Maximum likelihood symbol timing estimation for communication over a frequency non-selective MIMO fading channel is developed. The cases of known data (data-aided estimation) and unknown data (non-data-aided estimation) together with known channel and unknown channel are considered. The analysis shows that the log-likelihood functions and their approximations can be interpreted as SISO log-likelihood functions operating on each of the receive antennas. Previously published symbol timing estimators are shown to be special cases of the more general framework presented. Architectures based on both block processing and sequential processing using a discrete-time phase-locked loop are summarized. Performance examples over a MIMO channel based on measured data and on a simple stochastic MIMO channel model are given. These examples show that the mean-squared error performance of these techniques is not strongly dependent on the MIMO channel and is able to reach the Cramer Rao bound when sufficient complexity is applied.

The usefulness of vibrotactile feedback as a channel to communicate information has been widely recognized. However, most of the recent work on this subject uses actuators that are either too expensive or too large for many practical applications. This thesis explores the generation of vibrotacatile feedback patterns using a simple, inexpensive eccentric-mass motor that is constrained to use a constant voltage and a low on/off switching frequency. In particular, it explores the pulse timing method, which utilizes the slow transient response of the eccentric-mass motor to calculate pulse and rest times for an arbitrary envelope waveform shape. Several hardware tests were performed to (1) obtain a model of the transient response and (2) to verify that the resulting vibrations match the patterns predicted by the pulse timing method. Two custom built devices consisting of an eccentric-mass motor and a rigid housing were used in addition to a Wii remote. Vibrations for each device were measured and compared to the pattern predicted by the pulse timing method when the device was sitting on a table top and when the device was held in the hand. Results indicate that the vibrations match the predicted patterns very well in both cases. It was also determined that error in the motor's transient response model will result in some error between the measured and predicted vibrations. To assess whether this error affects perception of the intended envelope waveform, a study was performed in which users were asked to identify the envelope waveform of vibration patterns created using curve-fit models that contained various levels of error. An analysis of variance revealed that error in the curve-fit will have an effect on the perception of the envelope waveform if the error is large. Two more user studies were performed to determine the perceptual space of patterns generated using the pulse timing method, and to determine whether users could identify the meanings encoded within vibration features. The Perceptual Space study used a cluster-sorted Multi-Dimensional Scaling analysis to determine that envelope waveform, roughness (deviation from the envelope waveform), and amplitude are vibration features that may be used to encode information. Using these features, participants were presented with vibrations that contained GPS navigation instructions similar to those used in a car, and were asked to identify the associated meaning. Users were able to correctly identify all three features with an average accuracy of 80.6%, and were able to correctly identify the envelope waveform and roughness with accuracies of 96.9% and 94.5% respectively. These results are evidence that the pulse timing method (and eccentric-mass motors in general) are capable of generating complex vibrotactile feedback patterns that can be uniquely identified.

The capacity expansion of a district heating system is studied with the objective of evaluating the investment decision timing and type of capacity expansion. District energy is an energy generation system that provides energy, such as heat and electricity, generated at central locations and distributed to the surrounding area. The study develops an optimization framework to find the optimal investment schedule over a 30 year horizon with the options of investing in traditional heating sources (boilers) or a next-generation combined heat and power (CHP) plant that can provide heat and electricity. In district energy systems, the investment decision on the capacity and type of system is dependent on demand-side requirements, energy prices, and environmental costs. The main contribution of this work is to formulate the capacity planning over a time horizon asa dynamic optimal control problem. In this way, an initial system configuration can be modified by a 'controller' that optimally applies control actions that drive the system from an initial state to an optimal state. The optimal control is a model predictive control (MPC) formulation that not only provides the timing and size of the capacity investment, but also guidance on the mode of operation that meets optimal economic objectives with the given capacity.

<p>   </p> <p>Historically, roadway performance measures have focused almost exclusively on vehicular movement. In most urban settings, pedestrian movements typically outnumber vehicular movements significantly. However,  historically there has been no way to collect such data at scale in a systematic manner. With the widespread introduction of cameras for monitoring vehicular flow, there is an opportunity to leverage this infrastructure to acquire insights into the patterns and trends of pedestrian activities at signalized intersections in an automated and systematic manner. Such data and performance measures are critical inputs for detailed analysis of pedestrian movements. Overall, addressing this issue is a vital component of transportation agencies that seek to develop equitable treatment of all transportation system users including vulnerable road users. This dissertation addresses the gap in the literature regarding detailed characterization of pedestrian movement patterns and trends. The dissertation leverages data from signalized intersection cameras to (1) quantify the required duration for the pedestrian walk-interval based on pedestrian volume and geometric features of the intersection, (2) carry out time series analysis to acquire insights on pedestrian demand patterns and the influential variables, and (3) build machine learning algorithms to accurately predict pedestrian volumes and tie it to signal timing, to enhance service for all roadway users.</p> <p>The first study provides quantitative guidance for walk time interval selection. This part reports on 1,500 pedestrian movement observations from 12 signalized intersections with varying pedestrian demand, pedestrian storage areas, and pedestrian push-button locations. That data were used to develop a model predicting start-up time with an R2 of 0.89. The study concludes by presenting a quantitative table with four timing categories ranging from negligible volume to high volume and corresponding appropriate durations for the pedestrian walk interval time, based on the demand per cycle, storage area for pedestrians, and offset of the pedestrian push-button from the crosswalk.</p> <p>The second study describes several scalable techniques for measuring and analyzing the movement of pedestrians on a typical university campus. Approximately 35.6 million pedestrian movements over 19 months were tabulated in 15-minute counts of pedestrian volumes by intersection. Counts are used in evaluating pedestrian activity dependency on select explanatory variables at both the network and intersection levels at each time step for the entire analysis period. The study reports on time series correlation and cross-correlation and measures the time-dependency between pedestrian activities and influential factors such as the academic calendar, football games, basketball games, and graduation ceremonies. It provides a comprehensive understanding of the factors that are most influential of  pedestrian volumes at intersections.</p> <p>The third study presents a data-driven approach to predict pedestrian volume per intersection quadrant at 15-minute intervals, and to connect this information to signal timing. Machine learning random forest and XGBoost classification models were trained on a large dataset of pedestrian counts consisting of approximately 2.6 million observations collected through 19 months at 13 exclusive pedestrian service intersections. The predicted pedestrian volumes were then categorized per the pedestrian walk-interval categories to provide optimal signal timing for each intersection quadrant, thus enabling potential dynamic pedestrian signal timing at exclusive service intersections. The results of this study showed that the developed models accurately predict pedestrian volumes per 15-minute intervals for each quadrant of an intersection, with a high degree of precision and a prediction accuracy of 82.3%. Signal timing optimization based on predicted pedestrian volume can significantly improve pedestrian mobility and maximize traffic flow. </p> <p>The findings of this study provide valuable insights for traffic engineers and planners interested in developing and deploying dynamic pedestrian signal timing systems. It is a practical and effective solution for improving mobility for all roadway users at intersections with exclusive pedestrian service.</p> <p>  </p>

Covid-19-pandemin ändrade snabbt förutsättningarna på kapitalmarknaden och företagen tvingades fort att anpassa sig efter rådande restriktioner och rekommendationer. Denna studie undersöker hur kapitalstrukturen i bolag noterade på Stockholmsbörsen förändrades under pandemiåren 2020–2021 jämfört med före pandemin (2016–2019). Med hjälp av teorier för kapitalstruktur och tidigare studier identifierades fem centrala mått som hjälper till att förklara skuldsättningsgraden. Genom multivariat analys studeras och analyseras vilka variabler och samband som har påverkat kapitalstrukturen under perioden. Studiens resultat visar, i likhet med tidigare studier kring agerande under krissituationer, att företagens skuldsättningsgrad minskade under jämfört med före pandemin.

Time synchronization is critical for the operation of radio base stations (RBS) in telecommunication companies. Global navigation satellite system (GNSS) is an existing technology to provide precise timing information to distributed RBSs. GNSS timing receiver is used for providing higher timing accuracy than normal GNSS receiver in this synchronization domain.In this thesis, an experiment method for GNSS timing receiver performance in urban canyon has been designed and implemented to evaluate information and the quality of the one pulse per second (1PPS) signal generated by two different GNSS timing receivers. Multi-path signals and the gathered satellite geometry caused by poor sky visibility is identified as the main influential factors to the performance of the GNSS timing receivers. A mathematical model has been built for estimating the multi-path effect. GNSS planning tools are used to simulate the number of line-of-sight (LOS) satellites and Dilution of Precision (DOP) value.Sentinel is a 1PPS signal analyzing equipment from Calnex. Sentinel has an embedded rubidium clock, GNSS antenna, and receiver, and it can produce 1PPS signals to be used as a reference. In this report, we installed our GNSS antenna of Sentinel on the roof and test GNSS antenna in two specified positions representing urban canyon and rooftop. Recorded NMEA messages from GNSS receiver can help us to study the number of visible satellites, PDOP value and multi-path signals in realistic situations.The results show how the noise and time phase of 1PPS signals will be influenced in urban canyons. Since, the geometry of used satellites is similar to the rooftop situation, the multi-path effect of signals is identified as the main reason of this difference.This information is useful when telecommunication companies want to install their radio base station in urban canyons. It will help Ericsson to understand how their GNSS timing receiver is working and how the urban canyon will influence its performance. / Tidssynkronisering är kritisk för driften av radiobasstationer (RBS) i telekommunikationsföretag. Global Navigation Satellite System (GNSS) är en befintlig teknik för att ge exakt tidsinformation till distribuerade basstationer. GNSS-baserade tidsmottagare används för att ge högre timing-noggrannhet än vanlig GNSS mottagare i denna synkroniseringsdomän. I denna avhandling har en experimentmetod för GNSS-timingmottagarnas prestanda i urban canyon utformats och implementerats för att utvärdera den genererade informationen och kvaliteten på en puls per sekund-signal (1PPS). Flervägssignaler och den samlade satellitgeometrin som orsakas av dålig himmelsynlighet identifieras som de mest inflytelserika faktorerna för GNSS-tidsmottagarnas prestanda. En matematisk modell har donstruerats för att estimera multi-path-effekten. GNSS-planeringsverktyg används för att simulera antalet LOS-satelliter och DOP-värde (Dilution of Precision). Sentinel är en 1PPS signalanalysutrustning från Calnex. Sentinel har en inbyggd rubidiumklocka, GNSS-antenn och mottagare, och den kan producera 1PPS-signaler som ska användas som referens. I den här rapporten installerade vi vår GNSS-antenn på Sentinel på taket och GNSS-testantennen i två angivna positioner som representerar urban canyon och tak. Inspelade NMEA-meddelanden från GNSS-mottagare kan hjälpa oss att studera antalet synliga satelliter, PDOP-värde och flervägssignaler i realistiska scenarier. Resultatet visar att ljud- och tidsfasen för 1PPS-signaler påverkas i urban canyons. Eftersom satellitgeometrin liknar den för antenner placerade på taket, så är identifieras flervägsutbredningen som huvudorsak för denna skillnad. Denna information är användbar när telekommunikationsföretag vill installera sina radiobasstationer i urban canyons. Det kommer att hjälpa Ericsson att förstå hur deras GNSS-timingmottagare arbetar och hur urban canyon påverkar dess prestanda.

Monolithic passively mode-locked colliding pulse semiconductor lasers generating pico- to sub-picosecond terahertz optical pulse trains are promising sources for future applications in ultra-high speed data transmission systems and optical measurements. However, in the absence of external synchronization, these passively mode-locked lasers suffer from large amplitude and timing jitter instabilities resulting in broad comb linewidths, which precludes many applications in the field of coherent communications and signal processing where a much narrower frequency line set is needed. In this dissertation, a novel quantum dot based coupled cavity laser is presented, where for the first time, four-wave mixing (FWM) in the monolithically integrated saturable absorber is used to injection lock a monolithic colliding pulse mode-locked (CPM) laser with a mode-locked high-Q ring laser. Starting with a passively mode-locked master ring laser, a stable 30 GHz optical pulse train is generated with more than 10 dB reduction in the RF noise level at 20 MHz offset and close to 3-times reduction in the average optical linewidth of the injection locked CPM slave laser. The FWM process is subsequently verified experimentally and conclusively shown to be the primary mechanism responsible for the observed injection locking. Other linear scattering effects are found to be negligible, as predicted in the orthogonal waveguide configuration. The novel injection locking technique is further exploited by employing optical hybrid mode-locking and increasing the Q of the master ring cavity, to realize an improved stabilization architecture. Dramatic reduction is shown with more than 14-times reduction in the photodetected beat linewidth and almost 5-times reduction in the optical linewidth of the injection locked slave laser with generation of close to transform limited pulses at ~ 30 GHz. These results demonstrate the effectiveness of the novel injection locking technique for an all-on-chip stability transfer and provides a new way of stabilizing monolithic optical pulse sources for applications in future high speed optical networks.

The Java platform is becoming a vital tool for developing real-time and safety-critical systems. Design patterns and the availability of Java libraries, both provide solutions to many known problems. Furthermore, the object-oriented nature of Java simplifies modular development of real-time systems. However, limitations of Java as a programming language for real-time systems are a notable obstacle to producing safe real-time systems. These limitations are found in the unpredictable execution model of the language, due to Java’s garbage collector, and the lack of support for non-functional specification and verification tools. In this dissertation I introduce SafeJML, a specification language for support of functional and non-functional specifications, based on an implementation of a safety-critical Java platform and the Java Modeling Language (JML). This dissertation concentrates on techniques that enable specification and dynamic checking of timing constraints for some important Java features, including methods and subtyping. SafeJML and these dynamic checking techniques allow modular specification and checking of safety-critical systems, including those that use object-orientation and design patterns. Such coding techniques could have maintenance benefits for real-time and safety-critical software

A low-cost radio direction finding (RDF) VHF receiver has been investigated for development into a radio direction finding network (RDFN) with a particular focus towards public service and commercial asset tracking applications. The primary design criteria were reproducibility, low-cost, and simplicity such that public service and volunteer organizations can benefit from the technology. Two receiver designs were built and tested to allow for comparison of practicality, cost, and accuracy. A pseudo-Doppler RDF and a time difference of arrival (TDOA) receiver were built as proof-of-concept for a system design based on commercial off-the-shelf (COTS) components. The pseudo-Doppler system is a less practical implementation due to the necessity for custom hardware, a large antenna system, and an increased directional error due to multipath and weak signals. The TDOA system has potential as a very simple and low-cost RDFN implementation, but requires extremely accurate time synchronization that is difficult to achieve using COTS GPS receiver modules. The final proposed solution takes advantage of the simple TDOA hardware and multiple detection techniques (including signal strength) to produce improved locational data and ultimately provide a more accurate estimate of position. Further development and improvements to this receiver design have the potential for implementation as a low-cost radio direction finding network.

Secure Shell, a tool to securely access and run programs on a remote machine, is an important tool for both system administrators and developers alike. The technology landscape is becoming increasingly distributed and reliant on tools such as Secure Shell to protect information as a user works on a system remotely. While Secure Shell accounts for the abuses the security of older tools such as telnet overlook, it still has fundamental vulnerabilities which leak information about both the user and their activities through timing attacks. The OpenSSH client, the implementation included in all Linux, Mac, and Windows computers, sends each keystroke entered to the server as soon as it becomes available. As a result, an attacker can observe the network patterns to know when a user presses a key and draw conclusions based on that information such as what a user is typing or who they are. In this thesis, we demonstrate that such an attack allows a malicious observer to identify a user with a concerning level of accuracy without having direct access to either the client or server systems. Using machine learning classifiers, we identify individual users in a crowd based solely on the size and timing of packets traveling across the network. We find that our classifiers were able to identify users with 20\% accuracy using as little as one hour of network traffic. Two of them promise to scale well to the number of users.