Training Data Generation Framework For Machine-Learning Based Classifiers

In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having $11\%$ lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations

Tunable Plasmonic Metamaterial

Plasmonic metamaterials are artificial materials typically composed of noble metals in which the features of photonics and electronics are linked by coupling photons to conduction electrons of metal (known as surface plasmon). These rationally designed structures have spurred interest noticeably since they demonstrate some fascinating properties which are unattainable with naturally occurring materials. Complete absorption of light is one of the recent exotic properties of plasmonic metamaterials which has broadened its application area considerably. However, up to date all of the applied methods (perforated metallic films, grating structured systems, and conventional metamaterials) are costly and suffer from a lack of flexibility. Furthermore, their absorbance is mainly limited to a narrow spectral range or their fabrication is costly. So, such drawbacks make their vast application almost impossible. Here, in this dissertation, we design, fabricate and characterize a novel perfect absorbers based on nanocomposites whose total thickness is only a few tens of nanometers and its absorption band is broad, tunable and insensitive to the angle of incidence. The nanocomposites consist of metal nanoparticles embedded in a dielectric matrix with a high filling factor close to the percolation threshold. The filling factor can be tailored by vapor phase co-deposition of the metallic and dielectric components. Accordingly, three types of metals (gold, silver and copper) as the inclusions of the nanocomposite and four different mirrors (gold, silver, copper and aluminum) are used as the base layer. The high absorption of these metamaterials are originated from the huge absorption capability of the metallic nanoparticles (smaller than 5 nanometer in diameter) via localized plasmon resonance, confinement of the light within the tiny gap between nanoparticles as well as interference of the light by reflection through the layers. To functionalize the system, polymer-photoswitchable molecules were added as the top or spacer layer which enable us to demonstrate a photodriven perfect absorber in which the absorption band can be broadened or narrowed by ultraviolet or visible light illumination, respectively. In this approach, the absorption tuning is originated from the bond-breakage of the molecules which can be activated by irradiation. Due to the strong interaction of the molecules and metal mirror, plasmon-exciton coupling happens which not only enhances the absorption but also shifts or splits the absorption band. Also as the specific highlight of the idea, we show that a thin plasmonic nanocomposite film on a silicon wafer covered by a silicon dioxide film would diminish the reflection in a broad range of frequency and make a new class of plasmonic anti-reflection coating. Our novel concept (called hybrid ARC) combines two possible arrangements for the layers in an anti-reflection coating into a single structure; albeit at two different wavelengths. Its performance originates from the strong dispersive nature of the nanocomposite. Furthermore, we show that the current metamaterial on a metal reflector can be used for visualization of different colorations as a plasmonic rainbow despite its sub-wavelength thickness

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Portuguese SKA white book

Sem resumo disponível.publishe

Time domain classification of transient RFI

Since the emergence of radio astronomy as a field, it has been afflicted by radio frequency interference (RFI). RFI continues to present a problem despite increasingly sophisticated countermeasures developed over the decades. Due to technological improvements, radio telescopes have become more sensitive (for example, MeerKAT’s L-band receiver). Existing RFI has become more prominent as a result. At the same time, the prevalence of RFI-generating devices has increased as new technologies have been adopted by society. Many approaches have been developed for mitigating RFI, which are typically used in concert. New telescope arrays are often built far from human habitation in radio-quiet reserves. In South Africa, a radio-quiet reserve has been established in which several world class instruments are under construction. Despite the remote location of the reserve, careful attention is paid to the possibility of RFI. For example, some instruments will begin observations while others are still under construction. The infrastructure and equipment related to the construction work may increase the risk of RFI, especially transient RFI. A number of mitigation strategies have been employed, including the use of fixed and mobile RFI monitoring stations. Such stations operate independently of the main telescope arrays and continuously monitor a wide bandwidth in all directions. They are capable of recording spectra and high resolution time domain captures of transient RFI. Once detected, and if identified, an RFI source can be found and dealt with. The ability to identify the sources of detected RFI would be highly beneficial. Continuous wave intentional transmissions (telecommunication signals for example) are easily identified as they are required to adhere to allocated frequency bands. Transient RFI signals, however, are significantly more challenging to identify since they are generally broadband and highly intermittent. Transient RFI can be generated as a by-product of the normal operation of devices such as relays, AC machines and fluorescent lights, for example. Such devices may be present near radio telescope arrays as part of the infrastructure or equipment involved in the construction of new instruments. Other than contaminating observation data, transient RFI can also appear to have genuine astronomical origins. In one case, transient signals received from a microwave oven exhibited dispersion, suggesting a distant source. Therefore, the ability to identify transient RFI by source would be enormously valuable. Once identified, such sources may be removed or replaced where possible. Despite this need, there is a paucity of work on classifying transient RFI in the literature. This thesis focusses on the problem of identifying transient RFI by source in time domain data of the type captured by remote monitoring stations. Several novel approaches are explored in this thesis. If used with independent RFI monitoring stations, these approaches may aid in tracking down nearby RFI sources at a radio telescope array. They may also be useful for improving RFI flagging in data from radio telescopes themselves. Distinguishing between transient RFI and natural astronomical signals is likely to be an easier prospect than classifying transient RFI by source. Furthermore, these approaches may be better able to avoid excising genuine astronomical transients that nevertheless share some characteristics with RFI signals. The radio telescopes themselves are significantly more sensitive than RFI monitoring stations, and would thus be able to detect RFI sources more easily. However, terrestrial RFI would likely enter via sidelobes, tempering this advantage somewhat. In this thesis, transient RFI is first characterised, prior to classification by source. Labelled time-domain recordings of a number of transient RFI sources are acquired and statistically examined. Second, components analysis techniques are considered for feature selection. Cluster separation is analysed for principal components analysis (PCA) and kernel PCA, the latter proving most suitable. The effect of the supply voltage of certain RFI sources on cluster separation in the principal components domain is also explored. Several na¨ıve classification algorithms are tested, using kernel PCA for feature selection A more sophisticated dictionary-based approach is developed next. While there are variations in repeated recordings of the same RFI source, the signals tend to adhere to a common overarching structure. Full RFI signals are observed to consist of sequences of individual transients. An algorithm is presented to extract individual transients from full recordings, after which they are labelled using unsupervised clustering methods. This procedure results in a dictionary of archetypal transients, from which any full RFI sequence may be represented. Some approaches in Automated Speech Recognition (ASR) are similar: spoken words are divided into individual labelled phonemes. Representing RFI signals as sequences enables the use of hidden Markov models (HMMs) for identification. HMMs are well suited to sequence identification problems, and are known for their robustness to variation. For example, in ASR, HMMs are able to handle the variations in repeated utterances of the same word. When classifying the recorded RFI signals, good accuracy is achieved, improving on the results obtained using the more na¨ıve methods. Finally, a strategy involving deep learning techniques is explored. Recurrent neural networks and convolutional neural networks (CNNs) have shown great promise in a wide variety of classification tasks. Here, a model is developed that includes a pre-trained CNN layer followed by a bidirectional long short-term memory (BLSTM) layer. Special attention is paid to mitigating class imbalance when the model is used with individual transients extracted from full recordings. High classification accuracy is achieved, improving on the dictionary-based approach and the other na¨ıve methods. Recommendations are made for future work on developing these approaches further for practical use with remote monitoring stations. Other possibilities for future research are also discussed, including testing the robustness of the proposed approaches. They may also prove useful for RFI excision in observation data from radio telescopes