Traffic State Estimation (TSE) is a vital component in traffic control which requires an accurate viewof the current traffic situation. Since there is no full sensor coverage and the collected measurementsare inflicted with random noise, statistical estimation techniques are necessary to accomplish this.Common methods, which have been used in highway applications for several decades, are state-spacemodels in the form of Kalman Filters and Particle Filters. These methods are forms of BayesianFilters, and rely on transition models to describe the system dynamics, and observation models torelate collected measurements to the current state. Reliable estimation of traffic in urban environmentshas been considered more difficult than in highways owing to the increased complexity.This MsC thesis build upon previous research studying the use of non-parametric Gaussian Processtransition and measurement models in an extended Kalman Filter to achieve short-term TSE. To dothis, models requiring different feature sets are developed and analysed, as well as a hybrid approchcombining non-parametric and parametric models through an analytical mean function based on vehicleconservation law. The data used to train and test the models was collected in a simulated signalizedintersection constructed in SUMO.The presented results show that the proposed method has potential to performing short-term TSE inthis context. A strength in the proposed framework comes from the probabilistic nature of the GaussianProcesses, as it removes the need to manually calibrate the filter parameters of the Kalman Filter. Themean absolute error (MAE) lies between one and five vehicles for estimation of a one hour long dataseries with varying traffic demand. More importantly, the method has desirable characteristics andcaptures short-term fluctuations as well as larger scale demand changes better than a previously proposedmodel using the same underlying framework. In the cases with poorer performance, the methodprovided estimates unrelated to the system dynamics as well as large error bounds. While the causefor this was not determined, several hypotheses are presented and analysed. These results are takento imply that the combination of BF and GP models has potential for short-term TSE in a signalizedintersection, but that more work is necessary to provide reliable algorithms with known bounds. In particular,the relative ease of augmenting an available analytical model, built on conventional knowledgein traffic modelling, with a non-parametric GP is highlighted.

Enabling vehicles to preempt the normal operation of traffic signals has the potential to improve the safety and efficiency of both the requesting vehicle and all of the other vehicles. Little is known about which strategy is the most effective to exit from preemption control and transition back to the traffic signals normal timing plan. Common among these traffic signal transition strategies is the method of either increasing or decreasing the cycle length of the signal timing plan, as the process followed to return to the coordination point of the effected signal timing plan, to coordinate its operation with adjacent traffic signals. This research evaluates commonly available transition strategies: best way, long, short, and hold strategies. The major contribution of this research is enhancing the methodology to evaluate the impacts of using these alternative transition strategies. Part of this methodology consists of the "software-in-the-loop" simulation tool which replicates the stochastic characteristics of traffic flow under different traffic volume levels. This tool combines the software from a traffic signal controller (Gardner NextPhase Suitcase Tester, version 1.4B) with a microscopic traffic simulation model (CORSIM, TSIS 5.2 beta version). The research concludes that a statistically significant interaction exists between traffic volume levels and traffic signal transition strategies. This interaction eliminates the ability to determine the isolated effects of either the transition strategies on average travel delay and average travel time, or the effects of changes in traffic volume levels on average travel delay and average travel time. Conclusions, however, could be drawn on the performance of different transition strategies for specific traffic volume levels. As a result, selecting the most effective transition strategy needs to be based on the traffic volume levels and conditions specific to each traffic signal or series of coordinated traffic signals. The research also concludes that for the base traffic volume and a 40% increase in traffic volume, the most effective transition strategies are the best way, long or hold alternatives. The best way was the most effective transition strategy for a 20% increase in traffic volume. The least effective strategy is the short transition strategy for both the base and 40% increase in traffic volume, and the long and short for a 20% increase in traffic volume. Further research needs to be conducted to assess the performance of different transition strategies in returning to coordinated operation under higher levels of traffic volume (e.g., approaching or exceeding congested flow regime), with varying cycle lengths, with different signal timing plans, and when different roadway geometric configurations (e.g., turn lanes, length of turn lanes, number of lanes, spacing between intersections) are present. / Ph. D.

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de s / Traver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188

<p dir="ltr">Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. </p><p dir="ltr">The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), MobileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90\% for the combination of ECG and EMG signals. </p><p dir="ltr">Furthermore, this research explores the importance of analyzing dynamic physiological activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to identify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. </p><p dir="ltr">Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health. </p>

All wireless receivers have some degree of nonlinearity that can negatively impact performance. Two major effects from this nonlinearity are power compression, which leads to amplitude and phase distortions in the received signal, and desensitization caused by a high powered interfering signal at an adjacent channel. As the RF spectrum becomes more crowded, the interference caused by these adjacent signals will become a more significant problem for receiver design. Therefore, having bit and symbol error rate expressions that take the receiver nonlinearity into account will allow for determining the linearity requirements of a receiver. This thesis examines the modeling of the probability density functions of M-PAM and M-QAM signals through an AWGN channel taking into account the impact of receiver nonlinearity. A change of variables technique is used to provide a relationship between the pdf of these signals with a linear receiver and the pdf with a nonlinear receiver. Additionally, theoretical bit and symbol error rates are derived from the pdf expressions. Finally, this approach is extended by deriving pdf and error rate expressions for these signals when nearby blocking signals cause desensitization of the signal of interest. Matlab simulation shows that the derived expressions for a nonlinear receiver have the same accuracy as the accepted expressions for linear receivers. / Master of Science / All wireless receivers have some amount of nonlinearity that can distort a received signal and impact performance. For amplitude modulated signals, the power compression caused by the nonlinear receiver will cause distortions in the amplitude and phase of the received signal. Additionally, a high powered interfering signal at a close frequency can decrease the gain and distort the received signal. This thesis examines how the probability density of an amplitude modulated signal with a nonlinear receiver can be modeled for both of these situations. These theoretical probability density functions are used to derive theoretical error rate expressions for the signals both with and without the adjacent channel interference. Simulations in Matlab show that the accuracy of these derived expressions is similar to the accuracies of the linear receiver expressions. These derived expressions will be able to remove the need for time consuming simulation when designing receivers for wireless systems.

This thesis attempts to provide both an explanatory model and to forecast settlements using large-n statistical analysis and machine learning. By asking the question of how costly signals affect the likelihood of conflict settlement, and drawing upon the literature on bargaining and signaling, it argues that when the challenging actor publicly state their demands and policy desires, they are sending costly signals, revealing information about their willingness to fight. This information is used by conflict parties to recalculate costs of war, causing them to eventually locate an agreement which both parties prefer continued fighting. As such, the mechanism suggests that a greater number of such signals means a greater chance at locating such an agreement, resulting in a greater chance of settlement. Additionally, connecting the signal to the issue at stake, I argue that territorial signals would be especially important, in part because they are often seen as indivisible, suggesting that signals relating to territory would be especially important relative to signals of comparable policy domains. The results are statistically significant in support of the first hypothesis but findno benefit to predictive performance from costly signals. In contracts territorial signals are neither statistically significant, nor contribute to predictive performance.

No description available.

ABSTRACT Date:                           02 June 2024 Level:                          Master thesis in Product and Process Development, advanced level, 30                                       credits. Institution:                 School of Innovation, Design and Engineering, Mälardalen University Author:                       Aziz EzzAlden                        Zain Momin    20th November 1998  10th August 1998                                Title:                           Optimizing Logistic Operations: exploring diverse approaches to order                                         point  dynamics  Tutor:                         Ebba Bengtsson, Organisation                                     Yuji Yamamoto, Mälardalen University Keywords:                  RFID, Kanban; Push; Pull; Just In Time; internal logistics; Signals;                                      Material flow; Order point Purpose:                     The aim of the study was to investigate the impact of order points on                                           logistics operations and how smart inventory technologies address the                                                 challenges of order  point management. By utilizing a framework that                                                                  aligns with logistics operations and smart inventory practices.  Research  questions:  1. How does order point affect the logistic operations and their                                              performance?                           2. How can smart inventory address the challenges related to order                           points?  Method:                      This study adopts an abductive approach, facilitating a dynamic                                      interaction between theoretical frameworks and empirical findings.                                       The theoretical framework is based on scientific articles. The study                                      focuses on a singular main case organisation, supplemented by                                                    comparative analyses involving three additional organisations,                                       forming a case study design. Empirical data allocation used multiple                                       semi-structured interviews, unstructured interviews, and observations                                       to gather data.  Conclusion:                The study indicated that efficient order point systems are crucial for                                        ensuring timely material replenishment and optimizing material flow.                                        Additionally, smart inventory technologies offer effective solutions,                                        including real-time monitoring and automated reordering, which                                        minimize errors and enhance efficiency in logistical operations. / SAMMANFATTNING Datum:                        02 juni 2024 Nivå:                            Examensarbete i produkt- och processutveckling avancerad nivå, 30 hp.                                     Institution:                  Akademin för Innovation. Design och Teknik. IDT, Mälardalens                                                 universitet  Författare:                  Aziz EzzAlden                        Zain Momin     20 November 1998   10 Augusti 1998                                Titel:                            Optimering av logistik operationer: utforska olika tillvägagångssätt för                                        order punkts dynamik. Handledare:               Ebba Bengtsson, Organisation                                     Yuji Yamamoto, Mälardalens universitet Nyckelord:                  RFID, Kanban; Push, Pull; Just In Time; instant logistics; Signaler,                                       Materialflöde; Order Punkt Syfte:                          Syftet med studien var att undersöka hur orderpunkter påverkar                                      logistik operationer och hur smarta lager teknologier hanterar                                      utmaningarna med orderpunkt hantering. Detta genom att använda en                                    ram som överensstämmer med logistikoperationer och smarta                                    lagerpraxis. Frågeställningar:       1. Hur påverkar order punkten logistikoperationer och deras prestanda?                                      2. Hur kan smarta lager hantera utmaningarna relaterade till                                                                order punkter? Metod:                          Denna studie antar ett abduktivt tillvägagångssätt som underlättar en                                         dynamisk interaktion mellan teoretiska ramverk och empiriska resultat.                                        Det teoretiska ramverket är baserat på vetenskapliga artiklar. Studien                                           fokuserar på en enda huvudorganisation, kompletterad med jämförande                                          analyser som involverar tre ytterligare organisationer, vilket bildar en                                         fallstudiedesign. Empiriska datainsamlingen använde sig av flera                                          semi-strukturerade intervjuer, ostrukturerade intervjuer och                                                observationer för att samla in data. Slutsats:                       Studien indikerade att effektiva orderpunkt system är avgörande för att                                        säkerställa att material återfylls i rätt tid och för att optimera                                       materialflödet. Dessutom erbjuder smarta lager teknologier effektiva                                       lösningar, inklusive realtidsövervakning och automatisk ombeställning,                                        vilket minimerar fel och ökar effektiviteten i logistikoperationer.

The last advancements in technology leads to an easy acquisition and spreading of multi-dimensional multimedia content, e.g. videos, which in many cases depict human faces. From such videos, valuable information describing the intrinsic characteristic of the recorded user can be retrieved: the features extracted from the facial patch are relevant descriptors that allow for the measurement of subject's emotional status or the identification of synthetic characters. One of the emerging challenges is the development of contactless approaches based on face analysis aiming at measuring the emotional status of the subject without placing sensors that limit or bias his experience. This raises even more interest in the context of Quality of Experience (QoE) measurement, or the measurement of user emotional status when subjected to a multimedia content, since it allows for retrieving the overall acceptability of the content as perceived by the end user. Measuring the impact of a given content to the user can have many implications from both the content producer and the end-user perspectives. For this reason, we pursue the QoE assessment of a user watching multimedia stimuli, i.e. 3D-movies, through the analysis of his facial features acquired by means of contactless approaches. More specifically, the user's Heart Rate (HR) was retrieved by using computer vision techniques applied to the facial recording of the subject and then analysed in order to compute the level of engagement. We show that the proposed framework is effective for long video sequences, being robust to facial movements and illumination changes. We validate it on a dataset of 64 sequences where users observe 3D movies selected to induce variations in users' emotional status. From one hand understanding the interaction between the user's perception of the content and his cognitive-emotional aspects leads to many opportunities to content producers, which may influence people's emotional statuses according to needs that can be driven by political, social, or business interests. On the other hand, the end-user must be aware of the authenticity of the content being watched: advancements in computer renderings allowed for the spreading of fake subjects in videos. Because of this, as a second challenge we target the identification of CG characters in videos by applying two different approaches. We firstly exploit the idea that fake characters do not present any pulse rate signal, while humans' pulse rate is expressed by a sinusoidal signal. The application of computer vision techniques on a facial video allows for the contactless estimation of the subject's HR, thus leading to the identification of signals that lack of a strong sinusoidality, which represent virtual humans. The proposed pipeline allows for a fully automated discrimination, validated on a dataset consisting of 104 videos. Secondly, we make use of facial spatio-temporal texture dynamics that reveal the artefacts introduced by computer renderings techniques when creating a manipulation, e.g. face swapping, on videos depicting human faces. To do so, we consider multiple temporal video segments on which we estimated multi-dimensional (spatial and temporal) texture features. A binary decision of the joint analysis of such features is applied to strengthen the classification accuracy. This is achieved through the use of Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP). Experimental analyses on state-of-the-art datasets of manipulated videos show the discriminative power of such descriptors in separating real and manipulated sequences and identifying the creation method used. The main finding of this thesis is the relevance of facial features in describing intrinsic characteristics of humans. These can be used to retrieve significant information like the physiological response to multimedia stimuli or the authenticity of the human being itself. The application of the proposed approaches also on benchmark dataset returned good results, thus demonstrating real advancements in this research field. In addition to that, these methods can be extended to different practical application, from the autonomous driving safety checks to the identification of spoofing attacks, from the medical check-ups when doing sports to the users' engagement measurement when watching advertising. Because of this, we encourage further investigations in such direction, in order to improve the robustness of the methods, thus allowing for the application to increasingly challenging scenarios.

Data acquisition from multiple analog channels is an important function in many embedded devices used in avionics, medical electronics, robotics and space applications. It is desirable to engineer these systems to reduce their size, power consumption, heat dissipation and cost. The goal of this research is to explore designs that exploit a priori knowledge of the input signals in order to achieve these objectives. Sparsity is a commonly observed property in signals that facilitates sub-Nyquist sampling and reconstruction through compressed sensing, thereby reducing the number of A to D conversions. New architectures are proposed for the real-time, compressed acquisition of streaming signals. A. It is demonstrated that by sampling a collection of signals in a multiplexed fashion, it is possible to efficiently utilize all the available sampling cycles of the analogue-to-digital converters (ADCs), facilitating the acquisition of multiple signals using fewer ADCs. The proposed method is modified to accommodate more general signals, for which spectral leakage, due to the occurrence of non-integral number of cycles in the reconstruction window, violates the sparsity assumption. When the objective is to only detect the constituent frequencies in the signals, as against exact reconstruction, it can be achieved surprisingly well even in the presence of severe noise (SNR ~ 5 dB) and considerable undersampling. This has been applied to the detection of the carrier frequency in a noisy FM signal. Information redundancy due to inter-signal correlation gives scope for compressed acquisition of a set of signals that may not be individually sparse. A scheme has been proposed in which the correlation structure in a set of signals is progressively learnt within a small fraction of the duration of acquisition, because of which only a few ADCs are adequate for capturing the signals. Signals from the different channels of EEG possess significant correlation. Employing signals taken from the Physionet database, the correlation structure of nearby EEG electrodes was captured. Subsequent to this training phase, the learnt KLT matrix has been used to reconstruct signals of all the electrodes with reasonably good accuracy from the recordings of a subset of electrodes. Average error is below 10% between the original and reconstructed signals with respect to the power in delta, theta and alpha bands: and below 15% in the beta band. It was also possible to reconstruct all the channels in the 10-10 system of electrode placement with an average error less than 8% using recordings on the sparser 10-20 system. In another design, a set of signals are collectively sampled on a finer sampling grid using ADCs driven by phase-shifted clocks. Thus, each signal is sampled at an effective rate that is a multiple of the ADC sampling rate. So, it is possible to have a less steep transition between the pass band and the stop band, thereby reducing the order of the anti-aliasing filter from 30 to 8. This scheme has been applied to the acquisition of voltages proportional to the deflection of the control surfaces in an aerospace vehicle. The idle sampling cycles of an ADC that performs compressive sub-sampling of a sparse signal, can be used to acquire the residue left after a coarse low-resolution sample is taken in the preceding cycle, like in a pipelined ADC. Using a general purpose, low resolution ADC, a DAC and a summer, one can acquire a sparse signal with double the resolution of the ADC, without having to use a dedicated pipelined ADC. It has also been demonstrated as to how this idea can be applied to achieve a higher dynamic range in the acquisition of fetal electrocardiogram signals. Finally, it is possible to combine more than one of the proposed schemes, to handle acquisition of diverse signals with di_erent kinds of sparsity. The implementation of the proposed schemes in such an integrated design can share common hardware components so as to achieve a compact design.