. In this paper, we adapt the lightweight SqueezeNet model, of just 4.4MB, to effectively provide cross-pose face recognition. After trained on the MS-Celeb-1M and VGGFace2 databases, our model achieves an EER of 1.23% on the difficult frontal vs. profile comparison, and 0.54% on profile vs. profile images. Under less extreme variations involving frontal images in any of the enrolment/query images pair, EER is pushed down to <0.3%, and the FRR at FAR=0.1% to less than 1%. This makes our light model suitable for face recognition where at least acquisition of the enrolment image can be controlled. At the cost of a slight degradation in performance, we also test an even lighter model (of just 2.5MB) where regular convolutions are replaced with depth-wise separable convolutions. © 2021, Springer Nature Switzerland AG.