The demand for remote rehabilitation is increasing, opening up convenient and effective home-based therapy for the sick and elderly. In this study, we use AnyBody simulations to analyze muscle activity and determine key parameters for designing a rehabilitation exoskeleton, as well as selecting the appropriate motor torque to assist patients during rehabilitation sessions. The exoskeleton was designed with a PID control mechanism for the precise management of motor positions and joint torques, and it operates in both automated and teleoperation modes. Hip and knee movements are monitored via smartphone-based IMU sensors, enabling real-time feedback. Bluetooth communication ensures seamless control during various training scenarios. Our study demonstrates that remotely controlled rehabilitation systems can be implemented effectively, offering vital support not only during global health crises such as pandemics but also in improving the accessibility of rehabilitation services in remote or underserved areas. This approach has the potential to transform the way physical therapy can be delivered, making it more accessible and adaptable to the needs of a larger patient population.

Denna rapport presenterar hur en konstruktionsuppgift av ett automatiserat hybrid värmesystem har genomförts. Två värmekällor skulle inkluderas i systemet, primärkällan var en fastbränslepanna på 30 kW. Och som sekundärvärmekälla ett solfångarsystem med totalt tio solfångarmoduler. Värmesystemet skulle värma en bostad, en maskinverkstad samt ett maskingarage. Temperaturregleringen skulle styras med hjälp av automatik. För att inhämta data på de befintliga byggnader som var berörda av projektet gjordes fysiska mätresultat på plats, studier av ritningar, litteratur, internet källor samt samråd med den externa handledaren.Detta gav i slutändan ett system som är uppdelat i två olika delsystem. Ett system med en fastbränslepanna, och ett system med solfångarmoduler. Den uppvärmda median lagras i fyra ackumulatortankar, varje enskild ackumulatortank har olika volym och väljes med hjälp av automatik. Automatiken sköts av en så kallad PLC, som står för Programmable Logic Controller (Eng.). Denna PLC styr hela systemets elektriska utrustning, vilket innefattar pumpar, reglerventiler, avstängningsventiler och temperatur inhämtning från sensorer. Som presentations metod av resultatet valdes att använda ritningar som kan hittas som större bilagor i denna rapport. Samtliga formler som har använts för att dimensionera systemet återfinns under teori avsnittet, med kortfattade kommentarer. Vissa moment har fler än en lösning, vilket ställer krav på att även klara av att göra analyser av olika lösningar samtidigt ta i beaktande den externa handledarens önskemål.

Digital Twins integrating degradation models can be harnessed to enable industrial digitalization and promote sustainable manufacturing processes. Research in control has transitioned from methods ensuring stability and performance in the presence of degradation or faults to strategies that balance the performance and degradation of complex systems. This work formulates the the lifetime distribution for systems with multiple subsystems and several possible failure modes, considering both deterministic and stochastic failure modes. The proposed formulation is applied to the design of a two-layer control strategy with an model predictive control-based upper-layer controller that updates the parameters of the lower-layer PID-controlled closed-loop subsystems. The proposed method was evaluated on a numerical example using two hydraulic valves operating in parallel, a system commonly found in a steel manufacturing processes. The upper layer controller achieved equal time to failures for both valves, by varying the load on respective valves. The results suggest that heterogeneous systems, in terms of lifetime distributions and failure modes, can be controlled by balancing degradation and performance.

The system of connected vehicle to vehicle and vehicle to infrastructure can be considered as a wireless cyber-physical system of systems (Wireless CPSoS), which will be provided with the high ability of adaptive control on system of systems, cooperative scenarios to control of a Wireless CPSoSand adaptive wireless networked control system (WNCS). In this paper we present our multi-vehicle testbed based on the cyber-physical system that was designed for verification and validation of cooperative driving algorithm involving WNCStesting. Vehicles were developed as the physical prototype equipped with Raspberry-pi microprocessor and other sensing elements. This testbed consists of a fleet of 4 robot vehicles. An indoor positioning system (IPS) based on particle filter is purposed by using an inertial measurement unit (IMU) and iBeacon that is built upon Bluetooth Low Energy. Some typical cooperative driving scenarios can be implemented on this testbed under indoor laboratory. The method used to realize the objective statement was Model Predictive Control (MPC) with a state observer based on a Kalman Filter (KF). Because the wireless control systems can be severely affected by the imperfections of the wireless communication link. Our experimental testbed paves the way for testing and evaluating more intelligent cooperative driving scenario with the use new wireless technology and control system in the future.

This paper investigates the distributed event-triggered control of leader-follower consensus problem with harsh environmental conditions such as a high probability of burst packet drops occurs in the communication channels between the vehicles. The main objective in the vehicular platoons is to ensure that all follower vehicles are traveling at the same speed as the leader while maintaining a desired inter-vehicular distance with considering the minimum communication resource usage. To achieve the object, a novel distributed event-triggered communication strategy based on the estimation and prediction of self-states and other agents’ states in each vehicle is proposed which includes two parallel algorithms. The first one is based on comparing the state estimation and prediction of the vehicle itself, and the second one is based on comparing the state estimation and prediction of other vehicles which have interaction in their process with the active vehicle. Collision avoidance, speed convergence, and limited communication resource usage of each vehicle are simultaneously considered in the design of the event-triggered mechanism in a harsh packet drops environment. Numerical simulations are presented to show that the proposed event triggering algorithm under three scenarios of burst packet drops can converge the consensus problem of vehicular platooning.

In industrial production, it is of great importance to have high availability in its production equipment. Well-functioning maintenance is a significant factor for a high level of availability. This can be achieved by minimizing the number of reactive maintenance stops and optimizing scheduled maintenance. New methods for predictive maintenance provide a good opportunity for this, but most technologies that are available today are designed for individual sub-systems and they are rarely designed for a complex, interconnected machine. In the process industry, raw materials are rocessed into a finished product in a continuous flow through several subsystems and if one subsystem stops, the entire process flow stops. For these processes, it is more important to optimize the maintenance efforts for subsystems so maintenance can take place synchronized. This paper describes a method of supervised control that includes maintenance aspects; health parameters indicating deterioration are included in a MIMO controller. The method is verified in a simulation of a rolling mill with three rollers. The results show that it is possible to optimize the whole complex process including several subprocesses by using a health parameter as a control parameter and broadening the controllability of the system by dividing the workload in a way that all the subsystems reach the desired degradation level for maintenance in a desired optimum time. 

In this paper we evaluate the generation of a multi-tone set for characterizing the behavior of nonlinear radio frequency (RF) modules in its out-of-band when harmonic sampling is used as digitizer. The purpose is to provide the reader with a tool to select proper frequencies and record length for a given application and test-bed. The method is based on simulations and the use of Sidon sequences. The proposed method is applicable to sparse discrete frequency multi-tones.

Time domain analysis in vector network analyzers (VNAs) is a method to represent the frequency response, stated by the S-parameters, in time domain with apparent high resolution. Among other utilities time domain option from Agilent allows to measure microwave devices into a specific frequency range and down till DC as well with the two time domain mode: band-pass and low-pass mode. A special feature named gating is of important as it allows representing a portion of the time domain representation in frequency domain.

This thesis studies the time domain option 010 from Agilent; its uncertainties and sensitivity. The task is to find the best method to measure the antenna element matching taking care to reduce the influence of measurement errors on the results.

The Agilent 8753ES is the instrument used in the thesis. A specific matching problem in the antenna electric down-tilt (AEDT) previously designed by Powerwave Technologies is the task to be solved. This is because it can not be measured directly with 2-port VNAs. It requires adapters, extra coaxial cables and N-connectors, all of which influences the accuracy. The AEDT connects to the array antenna through cable-board-connectors (CBCs). The AEDT and the CBCs were designed before being put into the antenna-system. Their S-parameters do not coincide with the ones measured after these devices were put in the antenna block.

Time domain gating and de-embedding algorithms are two methods proposed in this thesis to measure the S-parameters of the desired antenna element while reducing the influence of measurement errors due to cables CBCs and other connectors. The aim is to find a method which causes less error and gives high confidence measurements.

For the time domain analysis, reverse engineering of the time domain option used in the Agilent VNA 8753ES is implemented in a PC for full control of the process. The results using time-domain are not sufficiently reliable to be used due to the multiple approximations done in the design. The methodology that Agilent uses to compensate the gating effects is not reliable when the gate is not centered on the analyzed response. Big errors are considered due to truncation and masking effects in the frequency response.

The de-embedding method using LRL is implemented in the AEDT measurements, taking away the influences of the CBCs, coaxial cables and N-connector. It is found to have sufficient performance, comparable to the mathematical model. Error analysis of both methods has been done to explaine the different in measurements and design.

The optimization of the PID parameters has been a challenge since the invention ofPID controllers. Many different optimization methods have been developed sincethe 1940s, in this work the Ziegler-Nichols step response method, the Lambdamethod and the AMIGO method were tested and compared.The aim of this thesis was to be able to choose which optimization method was bestsuited for process instruments designed at the Custimized Bioprocess Solutions(CBS) department at Cytiva . The optimization method that is chosen will be proposed as part of a work process to follow when a system needs to be optimized.The tests were performed using the step response method, where the preset PID parameters on a system were used as a reference. Based on the three parameters model,the necessary data could be collected, then new PID parameters were calculatedwith the calculation rules of the selected optimization methods. The new PID parameters were then used to generate new step responses. Finally, the new step responses were compared to the step response from the preset PID parameters to designate the most suitable optimization method.This work resulted in the conclusion that more tests need to be performed, but thatthe calculation tool used during the degree project should be included as part of therequested work process. 

Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. In order to overcome this problem of parameter variation the PI controllers are widely used in industrial plants because it is simple and robust. However there is a problem in tuning PI parameters. So the control engineers are on look for automatic tuning procedures. In recent years, many intelligence algorithms are proposed to tuning the PI parameters. Tuning PI parameters using different optimal algorithms such as the simulated annealing, genetic algorithm, and particle swarm optimization algorithm. In this paper a scheduling PI tuning parameters using genetic algorithm strategy for an induction motor speed control is proposed. The results of our work have showed a very low transient response and a non-oscillating steady state response with excellent stabilization. The simulation results presented in this paper show the effectiveness of the proposed method, with satisfied response for GA-PI controller.

Wireless sensor network is an intelligent network system which has the self-monitoring functionality. It consists of many low-cost, low-power and small-sized sensor nodes that can communicate with each other to perform sensing and data processing. Acting as an important role in the system, network coverage usually has a huge effect on the system’s lifetime.In this thesis, particle swarm algorithm was used as a method to optimize the coverage in the coverage of wireless sensor network. A network coverage optimization strategy based on particle swarm optimization was proposed and MATLAB was used as a tool to apply the algorithm. The model used in this thesis is the probability sensing model and the coverage type is area coverage. Effectiveness of the algorithm is proved by simulation. The simulation of the algorithm suggests the optimal deployment can be determined if a certain parameter which in this thesis is the sensing range is given.

Initiatives to open certain frequency bands for dynamic spectrum access (DSA) are ongoing. Examples are the wireless access policy for electronic communications services and white spaces coalition. A key issue in DSA is how to measure occupancy and interference in an open frequency band to decide whether or not it can be used for a certain service. Such measurement must be easy to perform and provide a result that can be used as decision metric. An earlier proposal, based on a so-called impulsiveness correction factor, with this purpose has been shown to work properly if the interference is dominated by a single pulsed signal. In this study, the former approach is extended to the case in which the interference signal consists of a multiple of interference signals. This extension is shown as a closed expression involving only parameters that can be determined from an interference measurement.

This project discusses the use of System Identification for Smart Maintenance. System Identification is the process of finding a mathematical model of a system using empirical data. The mathematical model can then be used to detect and predict the maintenance needs, which is considered as Smart Maintenance. Smart maintenance strategies have gained pretty much importance recently, since it contributes to economically sustainable production. This project uses the LAVA-framework, proposed in [1] for non-linear system identification, which has the capability of explaining the dynamics of the system very well, and at the same time follows the principle of parsimony. A nominal model is first identified using data from a system that operates under normal operating conditions, then the identified nominal model is used to detect when the system starts to deviate from normal behavior, and these deviations indicate the deteriorations in the system. Furthermore, a new Multiple Model Method which is developed in [2] using the similar idea from LAVA, is applied on the large data set of a system that operates on separate batches and units, which identifies individual model for each batch and unit, which is then used to detect the deficient units or batches and changes in the system behavior. Finally, the proposed methods are applied to two different real world industrial cases; a Heat exchanger and a Wood Moulder Machine. In the first, the purpose is to detect the dirt in a Heat Exchanger, and in the second, the goal is to detect when the tool in a Wood Moulder Machine needs to be changed.

The idea to develop  a platform for a PID-controller came from the need to control the temperature in  an espresso machine in a more exact way than a thermostat could perform. In discussions with Syntronic AB the idea developed into PID-control for industrial use. Syntronic AB suggested that the platform should be based on a commercially available processor module to shorten the development time. The suggestion included the use of the microcomputer Raspberry Pi, which supports USB, HDMI, memory card and Ethernet.

The work began with establishing a schedule for the 10 weeks of the projects duration, and after that a system specification listing all functions, implementation and limitations was made. When the foundation of the system specification was done, a preliminary system design took shape.

Because of the Raspberry Pi´s lack of Analog-to-Digital converter, a circuit board containing Wheatstone bridges, differential amplifiers and a two channel Analog-to-Digital converter was fabricated. The choice of Operative System fell on Linux Raspbian Wheezy, a popular Linux distribution with good documentation. Embedded programming was made solely in C language, Nginx was used as server application making it possible to show real-time graph and receive parameter input from a webpage. The webpage was written in PHP and JavaScript for server and client side respectively.

To evaluate the PID-controller it was mounted inside an espresso machine where it controlled the temperature of the boiler. Aiming for a short settling time, parameters for the PID-controller was produced by testing.

The cyber-physical system is the keyword of the fourth industrial revolution. The internet changed the way that humans interact with each other, and cyber-physical systems will change the way that we interact with the physical world around us. There are five levels for designing cyber-physical systems, the most important level among these five levels in which the physical world gets connected to the cyber world is the cyber level in which a cyber model of the system is built, following levels and any further decision will heavily depend on the cyber model that is derived in this level. In this project, the network of wireless control cars will be used as a simulation of Wireless Networked Control system for testing and implementation of the cyber-physical algorithms, this network of cars is composed of three parts: cars, controllers and communication link. The cyber model of the cars will be derived in the first part and will be validated through simulation. In the second part, three controllers will be designed for the cyber model and physical model, these controllers are: the controller for closed-loop control of one car, controller for distance and speed control of two cars and controller for distance and speed control of platoon of cars, all of these controllers will be designed according to PID, Linear Quadraticand Model Predictive Control. Finally, the communication link between the cyber controller and physical device will be implemented and a cyber controller will be used for controlling the physical system.

Health-aware control is essential for enhancing the longevity and efficiency of complex systems by integrating machine health metrics into production and maintenance planning. Traditional methods often treat production and maintenance as separate tasks, overlooking their interdependencies and thus failing to achieve optimal system performance. A key challenge in addressing this problem and considering the mutual effects of production and maintenance is establishing a cohesive framework that bidirectionally connects high-level decision-making with low-level operational control, optimizing production and maintenance at the same time. To address this challenge, the shop-floor machinery must be aware of its state of health (SOH), and the high-level planning should be able to include this information about SOH as an influential parameter in production planning. Doing this involves not only developing physically interpretable and cost-effective methods for identifying degradation but also implementing adaptive control strategies that respond to real-time conditions. Moreover, it is critical to study how lifespan control affects other system parameters to ensure optimal performance. To this end, this research first tackles the challenges in identifying and controlling degradation at the shop-floor level. It then bridges the gap between high- and low-level control by proposing a framework that integrates both levels into a unified, health-aware production and maintenance system. The thesis first explores the controllability of machine lifespan in interconnected systems. It then proposes methods for identifying degradation as a physically interpretable and controllable system parameter. The thesis further investigates the application of advanced control strategies such as model predictive control and linear quadratic regulators to manage degradation at both the low and high levels. A novel contribution is made by introducing the concept of state-action cost estimation for degradation cost, which links machine degradation directly to control actions, allowing for real-time degradation management without the need for costly physical modeling. The proposed approach is validated through simulations of various industrial systems and publicly accessible datasets, demonstrating its capability to optimize machine health and performance while maintaining product quality and minimizing degradation. Finally, bridging the gap between high-level strategic planning and low-level operational execution, this thesis provides a pathway to more efficient and resilient manufacturing systems where health-aware control is fully integrated.

Predicting the failure and estimating the machine's state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system's reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.

Diagnosis, fault prediction, and Remaining Useful Life (RUL) estimation are among the predictive maintenance research subjects used for maintenance cost reduction. Using the available data with different machine learning methods, especially deep learning methods, the accuracy of estimation and prediction of faults and RUL have increased dramatically. However, due to the statistical nature of the machine learning methods and the limitations of available datasets, physically interpreting this information might be impossible. On the other hand, controlling the degradation and faults in the machines as the optimum predictive maintenance solution needs the physical interpretation of the method's outcome. In order to test the new process-based methods for degradation and fault control, datasets with more information are required (compared to available datasets). In this article, we introduce an open-source degradation simulator for linear systems. This simulator can simulate the degradation in closed-loop machines whose dynamics are known. It is also possible to simulate different degradation models for different system parts simultaneously by adding different processes and output noise to the system. This simulator can generate enough data to test new machine learning-based predictive maintenance methods.

This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.

In this paper a synthetic vector network analyzing measurement system is presented. The system is based on a hardware set-up, including a signal generator and a vector signal analyzer, with the vector network analyzing functionality implemented in the software. The measurements of the proposed system demonstrated comparable performance in terms of accuracy and speed compared with a modern traditional vector network analyzer, but it is more flexible due to its inherent software implementation. The proposed system’s ability to measure nonlinear phenomena is addressed and discussed, and some preliminary results are given.

In this report we focus on the use of an economical way on how Scalar Network Analyzer (SNA) works instead of Vector Network Analyzer (VNA) to estimate the phase angle of signals in indoor channel. This is detailed in RMS delay theory and simulation section, experimental is designed in the according Experiment Design section, where we also state the required measurements known from the math part. In our work, data are recorded both from two different channel characteristics. Method of achieving amplitude is by using deconvolution theory. The condition of applying Hilbert transform are highlighted as impulse response h(t) in time domain should be causal.  The recorded data amplitude is computed by Hilbert Transform, and therefore validate the condition using Inverse Discrete Fourier Transform (IDFT) back to time domain to achieve h(t). Power delay profile P(t) is therefore presented afterwards. In paper calculations of rms delay τ_rms  of the channel which is the most important variable are also performed, the results calculated from different windowing truncation and the LOS and NLOS characteristics are compared in discussion and conclusion section, it also includes Opinions of window functions chosen for the phase estimation.

We describe a very short channel, 0.15 μm, LDMOS transistor, with a breakdown voltage of up to 45 V, manufactured in a standard 0.35 μm BiCMOS process. At 1900 MHz and a 12V supply voltage the 0.4mm gate width device gives 100 mW output power P1 dB at a drain efficiency of 43%. It has a transducer power gain of more than 20dB and a current gain cutoff frequency, fT, of 13 GHz. The maximum available gain cutoff frequency, fMAX, is 27 GHz. The LDMOS process module does not affect the performance or models of other devices. We present for the first time a simple way to create high voltage, high performance LDMOS transistors for RF power amplifier use even in a very downscaled silicon technology.

Spectrally pure signals are an indispensable requirement when the Signal Generator (SG) is to be used as part of a test bed. However, even sophisticated equipment may not comply with the needs imposed by certain applications. This work approaches the problem by using Digital Pre-Distortion (DPD) based on a polynomial memory-less model obtained for the SG.

Using the SG in arbitrary mode (ARB) an input signal is computer-generated and reproduced by the SG. Measurement accuracy is ensured using coherence sampling and grid matching to the Signal Analyzer (SA). Finally, careful time alignment is used to compare the transmitted and received three-tone signals to obtain the polynomials coefficients.

Results show that the accuracy of the model and the effectiveness of pre-distortion may vary depending on the amplitude of the three-tone signal. However, using polynomials of 5th and 9th degrees up to 15dB reduction of the 3rd order Inter-Modulation products can be obtained, and spurious powers may be lowered down to 70dBc.

The project consists of controlling a mechanical piston and thermal resistance using an Arduino’s microcontroller. The piston and the thermal resistance take part of an existing project. This project, known as The electronic nose, has the purpose to measure the aroma of different products. In order to achieve this purpose, this tool is a combination of various sensors used to detect gases by generating signals for an analysis system.

The project can be divided in different parts; electrical circuit design of piston and thermal resistance, design mechanical parts and components needed, develop a software able to control the piston and the thermal resistance and communication between software.

The piston is installed on the bottom part of hollow cylindrical case and the thermal resistance is located inside a cylindrical base. This base, where the sample for smelling is put up, is bound on the top of the piston. Arduino controls the piston up and down movement and the temperature in the sample’s base.

On the other hand, the electronic nose is installed at the top of the case. Through its sensors, The electronic nose is able to measure the aroma of some products. The aroma comes from the gases of the sample and they can be detected by the sensors. The purpose of installing the piston in the electronic nose is to get a pulse signal during the measure of these gases.

Moreover, is very important keep the temperature of the sample under control, therefore the software is based on a on/off controller. A on/off controller calculates continuously the difference between a desired temperature set point and the temperature measured during process. In order to minimize this difference value the controller turn on or off the resistance during a specific instant time.

Safety instrumented systems (SIS) are widely used in the process industry, like oil and gas, to prevent the initiation of dangerous events and mitigate their consequences if hazardous events are developed into an accident. International standards like IEC – 61508 and 61511 sets this requirement interms of Safety Integrity Level or SIL to be maintained during the entire safetylife cycle of the SIS. The standard developed by the PSAN – NOG 070 also has reliability requirements for SIS that align with those set by IEC standards.

A SIL is given a number between 1 and 4 which indicates SIF’s reliability in the increasing order. A single SIS can be designed to perform one or more Safety Instrumented Function (SIF). The SIL of the SIF defines a limit of themaximum tolerable average probability of failure on demand (average PFD) which is usually determined during the design phases and must be complied with during the operational phases as well.

In this thesis, the SIS in its operational life is reviewed by implementing a casestudy of the SIF called PSD PAHH (preassure alarm high high) by the closing of safety valves. The SIS and the SIF are implemented on Equinor’s Johan Castberg FPSO on the Northern Continental Shelf. Experienced failure rates of the components from the operational phase form important inputparameters that are required to carry out the reliability follow-up analyses. The guidelines recommend optimising the proof test interval in case the experienced failure rates, and consequently the average PFD, differs significantly from the initial values determined at design. The proof test optimisation may be done by extending the test interval which indicates that the PFD has improved (decreased) or by shortening the interval which shows that PFD has increased meaning the reliability of the SIF has degraded.

Reducing the proof test interval is not seen as an ideal situation by the end users of the SIS as it is a costly and resource-consuming affair that usually stops the production and bring additional safety related issues for the duration of thetest. Therefore, proof test alternatives are suggested and the benefits of using the partial proof test are discussed qualitatively and quantitatively.

In this paper we evaluate the application of convex optimization for PAPR reduction on OFDM 802.11a signal type. A radio frequency power amplifier is measured and characterized while excited by both original and optimized OFDM signals. A state-of-art test setup was used for the purpose. Improvements in power added efficiency, in-band errors, out-of-band spectral emissions are investigated for their relevance and a study of the power distribution in the excitation signal is evaluated.

Today, Communication has increasing influence on our Daily life. Wireless data Communication services allow people to access the data Network without a physical Connection. This work Studying Wireless Local Area Network WLAN features and describing all technologies and standard necessary to operate this network. This work aims also at comprehensive investigation of wireless network including wireless Local Area Networks implementation and performance analysis. Testing WLAN performance through some practical measurement such as site surveying in different locations, finally measuring the strength of the signals different locations in the coverage area, test results can be used in order to enhance the network performance.

Continuous surveillance of critical parts and locations in buildings using various sensor techniques should be an essential part of future building diagnostic and maintenance systems. Up until now, such ambitions have been confined to relatively few buildings world wide, mainly inhibited by the difficulties and costs of the installation, particularly in existing buildings. Recent advances in the field of information technology (IT) have highlighted the possibility of making building monitoring and control systems conceivable to a larger group of users. Novel sensors and measurement techniques enable monitoring of almost any part of a building in terms of moisture content, relative humidity, temperature, ventilation, etc. Local networks, communicating over any physical media, e.g. twisted pair, power lines or even wireless, connect the various measurement nodes together. On-line processing and evaluation of relevant data can provide the user with almost instant information on the status of buildings via the Internet or other modes of telecommunication. The paper reviews and exemplifies the experience of the Centre in the area of building monitoring in general and of historic buildings in particular. Ideas and speculations about future development of web-based maintenance systems for historic buildings will be discussed.

This thesis is a project carried out at the “Centre for RF Measurements Technology of Gävle”. The first aim of this work was basically to develop an EMI measurement system, to that purpose, it has been used an EMI Tester receiver, Spectrum Analyzer and a broadband antenna. Tables and graphics are shown to provide the values of the different detectors utilized.

Using this measurement system, an interference file was recorded and then inserted in a Bluetooth communication model. The interference file was simulated with Matlab Simulink, to check how the interference affected the communication; the effects of the signal degradation are presented in a graphic.

Finally a real Bluetooth communication was established using two Bluetooth modules from Free2Move Company, to prove that the effect of microwave oven interferences produces the increase of transmission time and therefore decrease the Throughput.

The objective of this thesis work was to implement all the hardware and software necessary to simulate the functionality of a Vector Network Analyzer (VNA). With equipment that is already available, and is common in a measurement station, the most common functions of a VNA were implemented, using an Vector Signal Generator, that provide the signal for testing, and a Vector Signal Analyzer, to make all the amplitude and phase measurements. With these instruments and the appropriate software that control them, the basic functionality of a Vector Network Analyzer can be achieved with a reasonable accuracy. With this system, we can reduce costs, avoiding the need of a real VNA and take advantage off instruments that are already available in a laboratory. A Complete measurement system of all four scattering parameters is proposed at the end of the report for future implementation. With this implementation all the different S-parameter measurements were made with an acceptable accuracy that can be comparable to a commercial VNA.

In today's industrial landscape, the pursuit of operational excellence has driven organizations to seek innovative approaches to ensure the uninterrupted functionality of machinery and equipment. Predictive maintenance (PM) provides a pivotal strategy to achieve this goal by detecting faults earlier and predicting maintenance before the system enters a critical state. This thesis proposed a fault detection and diagnosis (FDD) method for predictive maintenance using particle filter resampling and a particle tracking technique. To develop this FDD method, particle filter and hidden Markov model efficiency in the forecasting system state variables are studied on a hydraulic wind power transfer system with different noise levels and system faults. Furthermore, a particle tracker is developed to analyze the particle filter's resampling process and study the particle selection process. After that, the proposed FDD method was developed and validated through three simulation tests employing system degradation models. Furthermore, the system's remaining useful life (RUL) is estimated for those simulation tests.

Over the last few decades, the complexity and variety of automation systems have increased dramatically. Commissioning has grown more and more critical for the entire industry. Conventional commissioning is time-consuming and expensive.

It's always been a challenge in manufacturing to put new designs into production or implement new technologies, control codes, or tactics.

In Virtual Commissioning (VC), control programs of the physical system's Digital Twin (DT) can be validated in Software-in-the-Loop (SIL) before the actual commissioning.

The emergence of new VC tools and methods has become a tremendous advantage, bringing the values of shorter duration, flexibility, and lower risks to the commissioning process.

In this thesis, advanced process control was developed using the software Matlab and Simulink in conjunction with the engineering tools S7-PLCSIM Advanced and STEP 7 TIA Portal to conduct VC.

A VC approach with four key steps is taken to evaluate the possibility of validating advanced process control. The steps are modeling DT of a rolling mill, model-based control design, simulation model development in Simulink, communication between the simulation model and the PLC program using S-7 TIA Portal, and PLCSIM Advanced. Also, a simulated Human-Machine Interface was designed to operate and visualize the process.

VC of the rolling mill process was verified and validated by Model-in-the-Loop (MIL) and SIL simulation. The simulation gives satisfactory results as both MIL and SIL show identical outputs of the process.

PID controllers are widely used in industrial control systems since it could significantly improve the performance of the systems. It is a basic and important tool for an engineering student.An educational PID controller model using LEGO Mindstorms NXT kit as a line following robot was presented in this thesis. This robot could be controlled by a PC via Bluetooth. MATLAB was used for analyzing data and controlling the robot. A MATLAB Toolbox named “RWTH—Mindstorms NXT Toolbox” was utilized for remote controlling and gathering the feedback data from the robot in real-time via Bluetooth.The algorithm and tuning methods of PID controllers were studied in this thesis. Moreover, the effectiveness of each parameter would be studied by comparing the result of P, PI and PID control type.

The road traffic density is continuously increasing. By the intensive use of automobiles, it comes

to considerable difficulties and unpredictable events. The frequency of traffic obstructions, traffic

jams and accidents will also increase in future. A solution for this problem would be that

the driver would be supplied information when he is on the road. The information should be

including about road and traffic conditions and also information about other vehicles, which in

the near vicinity.

This kind of information sharing between vehicles is called C2C communication.Especially in

Europe there are many projects which are working for different C2C communcation applications,

like.

Objective of this thesis is based on former works, which optimized the antenna positions

for C2C communication by ray tracing simulation. The investigation of antennas for the C2C

communication, two different approches are taken in to account, a narrow band, and broad band.

Investigation of transparent material for broad band is also the part of this thesis.

Ultrasonic thickness gauging of steel tubes is typically performed by observing the time difference between two following echoes. This process is often connected to various problems regarding interference. This thesis describes how to avoid these problems by using signal processing and analyzing the signals in the frequency domain.

To achieve these results a signal model that describes the necessary effects is derived. From this model different measurement methods to analyze and determine the thickness are developed.

One of the methods proved to be useful.