Thesis (M. Eng.)--Carleton University, 1999. / Includes bibliographical references. Also available in electronic format on the Internet.

Thesis (PhD)-Open University. BLDSC no.DX190076.

M.Ing. / Intelligent object identification (the ability to find the identity, position and orientation of an remote object) in a Manufacturing environment plays an important role in the areas of Automation and Manufacturing. Radio frequency identification (RFID) systems have solve the problem of finding the identity of a remote object, but it fails to determine the position and orientation. The Global Positioning System (GPS) have a solution to find the position of a remote object in the global environment, but in a Manufacturing environment it fails. The main obstacle to overcome in finding a unique solution with radio frequency technology is reflections. This thesis investigates the idea of finding the identity, position (and orientation) of a (dormant) chip remotely. The chip transmits a binary signal at 244kHz. The string is Amplitude modulated. The receiver demodulates the signal to obtain the chip's identity. The receiver antenna is divided into four quadrants. First the quadrant in which the chip is located are determined. Three different voltages are then measured to obtain the position of the chip in the specific quadrant. Reflections can be ignored since the system works at a low frequency.

Photo-identification and telemetry studies have reported limited instances in which Florida manatees (Trichechus manatus latirostris) traversed the coastline of Florida. Manatee calls were recorded from four different locations in Florida. Using a single hydrophone, calls were recorded at two sites on the east coast and two sites on the west coast of Florida. These locations were representative of the four manatee management zones adopted by the Florida Fish and Wildlife Conservation Commission and the United States Geological Service. These include the Northwest, Southwest, Atlantic and Upper St. John’s River regions. Seven hundred calls from each site were selected for analysis to further quantify call parameters and evaluate differences within and across geographical locations. Fundamental and dominant frequencies, call duration, number of harmonics, and the percentage of frequency modulation, were measured, in addition to the first and third quartile frequency, energy, interquartile bandwidths. Spectrograms and statistical differences in frequency modulated vocalizations (one-way anova, p <0.0001) were used to classify calls into six categories; whistles, squeaks, high squeaks, squeak-squeals, chirps and squeals. (1) Whistles are narrow tonal calls with no frequency modulation. The frequency range for these calls is 1800 – 5000 Hz and a duration of 0.080 – 0.382 s. (2) Squeaks are more complex frequency modulated calls with 2 - 4 harmonics even at low intensities. They have a duration of 0.052 – 0.681 s and a frequency range of 562.1 – 10,312.5 Hz. (3) Squeals have no frequency modulation, are wideband signals, have a duration of 0.077 – 0.562 s and a frequency range of 468.4 – 6656.8 Hz. (4) Squeak-squeals have characteristics of squeaks and squeals. They may or may not have frequency modulation, have a duration of 0.090 – 0.612 s and a frequency range of 750.1 – 8625.5 Hz. High squeaks are strongly modulated, have a duration of 0.131 – 0.236 s, and a frequency range of 1,300.2 – 10,628.8 Hz. Chirps are characterized by having two or three dominant energy bands that are separated by short gaps, have frequency modulation, a duration of 0.031 – 0.283 s, and a frequency range of 1,265.3 – 6937.5 Hz. Since it was unknown which manatee was eliciting the call, group means of the variables center fundamental frequency and first and third quartile frequency where used to analyze differences or similarities within and between coastlines. A T test was used to compare means at a 95% confidence interval. Spectrograms of the categorized calls were analyzed within and between coastlines. Results from t-tests suggest there are no differences in calls for the variables studied within and between east and west coast populations of the Florida manatee (all p values > 0.05). Correlation of spectrographic images suggests there is a high degree of similarity among categorized calls and calls seem to differ mostly in call contour.

The objective of this thesis is to study the demodulation of narrowband radio frequency signals by aliasing sampling in order to reduce the sampling rate. The spectrum can be recreated at the lower frequency position by aliasing sampling. However, if the sampling rate is deviated from the desired one, error will occur. The sensitivity to the frequency error of aliasing sampling is studied. One main reason of the deviation of the sampling rate is the frequency drifting of the local oscillator. Being able to compensate the oscillator drifting errors inexpensively, automatic frequency control (AFC) loops are important at receivers. Two major digital AFC algorithms are studied. One is the Phase method AFC, and the other is the Magnitude method AFC. Study indicates that both methods perform almost equally well. One adaptive AFC algorithm is also proposed. The scheme of the adaptive AFC algorithm is to use Upper-bound and Lower-bound techniques to squeeze the frequency errors. It is shown that the adaptive AFC algorithm can achieve up to 20 dB average signal-to-noise power ratio over the Magnitude method AFC under a noisy environment.

Gesturing is a natural way of communication between people and is used in our everyday conversations. Hand gesture recognition systems are used in many applications in a wide variety of fields, such as mobile phone applications, smart TVs, video gaming, etc. With the advances in human-computer interaction technology, gesture recognition is becoming an active research area. There are two types of devices to detect gestures; contact based devices and contactless devices. Using ultrasonic waves for determining gestures is one of the ways that is employed in contactless devices. Hand gesture recognition utilizing ultrasonic waves will be the focus of this thesis work. This thesis presents a new method for detecting and classifying a predefined set of hand gestures using a single ultrasonic transmitter and a single ultrasonic receiver. This method uses a linear frequency modulated ultrasonic signal. The ultrasonic signal is designed to meet the project requirements such as the update rate, the range of detection, etc. Also, it needs to overcome hardware limitations such as the limited output power, transmitter, and receiver bandwidth, etc. The method can be adapted to other hardware setups. Gestures are identified based on two main features; range estimation of the moving hand and received signal strength (RSS). These two factors are estimated using two simple methods; channel impulse response (CIR) and cross correlation (CC) of the reflected ultrasonic signal from the gesturing hand. A customized simple hardware setup was used to classify a set of hand gestures with high accuracy. The detection and classification were done using methods of low computational cost. This makes the proposed method to have a great potential for the implementation in many devices including laptops and mobile phones. The predefined set of gestures can be used for many control applications.

With the advent of software-defined radio, many radio applications have and are currently being designed for FPGAs, due to their high performance and reconfigurability. Invariably, "legacy" waveforms, such as FM, will need to be supported in such systems. A challenge that comes with programming FPGAs is the increased design and implementation time over conventional software programming. In this thesis, three FM demodulator techniques are implemented and compared in an FPGA. Two techniques are found to have similar SNR performance while having very different FPGA implementation characteristics. Library based design is explored for demodulators to increase FPGA design productivity. A block library is created and verified by use in tested demodulator designs. Two design tools that aim to increase design productivity in FPGAs, Ogre and HMFlow, are also examined and used to implement FM demodulators in a PCM/FM receiver design. Ogre leverages the demodulator block library, along with accompanying metadata, to decrease design time significantly. Design performance is not sacrificed when using Ogre. HMFlow, which relies on finer-grained blocks, reuses block implementation data to speed up implementation of the full design. The implementation of the HMFlow demodulator design is sped up by 3x but, when compared with the standard flow, produces an implementation with a reduced maximum clock rate (about 1/2) and with slightly more resources (about 6%). When comparing Ogre with HMFlow, the coarser-grained blocks of Ogre provide a more efficient design experience than that of HMFlow.

Frequency Modulation (FM) based re-synthesis to find the parameter values which best make a FM-synthesizer produce an output sound as similar as possible to a given target sound is a challenging problem. The search space of a commercial synthesizer is often non-linear and high dimensional. Moreover, some crucial decisions need to be done such as choosing the number of modulating oscillators or the algorithm by which they modulate each other. In this work we propose to use Machine Learning (ML) to learn a mapping from target sound to the parameter space of an FM-synthesizer. In order to investigate the capabilities of ML to implicitly learn to make the mentioned key desicions in FM, we design and compare two approaches: first a concurrent approach where all parameter values are compared at once by one model, and second a sequential approach where the prediction is done by a mix of classifiers and regressors. We evaluate the performance of the approaches with respect to ability to reproduce instrumental sound samples from a dataset of 2255 samples from over 700 instrument in three different pitches with respect to four different distance metrics, . The results indicate that both approaches have similar performance at predicting parameters which reconstruct the frequency magnitude spectrum and envelope of a target sound. However the results also point at the sequential model being better at predicting the parameters which reconstruct the temporal evolution of the frequency magnitude spectrums. It is concluded that despite the sequential model outperforming the concurrent, it is likely possible for a model to make key decisions implicitly, without explicitly designed subproblems. / Denna masteruppsats undersöker återskapandet av instrumentala ljud genom användandet av maskininlärning och en synthesizer för frekvensmodulering (FM). Genom att använda maskininlärning kan rätt parametervärden för synthesizern förutspås, sådant att synthesizern skapar ett ljud som är så likt ett givet målljud som möjligt. Uppgiften görs svår då parametrarna för en FMsynthesizer är många och påverkar ljudet olinjärt, vilket skapar ett stort och komplext sökområde.I tidigare forskning har Genetiska Algorithmer använts frekvent för denna process. Det har förekommit olika meningar gällande huruvida det är nödvändigt att explicit dela upp prediktionsprocessen i subproblem, eller om det är bättre att låta förutspå alla parametrar samtidigt utan att explicit införa mänsklig expertis kring problemet. I denna uppsats jämförs därför två olika ansatser: en konkurrent där alla parametrar föruspås på samma gång, och en sekventiell där processen brytits ner till subproblem. De två ansatserna jämförs med avseende på deras förmåga att förutspå parametervärden som återskapar instrumentala ljud så väl som möjligt.Resultaten visar att den sekventiella ansatsen presterar bättre och skapar mer liknande ljud. Dock visas att de båda ansatserna har samma förmåga att återskapa frekvensspektrum. Alltså kan slutsatsen dras att det är möjligt att träna modeller som implicit tar beslut kring val av FM-parametrar lika bra som modeller som tar beslut baserat på explicit nedbrutna subproblem.

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The infrequent occurrence of a transient feature (deviance; e.g., frequency modulation, FM) in one of the regular occurring sinusoidal tones (standards) elicits the deviance related mismatch negativity (MMN) component of the event-related brain potential. Based on a memory-based comparison, MMN reflects the mismatch between the representations of incoming and standard sounds. The present study investigated to what extent the infrequent exclusion of an FM is detected by the MMN system. For that purpose we measured MMN to deviances that either consisted of the exclusion or inclusion of an FM at an early or late position within the sound that was present or absent, respectively, in the standard. According to the information-content hypothesis, deviance detection relies on the difference in informational content of the deviant relative to that of the standard. As this difference between deviants with FM and standards without FM is the same as in the reversed case, comparable MMNs should be elicited to FM inclusions and exclusions. According to the feature-detector hypothesis, however, the deviance detection depends on the increased activation of feature detectors to additional sound features. Thus, rare exclusions of the FM should elicit no or smaller MMN than FM inclusions. In passive listening condition, MMN was obtained only for the early inclusion, but not for the exclusions nor for the late inclusion of an FM. This asymmetry in automatic deviance detection seems to partly reflect the contribution of feature detectors even though it cannot fully account for the missing MMN to late FM inclusions. Importantly, the behavioral deviance detection performance in the active listening condition did not reveal such an asymmetry, suggesting that the intentional detection of the deviants is based on the difference in informational content. On a more general level, the results partly support the “fresh-afferent” account or an extended memory-comparison based account of MMN.