Traffic signals typically produce vehicle stops and thus increase vehicle fuel consumption levels. Vehicle stops produced by traffic signals, decrease vehicle fuel economy on arterial roads making it significantly lower than that on freeways. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems can improve vehicle fuel efficiency by receiving Signal Phasing and Timing (SPaT) data form downstream signalized intersections via vehicle-to-infrastructure communication. The algorithm that was developed in an earlier study provides advisory speed recommendations to drivers to reduce vehicle fuel consumption levels in the vicinity of traffic signalized intersections. The research presented in this thesis enhances the algorithm by adding a queue length estimation component and incorporates the algorithm in the INTEGRATION microscopic traffic simulation software to test the system under varying conditions. The enhanced Eco-CACC algorithm is then tested in a simulation environment considering different levels of connected vehicle (CV) market penetration levels. The simulation analysis demonstrates that the algorithm is able to reduce the vehicle fuel consumption level by as high as 40%. Moreover, the overall benefits of the proposed algorithm is evaluated for different intersection configurations and CV market penetration rates (MPRs). The results demonstrate that for single lane approaches, the algorithm can reduce the overall fuel consumption levels and that higher MPRs result in larger savings. While for multilane approaches, lower MPRs produce negative impacts on fuel efficiency; only when MPRs are greater than 30%, can the algorithm work effectively in reducing fuel consumption levels. Subsequently a sensitivity analysis is conducted. The sensitivity analysis demonstrates that higher market penetration rates of Eco-CACC enabled vehicles can improve the environmental benefits of the algorithm, and the overall savings in fuel consumption are as high as 19% when all vehicles are equipped with the system. While, on multi-lane approaches, the algorithm has negative impacts on fuel consumption levels when the market penetration rate is lower than 30 percent. The analysis also indicates that the length of control segments, the SPaT plan, and the traffic demand levels affect the algorithm performance significantly. The study further demonstrates that the algorithm has negative impacts on fuel consumption levels when the network is over-saturated. / Master of Science

This report represents a concentrated effort that determines the feasibility of audible pedestrian signals. These signals are devices which give auditory cues to help the visually impaired cross safely at difficult intersections. Surveys were sent out to over 100 organizations, audible signal manufacturers, and cities who have knowledge of the devices, and responses were analyzed. The devices were found to be feasible but only at certain complex and confusing intersections. Twelve criteria for the installation of the devices were developed as were twelve criteria for the operation of the devices. Buzzers, constant tones, bird calls, and voice signals were examined by obtaining information from traffic engineers who had experience with each sound. It was determined that intermittent tones were the most effective for human localization. For the most widely used devices, cost data were developed for the products, installation, and maintenance. A partial listing of the U.S. and foreign cities which have the devices was compiled along with a partial listing of audible signal manufacturers. The problems the visually impaired face as well as their suggested solutions are listed. Topics for further study include the use of hand-held devices which activate sound signals at intersections and the development of tone schemes for 4-leg and multi-leg intersections which are not north south and east-west. An additional topic for future study is the development of tone schemes for traffic circles. / Master of Science

Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, horticulture, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatiotemporal resolution information about surface SM is crucial. However, the use of invasive SM probes and other sensors is expensive and requires extensive manpower. Moreover, these intrusive techniques provide point measurements and are unsuitable for large agricultural fields. As an alternative, this dissertation explores the remote sensing of surface SM by utilizing the surface reflectivity estimated from global navigation satellite systems reflectometry (GNSS-R) data acquired through smartphones and off-the-shelf, cost-effective U-blox global navigation satellite systems (GNSS) receivers. To estimate surface reflectivity, the GNSS receivers are attached underneath a small unmanned aircraft system (UAS), which flies over agricultural fields. Additionally, this dissertation investigates a fully custom UAS-based dual-polarized L-band microwave radiometric measurement technique over agricultural areas to estimate surface brightness temperature (����). The radiometer measures surface emissivity as ����, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This radiometer processes the data in near real-time onboard the UAS, collecting raw in-phase and quadratic (I&Q) signals across the study field. This feature mitigates the RFI onboard and significantly reduces post-processing time. In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in conjunction with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for the estimation of surface reflectivity and ����. The radiometric measurement of surface emissivity is related to surface reflectivity through the relationship (Emissivity = 1 -Reflectivity).

Yes / Sensory adaptation experiments have revealed the existence of ‘rate after-effects’ - adapting to a relatively fast rate makes an intermediate test rate feel slow, and adapting to a slow rate makes the same moderate test rate feel fast. The present work aims to deconstruct the concept of rate and clarify how exactly the brain processes a regular sequence of sensory signals. We ask whether rate forms a distinct perceptual metric, or whether it is simply the perceptual aggregate of the intervals between its component signals. Subjects were exposed to auditory or visual temporal rates (a ‘slow’ rate of 1.5 Hz and a ‘fast’ rate of 6 Hz), before being tested with single unfilled intervals of varying durations. Results show adapting to a given rate strongly influences the perceived duration of a single empty interval. This effect is robust across both interval reproduction and duration discrimination judgments. These findings challenge our understanding of rate perception. Specifically, they suggest that contrary to some previous assertions, the perception of sequence rate is strongly influenced by the perception of the sequence’s component duration intervals. / This work was supported by a Wellcome Trust [WT097387] grant to NWR

In the traffic engineering field today, much attention is being given to the area of intersection control. The intersection has long been recognized as the most critical element in our highway system. Accidents, delay, wasted fuel and congestion are greatest at intersections. The variable having the greatest effect on traffic flow at an intersection or in a network of intersections is the traffic signal timing. In recent years, several computer programs have been developed to aid the traffic engineer in signal timing. This thesis examines the effect of the signal timing plans generated by one of the more widely used programs, TRANSYT 7, on the energy consumption of two signalized networks. Also examined are the relationships of delay and stops to fuel consumption. The TRANSYT 7 program was used to generate signal timing plans over a range of cycle lengths and stop penalties. The TRANSYT 7 signal timing plans were entered into NETSIM, a microscopic traffic simulation program, to determine their effect on fuel consumption in the two study networks. / Master of Science

Earth science observations are crucial for our understanding of the Earth’s climate, water cycle, land, and atmosphere. Signals of Opportunity (SoOp) has recently emerged as an innovative method for producing these observations. SoOp reuses existing satellite communication signals for science measurements. A key factor in the accuracy of SoOp measurements, is the accuracy with which the transmitting satellite’s position can be determined. This thesis developed a distributed network of receivers, which performed time delay of arrival (TDOA) measurements, to solve for the position of a transmitting satellite, using their existing signals. These results were used to characterize the sensitivity of the calculated satellite position, to the TDOA measurement error.

Special effects (SFX) for film, television, and any type of video are usually digital, practical, or a mix of both. Animatronics is the use of robotics to mimic living movements, which is often used in the SFX industry. Currently, there is a gap between practical and digital effects, which means that post-production of practical effects leads to an inefficient workload. A robot is recorded on video, and if any digital enhancements or corrections are needed, the digital artists must start from nothing but the recorded video. Since the practical effects – controlling the animatronic- are using RC signals to manipulate servomotors, there is movement information that could be used to “record” movement into a digital space. This thesis project aims to create a way to bridge the gap between digital and physical by translating RC signals into digital animation. The RC signals used in this project were sent via a Taranis remote control, mixed using Open-TX software, and then translated into a digital animation rig in the 3D software Blender.

The impact of electromagnetic interference (EMI) from all kinds of electronic devices could be a severe problem for the reliability on communication systems. Many of the services and functions we use everyday in society are built on radio communication. Examples of this could be to use WiFi, Bluetooth or listen to FM radio. These wanted signals is though always exposed to noise and unwanted EMI, by for example a solar cell plant or an office with several computers. Radio receivers uses as weak signals as possible to be energy efficient and they are therefore sensitive to all kinds of interfering signals.  The interfering signals that affect the communication system is in this report modeled as sinusoidals. It is investigated how the sum of multiple similar interfering signals could be modeled and which effects the models have on the performance of a communication system. Having similar interfering signals implies having the same amplitude and roughly the same frequency but the phase may be individual. It is common to approximate the sum of interfering signals as additive white Gaussian noise (AWGN) with total power corresponding to the sum of the individual interfering signals' power. However, it is not stated when this power summation approximation is valid to use in terms of the interfering signals' characteristics. The thesis' results are obtained by simulating interfering signals and their effect on a basic communication system. The performance of the system is evaluated primarily by the bit error rate (BER) and three cases are found when the power summation approximation is not valid. The first case is when the sent signal and the interfering signal(s) have the exact same frequency, the second case is when the number of interfering signals is below three, and the third case is when the interfering signals' aggregated maximum amplitude (each interfering signals amplitude times the number of interferers) is below, or near, the amplitude of the sent signal. / Påverkan från elektromagnetiska signaler från olika typer av elektroniska apparater kan vara ett stort problem för ett kommunikationssystems prestanda. Många av de tjänster och funktioner vi använder dagligen i samhället bygger på radio-kommunikation. Exempel på detta kan vara att använda WiFi, Bluetooth eller lyssna på FM radio. Dessa önskade signaler, nyttosignaler, utsätts dock alltid för brus och oönskade störsignaler, från till exempel en solcellspark eller ett kontor med många datorer. Radiomottagare använder så svaga signaler som möjligt för att vara energieffektiva och de är därför känsliga för alla typer av störsignaler.  Störsignalerna som påverkar kommunikationssystemet är i denna rapport modellerade som sinusformade signaler. Det undersöks hur summan av flertalet liknande störsignaler kan modelleras och vilka effekter modellerna har på ett kommunikationssystems prestanda. Att störsignalerna är liknande innebär att de har samma amplitud och ungefär samma frekvens, men att fasen är individuell. Det är vanligt att approximera summan av störsignaler som additivt vitt Gaussiskt brus (AWGN) med en total effekt motsvarande summan av de enskilda störsignalernas effekt, men det är inte fastställt när denna effektsummering-approximation är giltig att använda med hänsyn till signalernas egenskaper. Rapportens resultat är framtaget via simuleringar av störsignaler och deras effekt på ett grundläggande kommunikationssystem. Systemets prestanda är främst utvärderad genom att beräkna bitfelssannolikheten, och det visar sig finnas tre fall då man inte bör använda effektsummerings-approximationen. Det första fallet är när nyttosignalen och störsignalerna har exakt samma frekvens, det andra fallet är när antalet störkällor är under tre, och det tredje fallet är när störsignalernas sammanlagda maximala amplitud (störsignalernas enskilda amplitud gånger antal störkällor) är mindre än, eller nära, nyttosignalens amplitud.

Convolutional Neural Networks (CNNs) have traditionally been used for image analysis and computer vision and are known for their ability to detect complex patterns in data. This report studies an application of CNNs within bioelectronic medicine, namely predicting blood glucose levels using nerve signals. Nerve signals and blood glucose levels were measured on a mouse before and after administration of glucose injections. The nerve signals were measured by placing 16 voltage-measuring electrodes on the vagus nerve of the mouse. The obtained nerve signal data was segmented into time intervals of 5 ms and aligned with the corresponding glucose measurements. Two LeNet-5 based CNN architectures, one 1-dimensional and one 2-dimensional, were implemented and trained on the data. Evaluation of the models’ performance was based on the mean squared error, the mean absolute error, and the R2-score of a simple moving average over the dataset. Both models had promising performance with an R2-score of above 0.92, suggesting a strong correlation between nerve signals and blood glucose levels. The difference in performance between the 1-dimensional and 2-dimensional model was insignificant. These results highlight the potential of using CNNs in bioelectronic medicine for prediction of physiological parameters from nerve signal data.

The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.