Logarithmic number system (LNS) is an attractive alternative to realize finite-length impulse response filters because ofmultiplication in the linear domain being only addition in the logarithmic domain. In the literature, linear coefficients are directlyreplaced by the logarithmic equivalent. In this paper, an approach to directly optimize the finite word length coefficients in theLNS domain is proposed. This branch and bound algorithm is implemented based on LNS integers and several different branchingstrategies are proposed and evaluated. Optimal coefficients in the minimax sense are obtained and compared with the traditionalfinite word length representation in the linear domain as well as using rounding. Results show that the proposed method naturallyprovides smaller approximation error compared to rounding. Furthermore, they provide insights into finite word length propertiesof FIR filters coefficients in the LNS domain and show that LNS FIR filters typically provide a better approximation error comparedto a standard FIR filter.

The most challenging step of implementing particle filtering is the resampling step which replicates particles with large weights and discards those with small weights. In this paper, we propose a generic architecture for resampling which uses double multipliers to avoid normalization divisions and make the architecture  equally efficient for non-powers-of-two number of particles. Furthermore, the complexity of resampling is greatly affected by the size of memories used to store weights. We illustrate that by storing the original weights instead of their cumulative sum and calculating them online reduces the total complexity, in terms of area, ranging from 21% to 45%, while giving up to 50% reduction in memory usage.

The complexity of narrow transition band FIR filters is highand can be reduced by using frequency response masking (FRM) techniques. Thesetechniques use a combination of periodic model filters and masking filters. Inthis paper, we show that time-multiplexed FRM filters achieve lowercomplexity, not only in terms of multipliers, but also logic elements compared to time-multiplexed singlestage filters. The reduced complexity also leads to a lower power consumption. Furthermore, we show that theoptimal period of the model filter is dependent on the time-multiplexing factor.

Frequency-response masking (FRM) is a set of techniques for lowering the computational complexity of narrow transition band FIR filters. These FRM use a combination of sparse periodic filters and non-sparse filters. In this work we consider the implementation of these filters in a time-multiplexed manner on FPGAs. It is shown that the proposed architectures produce lower complexity realizations compared to the vendor provided IP blocks, which do not take the sparseness into consideration. The designs are implemented on a Virtex-6 device utilizing the built-in DSP blocks.

The most challenging aspect of particle filtering hardware implementation is the resampling step which replicates particles with large weights and discards those with small weights because it has a high latency and can only be partially executed in parallel with the other steps of particle filtering. To reduce the latency, an improved resampling scheme is proposed in this work which involves pre-fetching from the weight memory in parallel to the fetching of a value from a random function generator. Architectures for realizing the pre-fetch technique are also proposed. The trade-off between the latency reduction achieved by increasing the size of the pre-fetch memory and the architectural implementation complexity has been analyzed. Results show that a pre-fetch of five achieves the best area-latency trade-off while on average achieving an 85% reduction in the latency.

We also propose a generic double multiplier architecture for resampling which avoids normalization divisions and makes the architecture equally efficient for non-powers-of-two number of particles as well as removes the need of explicitly ordering the random values for efficient multinomial resampling implementation. It is further improved by computing the cumulative sum of weights on-the-fly which helps in reducing the size of the weight memories by up to 50%.

The complexity of narrow transition band finite-length impulse response (FIR) filters is high and can be reduced by using frequency-response masking (FRM) techniques. These techniques use a combination of periodic model and, possibly periodic, masking filters. Time-multiplexing is in general beneficial since only rarely does the technology bound maximum obtainable clock frequency and the application determined required sample rate correspond. Therefore, architectures for time-multiplexed FRM filters that benefit from the inherent sparsity of theperiodic filters are introduced in this work.

We show that FRM filters not only reduces the number of multipliers needed, but also have benefits in terms of memory usage. Despite the total amount of samples to be stored is larger for FRM, it results in fewer memory resources needed in FPGAs and more energy efficient memory schemes in ASICs. In total, the power consumption is significantly reduced compared to a single stage implementation. Furthermore, we show that the choice of the interpolation factor which gives the least complexity for the periodic model filter and subsequent masking filter(s) is a function of the time-multiplexing factor, meaning that the minimum number of multipliers not always correspond to the minimum number of multiplications. Both single-port and dual-port memories are considered and the involved trade-off in number of multipliers and memory complexity is illustrated. The results show that for FPGA implementation, the power reduction ranges from 23% to 68% for the considered examples.

Det här examensarbetet studerar problemet med parameteridentifiering för systembiologi. Två metoder har studerats. Metoden med intervallanalys använder union av intervallvektorer som klass av objekt för att manipulera och bilda inre och yttre approximationer av kompakta mängder. Denna metod fungerar väl för modeller givna som ett system av differentialekvationer, men har sina begränsningar, eftersom det analytiska uttrycket för lösningen till differentialekvationen som är nödvändigt att känna till för att kunna formulera inkluderande funktioner, inte alltid är tillgängliga. Den andra studerade metoden, använder SDP-relaxering, som ett sätt att komma runt problemet med olinjäritet och icke-konvexitet i systemet. Denna metod, implementerad i toolboxen bio.SDP, utgår från system av differensekvationer, framtagna via Eulers diskretiserings metod. Diskretiseringsmetoden innehåller fel och osäkerhet, vilket gör det nödvändigt att estimera en gräns för felets storlek. Några felestimeringsmetoder har studerats. Metoden med ∞-norm optimering, också kallat worst-case-∞-norm är tillämpat på ett-stegs felestimerings metoder.

Metoderna har illustrerats genom att lösa två system biologiska problem och de accepterade parametermängderna, benämnt SCP, har jämförts och diskuterats.

The so-called He-3-crisis has motivated the neutron detector community to undertake an intense Ramp;D programme in order to develop technologies alternative to standard He-3 tubes and suitable for neutron detection systems in future spallation sources such as the European spallation source (ESS). Boron-based GEM (gas electron multiplier) detectors are a promising He-3-free technology for thermal neutron detection in neutron scattering experiments. In this paper the evolution of boron-based GEM detectors from planar to 3D converters with an application in diffraction measurements is presented. The use of 3D converters coupled with GEMs allows for an optimization of the detector performances. Three different detectors were used for diffraction measurements on the INES instrument at the ISIS spallation source. The performances of the GEM-detectors are compared with those of conventional He-3 tubes installed on the INES instrument. The conceptual detector with the 3D converter used in this paper reached a count rate per unit area of about 25% relative to the currently installed He-3 tube. Its timing resolution is similar and the signal-to-background ratio (S/B) is 2 times lower.

Mines, construction sites, road construction and quarries are examples of applications where construction equipment are used. In a production chain consisting of several construction machines working together, the work needs to be optimised and coordinated to achieve an environmental friendly, energy efficient and productive production. Recent rapid development within positioning services, telematics and human machine interfaces (HMI) opens up for control of individual machines and optimisation of transport missions where several construction machines co-operate.

The production chain on a work site can be split up in different sub-tasks of which some can be transport missions. Taking off in a transport mission where one wheel loader ("loader" hereinafter) and two articulated haulers ("haulers" hereinafter) co-operate to transport material at a set production rate [ton/h], a method for fuel optimal control is developed. On the mission level, optimal cycle times for individual sub-tasks such as wheel loader loading, hauler transport and hauler return, are established through the usage of Pareto fronts.

The haulers Pareto fronts are built through the development of a Dynamic Programming (DP) algorithm that trades fuel consumption versus cycle time for a road stretch by means of a time penalty constant. Through varying the time penalty constant n number of times, discrete fuel consumption - cycle time values can be achieved, forming the Pareto front. At a later stage, the same DP algorithm is used to generate fuel optimal vehicle speed and gear trajectories that are used as control signals for the haulers. Input to the DP algorithm is the distance to be travelled, road inclination, rolling resistance coefficient and a max speed limit to avoid unrealistic optimisation results.

Thus, a method to describe the road and detect the road related data is needed to enable the optimisation. A map module is built utilising an extended Kalman Filter, Rauch-Tung-Striebel smoother and sensor fusion to merge data and estimate parameters not observable by sensors. The map module uses a model of the vehicle, sensor signals from a GPS or GNSS sensor and machine sensors to establish a map of the road.

The wheel loader Pareto front is based on data developed in previous research combined with Volvo in-house data. The developed optimisation algorithms are implemented on a PC and in an interactive computer tablet based system. A human machine interface is created for the tablet, guiding the operators to follow the optimal control signals, which is speed for the haulers and cycle time for the loader. To evaluate the performance of the system it is tested in real working conditions.

The contributions develop algorithms, set up a demo mission control system and carry out experiments. Altogether rendering in a platform that can be used as a base for a future design of an off-road transport mission control system.

This report is about control, design and implementation of a low voltage-fed quasi Z-source three-level inverter. The topology has been interesting for photovoltaic-systems due to its ability to boost the incoming voltage without needing an extra switching control. The topology was first simulated in Simulink and later implemented on a full-bridge module to measure the harmonic distortion and estimating the power losses of the inverter. An appropriate control scheme was used to set up a shootthrough and design a three-level inverter. The conclusion for the report is that the quasi Z-source inverter could boost the DC-link voltage in the simulation. But there should be more consideration to the internal resistance of the components for the implementation stage as it gave out a lower output voltage than expected. 

Loggar är en viktig del av alla system, det ger en inblick i vad som sker. Att analysera loggar och extrahera väsentlig information är en av de största trenderna nu inom IT-branchen. Informationen i loggar är värdefulla resurser som kan användas för att upptäcka anomalier och hantera dessa innan det drabbar användaren.

I detta examensarbete dyker vi in i grunderna för informationssökning och analysera undantagsutskrifter i loggar från COSMIC för att undersöka om det är möjligt att upptäcka anomalier med hjälp av retrospektivdata. Detta examensarbete ger även en inblick i möjligheten att visualisera data från loggar och erbjuda en kraftfull sökmotor. Därför kommer vi att fördjupa oss i de tre välkända program som adresserar frågorna i centraliserad loggning: Elasticsearch, Logstash och Kibana.

Sammanfattningsvis visar resultatet att det är möjligt att upptäckta anomalier genom att tillämpa statistiska metoder både på retrospektiv- och realtidsdata.

A nonlinear mean value engine model (MVEM) of a two-stroke turbocharged marine diesel engine is developed, parameterized and validated against measurement data. The goal is to have a computationally fast and accurate engine model that captures the main dynamics and can be used in the development of control systems for the newly introduced EGR system. The tuning procedure used is explained, and the result is a six-state MVEM with seven control inputs that capture the main system dynamics.

In this work, WS2 nanowire-nanoflake hybrids are synthesized by the sulfurization of hydrothermally grown WO3 nanowires. The influence of temperature on the formation of products is optimized to grow WS2 nanowires covered with nanoflakes. Current-voltage and resistance-temperature measurements carried out on random networks of the nanostructures show nonlinear characteristics and negative temperature coefficient of resistance indicating that the hybrids are of semiconducting nature. Bottom gated field effect transistor structures based on random networks of the hybrids show only minor modulation of the channel conductance upon applied gate voltage, which indicates poor electrical transport between the nanowires in the random films. On the other hand, the photo response of channel current holds promise for cost-efficient solution process fabrication of photodetector devices working in the visible spectral range.

Drone usage is increasing, both in recreational use and in the industry. With it comes a number of problems to tackle. Primarily, there are certain areas in which flying drones pose a security threat, e.g., around airports or other no-fly zones. Other problems can appear when there are drones in mobile networks which can cause interference. Such interference comes from the fact that radio transmissions emitted from drones can travel more freely than those from regular UEs (User Equipment) on the ground since there are few obstructions in the air. Additionally, the data traffic sent from drones is often high volume in the form of video streams. The goal of this thesis is to identify so-called "rogue drones" connected to an LTE network. Rogue drones are flying drones that appear to be regular UEs in the network. Drone identification is a binary classification problem where UEs in a network are classified as either a drone or a regular UE and this thesis proposes machine learning methods that can be used to solve it. Classifications are based on radio measurements and statistics reported by UEs in the network. The data for the work in this thesis is gathered through simulations of a heterogenous LTE network in an urban scenario. The primary idea of this thesis is to use a type of cascading classifier, meaning that classifications are made in a series of stages with increasingly complex models where only a subset of examples are passed forward to subsequent stages. The motivation for such a structure is to minimize the computational requirements at the entity making the classifications while still being complex enough to achieve high accuracy. The models explored in this thesis are two-stage cascading classifiers using decision trees and ensemble learning techniques. It is found that close to 60% of the UEs in the dataset can be classified without errors in the first of the two stages. The rest is forwarded to a more complex model which requires more data from the UEs and can achieve up to 98% accuracy. 

In the automotive industry today, embedded systems have reached a level of complexity which is not maintainable with the traditional approach of design- ing automotive embedded systems. For this purpose, many of the worlds leading automotive manufacturers have formed an alliance to apprehend this problem. This has resulted in AUTOSAR, an open standardized architecture for automotive embedded systems, which strives for increased flexibility and safety regulations. This thesis will explore the possibilities of implementing a CAN Communication stack using the AUTOSAR architecture and its corresponding methodology. As a result of this thesis, a complete AUTOSAR CAN communication stack has been implemented, as well has a simulator application with the purpose of testing its functionality. 

As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications.

This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm.

This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.

The goals for most of today’s receiver system are sampling at high-speed, with high resolution and with as few errors as possible. This master thesis describes the design of a high-speed sampling system with"state-of-the-art"components available on the market. The system is designed with a parallel Analog-to-digital converter (ADC) architecture, also called time interleaving. It aims to increase the sampling speed of the system. The system described in this report uses four 12-bits ADCs in parallel. Each ADC can sample at 125 MHz and the total sampling speed will then theoretically become 500 Ms/s. The system has been implemented and manufactured on a printed circuit board (PCB). Up to four boards can be connected in parallel to get 2 Gs/s theoretically.

In an approach to increase the systems performance even further, a timing error estimation algorithm will be used on the sampled data. This algorithm estimates the timing errors that occur when sampling with non-uniform time interval between samples. After the estimations, the sampling clocks can be adjusted to correct the errors.

This thesis is concerning some ADC theory, system design and PCB implementation. It also describes how to test and measure the system’s performance. No measurement results are presented in this thesis because measurements will be done after this project. The last part of the thesis discusses future improvementsto achieve even higher performance.

The purpose of a heat pump is to control the temperature of an enclosed space. This is done by using heat exchange with a heat source, for example water, air, or ground. In the air source heat pump that has been studied during this master thesis, a refrigerant exchanges heat with the outdoor air and with a water distribution system.

The heat pump is controlled through the circuit containing the refrigerant and it is therefore crucial that this circuit is functional. To ensure this, a diagnosis system has been created, to be able to detect and isolate sensor errors. The diagnosis system is based on mathematical models of the refrigerant circuit with its main components: a compressor, an expansion valve, a plate heat exchanger, an air heat exchanger, and a four-way valve. Data has been collected from temperature- and pressure sensors on an air source heat pump. The data has then been divided into data for model estimation and data for model validation. The models are used to create test quantities, which in turn are used by a diagnosis algorithm to determine whether an error has occurred or not.

There are nine temperature sensors and two pressure sensors on the studied air source heat pump. Four fault modes have been investigated for each sensor: Stuck, Offset, Short circuit and Open circuit. The designed diagnosis system is able to detect all of the investigated error modes and isolate 40 out of 44 single errors. However, there is room for improvement by constructing more test quantities to detect errors and decouple more fault modes. To further develop the diagnosis system, the existing models can be improved and new models can be created.

Radiation-hardened processors are designed to be resilient against soft errorsbut such processors are slower than Commercial Off-The-Shelf (COTS)processors as well significantly costlier. In order to mitigate the high costs,software techniques such as task re-executions must be deployed together withadequately hardened processors to provide reliability. This leads to a huge designspace comprising of the hardening level of the processors and the numberof re-executions of each task in the system. Each configuration in this designspace represents a tradeoff between processor load, reliability and costs.

The reliability comes at the price of higher costs due to higher levels of hardeningand performance degradation due to hardening or due to re-executions.Thus, the tradeoffs between performance, reliability and costs must be carefullystudied. Pertinent questions that arise in such a design scenario are — (i)how many times a task must be re-executed and (ii) what should be hardeninglevel? — such that the system reliability is satisfied.

In order to evaluate such tradeoffs efficiently, in this thesis, we proposenovel framework that harnesses the computational power of Graphics ProcessingUnits (GPUs). Our framework is based on a system failure probabilityanalysis that connects the probability of failure of tasks to the overall systemreliability. Based on characteristics of this probabilistic analysis as well asreal-time deadlines, we derive bounds on the design space to prune infeasiblesolutions. Finally, we illustrate the benefits of our proposed framework withseveral experiments

In this study, a potentiometric urea biosensor has been fabricated on glass filter paper through the immobilization of urease enzyme onto chitosan/cobalt oxide (CS/Co3O4) nanocomposite. A copper wire with diameter of 500 mu m is attached with nanoparticles to extract the voltage output signal. The shape and dimensions of Co3O4 magnetic nanoparticles are investigated by scanning electron microscopy and the average diameter is approximately 80-100 nm. Structural quality of Co3O4 nanoparticles is confirmed from X-ray powder diffraction measurements, while the Raman spectroscopy has been used to understand the chemical bonding between different atoms. The magnetic measurement has confirmed that Co3O4 nanoparticles show ferromagnetic behavior, which could be attributed to the uncompensated surface spins and/or finite size effects. The ferromagnetic order of Co3O4 nanoparticles is raised with increasing the decomposition temperature. A physical adsorption method is adopted to immobilize the surface of CS/Co3O4 nanocomposite. Potentiometric sensitivity curve has been measured over the concentration range between 1 x 10(-4) and 8 x 10(-2) M of urea electrolyte solution revealing that the fabricated biosensor holds good sensing ability with a linear slope curve of similar to 45 mV/decade. In addition, the presented biosensor shows good reusability, selectivity, reproducibility and resistance against interferers along with the stable output response of similar to 12 s.

Objektorienterad modellering är ett utmärkt sätt att analysera och modellera fysikaliska system. Den ger möjlighet att hantera stora, komplexa och blandade system.

Modelica är ett exempel på ett multidomän modelleringsspråk som är ekvationsbaserat och hanterar modeller från olika fysikaliska domäner. Det är baserat på principer från objektorientering och hanterar icke-kausala problem. Modelicas struktur gör att befintliga modeller kan delas upp i delmodeller som kan designas och testas oberoende av de andra delmodellerna. Det ger överskådlig bild av fysikaliska systemet. Modellbibliotek i Modelica kan enkelt modifieras för att designa nya komponenter.

Modellering av fysikaliska system, speciellt mekaniska eller mekatroniska system ger upphov till DAE system med index 2 eller 3. Det resulterar svårigheter vid simulering av modeller. En automatiserad symbolisk manipulering av DAE systemen behövs för att underlätta simuleringen.

För att simulera modeller skrivna i Modelica, behövs en kompilator och ett simuleringsverktyg. Dymola är det dominerande verktyget för dessa ändamål. Man har tillgång till Modelicas standardbibliotek via ett grafiskt gränssnitt. Vid simulering kontrolleras modellen så att Modelicas syntax är uppfylld. Sedan kompileras och simuleras modellen.

Mobile payments are increasing in popularity in recent years. New mobile solutions are being developed in the form of new Internet capable mobile devices such as the IPhone and new wireless networks such as the LTE and WiMAX networks.

This report will present, explain and compare some of the most popular wireless networks that enable mobile payments, from a security point of view. The chosen networks are 3G with connection to GSM, and WLAN networks. The main security mechanisms involved in each network, and how they work will be studied. Security requirements and some of the most important threats each network faces will be presented and discussed. The main purpose of the report is to examine if mobile payments offer an acceptable level of security to the average user.

Silver nanoparticles (Ag NPs) were synthesized by aqueous chemical growth technique. The above mentioned synthesized materials were characterized by applying scanning electron microscope (SEM) and X-ray diffraction for confirmation of morphological analysis, compositional purity, and crystalline property and emission characteristics as well. In order to fabricate the urea biosensor (potentiometric), a solution of deionized water and chitosan was prepared having Ag NPs. The said solution was dropped on the glass fiber filter having diameter of 2 cm. A wire of copper having thickness of approximately 500 pm was used for the voltage signal to pull out from the said working nanoparticles (NPs). To improve the strength, sensitivity and the quality of the potentiometric urea biosensor, a specific functional surface of Ag NPs was attained by electrostatic restrained of an enzyme (urease) onto the chitosan-Ag (a nanobiocomposite). The potentiometric reaction was measured via electrochemical detection technique. The potentiometric urea biosensor illustrates significant sensibility at room temperature with approximate to 42 mV as per span. Furthermore, the said biosensor showed an appropriate stable response within 7 sec.

In this work, we evaluate the effectiveness of OpenCL for programming multicore CPUs in a comparative case study with OpenMP and Intel TBB for five benchmark applications: matrix multiply, LU decomposition, 2D image convolution, Pi value approximation and image histogram generation.

Advances in communication technologies continue to increase information sharing among the people.~Short-range wireless networking technologies such as Bluetooth or ZigBee, which are mainly used for data transfer over short range, will, however, suffer from network congestion, high power consumption and security issues in the future.

The body-coupled communication (BCC), a futuristic short-range wireless technology, uses the human body as a transmission medium. In BBC channel, a small electric field is induced onto the human body which enables the propagation of a signal between communication devices that are in the proximity or direct contact with the human body. The direct baseband transmission and simple architecture make BCC an attractive candidate for a future short-range wireless communication technology in particular applications such as body area network.

The main focus of this thesis is on the design and implementation of digital baseband transmitter and receiver for the body-coupled communication. The physical layer (PHY) implementation of the digital baseband transmitter and receiver is inspired from the IEEE 802.3 Ethernet transmission protocol. The digital design is implemented at RTL level using hardware description language (VHDL). The functionality of the digital baseband transmitter and receiver is demonstrated by developing data transfer application layers.

This thesis presents a model of reconfigurable sigma-delta modulator. These modulators areintended for high speed digital Digital to Analog Converters. The modulators are intendedto reduce complexity of current steering DACs and also considered as a front end of dataconverters. Quantization noise present in digital signal is pushed to higher frequencies bysigma-delta modulators. Noise in high band frequencies can be removed by a low pass filter.

A test methodology involving generation of baseband signal, interpolation and digitizationis opted. Topologies tested in MATLAB® include signal feedback and error feedback modelsof first-order and second-order sigma-delta modulators. Error feedback and signal feedbackfirst-order modulators’ performance is quite similar. The SNR of a first-order error feedbackmodel is 52.3 dB and 55.9 dB for 1 and 2 quantization bits, respectively. In second-orderSDM, signal feedback provides best performance with 80 dB SNR.

The other part of the thesis focuses on the implementation of the sigma-delta modulator(SDM) using faster time to market approach. SoC Encounter, a tool from Cadence, is theeasiest way to do this job. The modulators are implemented in 65-nm technology. The reconfigurablesigma-delta modulator is designed using Verilog-HDL language. Switches areintroduced to control the reconfigurable SDM for different input word lengths. Word-lengthcan vary from 0 to 4 bits. Modulator is designed to work for frequencies of 2 GHz. To netlistthe design, Design Compiler is used which is a tool from Synopsys®.

The area of the chip reported by design compiler is 563.68 um. When the design is implementedin SoC Encounter, area of the chip is increased, because the core utilization, whiledesigning, is only 60%, which is 556.8 um. Remaining 40% area is used by buffers, inverterand filler cells during clock tree synthesis. The buffers and inverters are added to removethe clock phase delay between different registers. Power consumption of the chip is 319mW.Internal power of the modulators is 219.1 mW. Switching power of output capacitances is99.9 mW, which is 31% of the total power consumed. Main concern of the power loss isconsidered to be power leakage. To reduce the leakage power and achieve high speed designCORE65GPHVT libraries are used. Leakage power of the design is 2.825 uW which is0.00088% of the total power.

A simple, economical and mild solution chemistry method was used to synthesize diverse nickel oxide (NiO) nanostructures employing methionine as a growth-directing agent. The as-synthesized NiO nanostructures were observed to possess a unique skein-shape morphology with uniform spherical distribution. The NiO nanoskein (NiO NSk) formation was extensively studied using X-ray diffraction (XRD), X-ray photoelectron microscopy (XPS), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) techniques, respectively. The unique NiO NSks exhibited better electrocatalytic activity towards glucose oxidation in alkaline media, enabling the development of a highly sensitive non-enzymatic glucose sensor. The observed analytical properties included high sensitivity (1915 mu A mM(-1) cm(-2)), wide linear range (0.1-5.0 mM), low detection limit (0.7 mu M), higher stability and reproducibility. Moreover, the sensor is selective in the presence of interfering species such as ascorbic acid (AA), uric acid (UA) and dopamine (DP) during the non-enzymatic glucose sensing. The worthy-of-notice electrocatalytic activity and economical feasible preparation of NiO NSk-shaped electroactive material for direct glucose-sensing applications make the present study of high interest for the fabrication of low-cost devices. A NiO NSk-based glucose sensor has also been employed for glucose determination in human serum with adequate results, suggesting high potential for the routine monitoring of glucose from biotechnology, clinical and food industry samples.

In this study a simple, inexpensive and efficient route is proposed to synthesise attractive cobalt oxide (Co3O4) nanostructures using glycine as an effective growth controller and regulator. The as-synthesised Co3O4 nanostructures were observed to possess unique nanoflake shape morphological features with highly dense distribution. The formation of Co3O4 nanoflakes (Co3O4 NFKs) was elaborately explored using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Scanning electron microscopy (SEM) respectively. The unique Co3O4 nanoflakes were known to possess excellent electro-catalytic potential for the oxidation of glucose in alkaline medium. This potential property allowed successful development of highly sensitive (1180 mu A mM(-1) cm(-2)), selective and stable non-enzymatic glucose sensor. In addition, the developed sensor had a wide working range (0.1-5.0 mM), low limit of detection (0.7 mu M), and excellent reproducibility, besides the capability of analysing real blood glucose samples.

The study reports the synthesis of cobalt oxide (Co3O4) nanostructures and their application in enzyme free electrochemical sensing of glucose. The synthesized nanostructures were elaborately characterized via number of analytical techniques including scanning electron microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The as-synthesized nanostructures of Co3O4 were found to exhibited nanodisc like morphology with the size dimension in range of 300-500 nm. The obtained morphological features were evaluated for their electrochemical potential towards oxidation of glucose which enabled development of sensitive (27.33 mu A mM(-1) cm(-2)), and stable enzyme free glucose sensor. In addition, the developed sensor showed excellent linearity (r(2)=0.9995), wide detection range (0.5-5.0 mM), lower detection limit (0.8 mu M) and extreme selectivity towards glucose in the presence of common interferents like dopamine (DP), ascorbic acid (AA) and uric acid (UA). The successfully application of developed sensor for real blood glucose analysis further reflects its capability for routine glucose measurement.

This work reports a highly sensitive electrochemical sensor for organophosphate pesticide (malathion) based on unique and attractive CuO nanostructures. The discussed nanostructures were synthesized using low temperature hydrothermal growth method utilizing green amino acids such as glycine, serine, threonine and histidine as effective bio-compatible templates. The morphological evaluation demonstrated formation of unique and attractive 1-D nanostructures reflecting the effective growth controlling and directing capabilities of the utilized amino acids. The as-synthesized CuO nanostructures were noted to possess high affinity towards malathion which enabled their application as electrode material for the development of affinity based electrochemical sensor. Although, the as-synthesized morphologies were all sensitive towards malathion but the glycine directed triangular flake-like nanostructures exhibited greater sensitivity compared to other competitors. The electrochemical behaviour of the modified electrodes was studied using cyclic voltammetry (CV) whereas, differential pulse voltammetry (DPV) was utilized for the analytical evaluation of the sensor. The developed sensor demonstrated high reproducibility, stability, wide detection window (1-12 nM), and sensitivity to detect malathion up to 0.1 nM based on suppressive signal measurement. In addition, the sensor system exhibited high anti-interference capability in the presence of common co-existing pesticides like lindane, carbendazim, and trichlorfon. The developed sensor provides an effective measure for detecting extremely low concentration of malathion with wide applicability in various fields. (C) 2015 Elsevier Ltd. All rights reserved.

Nanotechnology is an emerging inter-disciplinary paradigm which encompasses diverse fields of science and engineering converge at the nanoscale. This nanoscale science and nanostructure engineering have well demonstrated in the fabrication of sensors/transducers devices with faster response time and better sensitivity then the planer version of the sensor’s configurations. Nanotechnology is not just to grow/fabricate nanostructures by just mixing nanoscale materials together but it requires the ability to understand and to precisely manipulate and control of the developed nanomaterials in a useful way. Nanotechnology is aiding to substantially improve, even revolutionize, many technology and industry sectors like information technology, energy, environmental science, medicine/medical instrumentation, homeland security, food safety, and transportation, among many others. Such applications of nanotechnology are delivering in both expected and unexpected ways on nanotechnology’s promise to benefit the society.

The semiconductor ZnO with wide band gap (~ 3.37 eV) is a distinguish and unique material and its nanostructures have attracted great attention among the researchers due to its peculiar properties such as large exciton binding energy (60 meV) at room temperature, the high electron mobility, high thermal conductivity, good transparency and easiness of fabricating it in the different type of nanostructures. Based on all these fascinating properties, ZnO have been chosen as a suitable material for the fabrication of photonic, transducers/sensors, piezoelectric, transparent and spin electronics devices etc. The objective of the current study is to highlight the recent developments in materials and techniques for electrochemical sensing and hetrostructure light emitting diodes (LEDs) luminescence properties based on the different ZnO nanostructures. The sensor devices fabricated and characterized in the work were applied to determine and monitor the real changes of the chemical or biochemical species. We have successfully demonstrated the application of our fabricated devices as primary transducers/sensors for the determination of extracellular glucose and the glucose inside the human fat cells and frog cells using the potentiometric technique. Moreover, the fabricated ZnO based nanosensors have also been applied for the selective determination of uric acid, urea and metal ions successfully. This thesis relates specifically to zinc oxide nanostructure based electrochemical sensors and photonic device (LED) applications.

This study proposes a new model which is fully specified for automated seizure onset detection and seizure onset prediction based on electroencephalography (EEG) measurements. We processed two archetypal EEG databases, Freiburg (intracranial EEG) and CHB-MIT (scalp EEG), to find if our model could outperform the state-of-the art models. Four key components define our model: (1) multiscale principal component analysis for EEG de-noising, (2) EEG signal decomposition using either empirical mode decomposition, discrete wavelet transform or wavelet packet decomposition, (3) statistical measures to extract relevant features, (4) machine learning algorithms. Our model achieved overall accuracy of 100% in ictal vs. inter-ictal EEG for both databases. In seizure onset prediction, it could discriminate between inter-ictal, pre-ictal, and ictal EEG with the accuracy of 99.77%, and between inter-ictal and pre-ictal EEG states with the accuracy of 99.70%. The proposed model is general and should prove applicable to other classification tasks including detection and prediction regarding bio-signals such as EMG and ECG. (C) 2017 Elsevier Ltd. All rights reserved.

We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.

Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohens kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.

In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F- measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).

The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased over the last years. This has been possible mainly due to the increased computing power of microcontrollers and computers. An UAV can be used in both civilian and military areas, for example surveillance and intelligence. The UAV concerned in this master's thesis is a prototype and is currently being developed at DST Control AB in Linköping.

With the use of UAVs, the need for a positioning and navigation system arises. Inertial sensors can often give a good position estimation, however, they need continuous calibration due to error build-up and drift in gyros. An external reference is needed to correct for this drift and other errors. The positioning system investigated in this master's thesis is supposed to work in an area defined by an inverted cone with the height of 25m and a diameter of 10m.

A comparison of different techniques suitable for position sensing has been performed. These techniques include the following: a radio method based on the Instrument Landing System (ILS), an optical method using a Position Sensing Detector (PSD), an optical method using the Indoor GPS system, a distance measurement method with ultrasound and also a discussion of the Global Positioning System (GPS).

An evaluation system has been built using the PSD sensor and tests have been performed to evaluate its possibilities for positioning. Accuracy in the order of a few millimetres has been achieved in position estimation with the evaluation system.

Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.

The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.

In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

This Bachelor thesis is a pre-study of the possibilities of using programmable logic in the purpose to enable fast trigger based data communication. Triggerbased data communication is in this case referred to a context where the processed data is stored and examined so when the trig situation appears the data should be able read out to a computer for evaluating. The purpose of this thesis is to find difficult and time consuming elements but also to find elements that is well suited for implementation in programmable logic. The work should also support further development and verification of trig functionalities and additional hardware. This with the intent of constructing an Ethernet based oscilloscope.

The result of this thesis is a conclusion that programmable logic is well suited for many of the implemented logic function

A lane departure warning system relying exclusively on a camera has several shortcomings and tends to be sensitive to, e.g., bad weather and abrupt manoeuvres. To handle these situations, the system proposed in this thesis uses a dynamic model of the vehicle and integration of relative motion sensors to estimate the vehicle’s position on the road. The relative motion is measured using vision, inertial, and vehicle sensors. All these sensors types are affected by errors such as offset, drift and quantization. However the different sensors are sensitive to different types of errors, e.g., the camera system is rather poor at detecting rapid lateral movements, a type of situation which an inertial sensor practically never fails to detect. These kinds of complementary properties make sensor fusion interesting. The approach of this Master’s thesis is to use an already existing lane departure warning system as vision sensor in combination with an inertial measurement unit to produce a system that is robust and can achieve good warnings if an unintentional lane departure is about to occur. For the combination of sensor data, different sensor fusion models have been proposed and evaluated on experimental data. The models are based on a nonlinear model that is linearized so that a Kalman filter can be applied. Experiments show that the proposed solutions succeed at handling situations where a system relying solely on a camera would have problems. The results from the testing show that the original lane departure warning system, which is a single camera system, is outperformed by the suggested system.

More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.

Trends show that cyber attacks targeting critical infrastructures are increasing, but security research for protecting such systems are challenging. There is a gap between the somewhat simplified models researchers at universities can sustain contra the complex systems at infrastructure owners that seldom can be used for direct research. There is also a lack of common datasets for research benchmarking. This paper presents a national experimental testbed for security research within supervisory control and data acquisition systems (SCADA), accessible for both research training and experiments. The virtualized testbed has been designed and implemented with both vendor experts and security researchers to balance the goals of realism with specific research needs. It includes a real SCADA product for energy management, a number of network zones, substation nodes, and a simulated power system. This environment enables creation of scenarios similar to real world utility scenarios, attack generation, development of defence mechanisms, and perhaps just as important: generating open datasets for comparative research evaluation.

The popularity of mobile games has increased drastically during the recent years andmany people use them as their main source of entertainment. Mobile gamescommunicate with other devices over the network which consumes a lot of energy,especially when connected to cellular networks (e.g., 3G). This high energy expensecan feel unjustified to the player since always-on network connectivity is not requiredin order to play most games.Furthermore, the number of malware-infected applications in offical applicationstores has increased significantly in the recent years. These malware-infectedapplications can gain unrestricted access and control of users phones which can be athreat to security. Information about the behaviour characteristics of games can beused to develop or improve systems for detecting malware applications.In this thesis, 20 popular Android games are analysed with a focus on the datacommunication, CPU utilization and system call behaviour. The main subject of thedata communication study is the 3G communication energy consumed by games. Thesystem call study aims at quantifying the number and type of calls used by games.This may be useful in a further study of harmful behaviour by apps.The profiling results presented in this report show that the communication energyvaries drastically among games. Games with a very similar gameplay can consumevery different amounts of energy which indicates that there is room for improvementsin many of the games. Ad-free games consume significantly less energy than gamesthat use in-app advertisements. The results show that improving the advertisementfetching policy could reduce the energy consumption of these games. The majority ofthe games can be played without network connectivity and therefore thecommunication energy consumed could be completely avoided. The thesis alsoshows that games use a wide variety of system calls and that many of the system callsare common among the games.

In this thesis we study users' viewing motions when watching 360° videos in order to provide information that can be used to optimize future view-dependent streaming protocols. More specifically, we develop an application that plays a sequence of 360° videos on an Oculus Rift Head Mounted Display and records the orientation and rotation velocity of the headset during playback. The application is used during an extensive user study in order to collect more than 21 hours of viewing data which is then analysed to expose viewing patterns, useful for optimizing 360° streaming protocols. 

Although a significant proportion of the mobile apps are games there has been little attention paid to their specific characteristics with respect to communication energy. In this paper we select 20 mobile games among the top 100 free Android games, and study their data patterns and communication energy use over a total of 25 hours of playing. The analysis of the energy for communication over 3G networks indicates that there is a wide variation among the games, the largest footprint being 8 times higher than the lowest one. The results also indicates both app-specific and category-specific relations between data pattern and energy use, as well as variations in CPU utilisation.

Vid sifferseriell aritmetik används ett antal parallella bitar för varje siffra. För att jämföra prestanda och effektförbrukning i förhållande till antalet bitar behövde Institutionen för systemteknik (ISY) en layout-generator för att enkelt kunna generera layout för en sifferseriell tvåportsadaptor. Layouten skulle göras i 0.18 mikrometer process. Antalet inkommande databitar och antalet koefficientbitar skulle vara variabelt. Stor vikt lades vid planeringen av layouten för att genereringen av adaptorn skulle fungera smidigt oberoende av de variabla parametrarna. Kod skrevs för att koppla samman layout-instanserna och för att förenkla adaptorn.

Kartering är ett centralt och vanligt förekommande problem inom robotik. Att bygga en korrekt karta av en robots omgivning utan mänsklig hjälp har en mängd tänkbara användningsområden. Exempel på sådana är rymduppdrag, räddningsoperationer,övervakning och användning i områden som är farliga för människor. En tillämpning för robotkartering är att mäta volymökning hos terräng över tiden. I Sverige finns det över hundra soptippar, och dessa soptippar är reglerade av lagar som säger att man måste mäta soptippens volymökning minst en gång om året.

I detta exjobb görs en undersökning av möjligheterna att göra dessa volymberäkningarmed hjälp av obemannade helikoptrar utrustade med en Light Detectionand Ranging (LIDAR) sensor. Olika tekniker har testats, både tekniker som slår ihop LIDAR data till en karta och regressionstekniker baserade på Gauss Processer. I avsaknad av data inspelad med riktig helikopter har ett experiment med en industri robot genomförts för att samla in data. Resultaten av volymmätningarnavar goda i förhållande till LIDAR-sensorns upplösning. För att få bättre volymmätningaroch bättre utvärderingar av de olika algoritmerna är en bättre LIDAR-sensor nödvändig.

This paper describes a solution to the Advanced Diagnosis and Prognostics testbed (ADAPT) diagnosis benchmark problem. One main objective was to study and discuss how engineering students, with no diagnosis research background, would solve a challenging diagnosis problem. The study was performed within the framework of a final year project course for control engineering students. A main contribution of the work is the discussion on the development process used by the students. The solution is based on physical models of components and includes common techniques from control theory, like observers and parameter estimators, together with established algorithms for consistency based fault isolation. The system is fully implemented in C++ and evaluated, using the DXC software platform, with good diagnosis performance.