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The decline in fishermen catches of Lake kainji, which went down from 28,639.6t in 1970 to 6.048t in 1974 (Ita 1982) prompted the promulgation of fisheries edict banning beach seines and other obnoxious fishing gear by Kebbi and Niger State governments in 1997 as a measure to prevent depletion of fisheries resources in the lake. Radio as a medium of communication was adopted as the method to disseminate information in the fisheries edict to the fisherfolks in order to bring about positive changes. Radio Niger, Koro station, was involved in the broadcast. Fisherfolk listened to the broadcast 32 times a week. Frame survey data and radio impact assessment studies were used to assess the effect of the broadcast on the number of beach seines and other obnoxious fishing gears on the lake which were banned in the fisheries edicts. It was revealed that the number of beach decreased drastically from 582 in 1997 to 122 in 1999 and to only 17 in 2001

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Signal processing techniques play important roles in the design of digital communication systems. These include information manipulation, transmitter signal processing, channel estimation, channel equalization and receiver signal processing. By interacting with communication theory and system implementing technologies, signal processing specialists develop efficient schemes for various communication problems by wisely exploiting various mathematical tools such as analysis, probability theory, matrix theory, optimization theory, and many others. In recent years, researchers realized that multiple-input multiple-output (MIMO) channel models are applicable to a wide range of different physical communications channels. Using the elegant matrix-vector notations, many MIMO transceiver (including the precoder and equalizer) design problems can be solved by matrix and optimization theory. Furthermore, the researchers showed that the majorization theory and matrix decompositions, such as singular value decomposition (SVD), geometric mean decomposition (GMD) and generalized triangular decomposition (GTD), provide unified frameworks for solving many of the point-to-point MIMO transceiver design problems.

In this thesis, we consider the transceiver design problems for linear time invariant (LTI) flat MIMO channels, linear time-varying narrowband MIMO channels, flat MIMO broadcast channels, and doubly selective scalar channels. Additionally, the channel estimation problem is also considered. The main contributions of this dissertation are the development of new matrix decompositions, and the uses of the matrix decompositions and majorization theory toward the practical transmit-receive scheme designs for transceiver optimization problems. Elegant solutions are obtained, novel transceiver structures are developed, ingenious algorithms are proposed, and performance analyses are derived.

The first part of the thesis focuses on transceiver design with LTI flat MIMO channels. We propose a novel matrix decomposition which decomposes a complex matrix as a product of several sets of semi-unitary matrices and upper triangular matrices in an iterative manner. The complexity of the new decomposition, generalized geometric mean decomposition (GGMD), is always less than or equal to that of geometric mean decomposition (GMD). The optimal GGMD parameters which yield the minimal complexity are derived. Based on the channel state information (CSI) at both the transmitter (CSIT) and receiver (CSIR), GGMD is used to design a butterfly structured decision feedback equalizer (DFE) MIMO transceiver which achieves the minimum average mean square error (MSE) under the total transmit power constraint. A novel iterative receiving detection algorithm for the specific receiver is also proposed. For the application to cyclic prefix (CP) systems in which the SVD of the equivalent channel matrix can be easily computed, the proposed GGMD transceiver has K/log_2(K) times complexity advantage over the GMD transceiver, where K is the number of data symbols per data block and is a power of 2. The performance analysis shows that the GGMD DFE transceiver can convert a MIMO channel into a set of parallel subchannels with the same bias and signal to interference plus noise ratios (SINRs). Hence, the average bit rate error (BER) is automatically minimized without the need for bit allocation. Moreover, the proposed transceiver can achieve the channel capacity simply by applying independent scalar Gaussian codes of the same rate at subchannels.

In the second part of the thesis, we focus on MIMO transceiver design for slowly time-varying MIMO channels with zero-forcing or MMSE criterion. Even though the GGMD/GMD DFE transceivers work for slowly time-varying MIMO channels by exploiting the instantaneous CSI at both ends, their performance is by no means optimal since the temporal diversity of the time-varying channels is not exploited. Based on the GTD, we develop space-time GTD (ST-GTD) for the decomposition of linear time-varying flat MIMO channels. Under the assumption that CSIT, CSIR and channel prediction are available, by using the proposed ST-GTD, we develop space-time geometric mean decomposition (ST-GMD) DFE transceivers under the zero-forcing or MMSE criterion. Under perfect channel prediction, the new system minimizes both the average MSE at the detector in each space-time (ST) block (which consists of several coherence blocks), and the average per ST-block BER in the moderate high SNR region. Moreover, the ST-GMD DFE transceiver designed under an MMSE criterion maximizes Gaussian mutual information over the equivalent channel seen by each ST-block. In general, the newly proposed transceivers perform better than the GGMD-based systems since the super-imposed temporal precoder is able to exploit the temporal diversity of time-varying channels. For practical applications, a novel ST-GTD based system which does not require channel prediction but shares the same asymptotic BER performance with the ST-GMD DFE transceiver is also proposed.

The third part of the thesis considers two quality of service (QoS) transceiver design problems for flat MIMO broadcast channels. The first one is the power minimization problem (min-power) with a total bitrate constraint and per-stream BER constraints. The second problem is the rate maximization problem (max-rate) with a total transmit power constraint and per-stream BER constraints. Exploiting a particular class of joint triangularization (JT), we are able to jointly optimize the bit allocation and the broadcast DFE transceiver for the min-power and max-rate problems. The resulting optimal designs are called the minimum power JT broadcast DFE transceiver (MPJT) and maximum rate JT broadcast DFE transceiver (MRJT), respectively. In addition to the optimal designs, two suboptimal designs based on QR decomposition are proposed. They are realizable for arbitrary number of users.

Finally, we investigate the design of a discrete Fourier transform (DFT) modulated filterbank transceiver (DFT-FBT) with LTV scalar channels. For both cases with known LTV channels and unknown wide sense stationary uncorrelated scattering (WSSUS) statistical channels, we show how to optimize the transmitting and receiving prototypes of a DFT-FBT such that the SINR at the receiver is maximized. Also, a novel pilot-aided subspace channel estimation algorithm is proposed for the orthogonal frequency division multiplexing (OFDM) systems with quasi-stationary multi-path Rayleigh fading channels. Using the concept of a difference co-array, the new technique can construct M^2 co-pilots from M physical pilot tones with alternating pilot placement. Subspace methods, such as MUSIC and ESPRIT, can be used to estimate the multipath delays and the number of identifiable paths is up to O(M^2), theoretically. With the delay information, a MMSE estimator for frequency response is derived. It is shown through simulations that the proposed method outperforms the conventional subspace channel estimator when the number of multipaths is greater than or equal to the number of physical pilots minus one.

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A search for dielectron decays of heavy neutral resonances has been performed using proton-proton collision data collected at √s = 7 TeV by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) in 2011. The data sample corresponds to an integrated luminosity of 5 fb−1. The dielectron mass distribution is consistent with Standard Model (SM) predictions. An upper limit on the ratio of the cross section times branching fraction of new bosons, normalized to the cross section times branching fraction of the Z boson, is set at the 95 % confidence level. This result is translated into limits on the mass of new neutral particles at the level of 2120 GeV for the Z′ in the Sequential Standard Model, 1810 GeV for the superstring-inspired Z′ψ resonance, and 1940 (1640) GeV for Kaluza-Klein gravitons with the coupling parameter k/MPl of 0.10 (0.05).

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The AM CVn systems are a rare class of ultra-compact astrophysical binaries. With orbital periods of under an hour and as short as five minutes, they are among the closest known binary star systems and their evolution has direct relevance to the type Ia supernova rate and the white dwarf binary population. However, their faint and rare nature has made population studies of these systems difficult and several studies have found conflicting results.

I undertook a survey for AM CVn systems using the Palomar Transient Factory (PTF) astrophysical synoptic survey by exploiting the "outbursts" these systems undergo. Such events result in an increase in luminosity by a factor of up to two-hundred and are detectable in time-domain photometric data of AM CVn systems. My search resulted in the discovery of eight new systems, over 20% of the current known population. More importantly, this search was done in a systematic fashion, which allows for a population study properly accounting for biases.

Apart from the discovery of new systems, I used the time-domain data from the PTF and other synoptic surveys to better understand the long-term behavior of these systems. This analysis of the photometric behavior of the majority of known AM CVn systems has shown changes in their behavior at longer time scales than have previously been observed. This has allowed me to find relationships between the outburst properties of an individual system and its orbital period.

Even more importantly, the systematically selected sample together with these properties have allowed me to conduct a population study of the AM CVn systems. I have shown that the latest published estimates of the AM CVn system population, a factor of fifty below theoretical estimates, are consistent with the sample of systems presented here. This is particularly noteworthy since my population study is most sensitive to a different orbital period regime than earlier surveys. This confirmation of the population density will allow the AM CVn systems population to be used in the study of other areas of astrophysics.

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The LIGO and Virgo gravitational-wave observatories are complex and extremely sensitive strain detectors that can be used to search for a wide variety of gravitational waves from astrophysical and cosmological sources. In this thesis, I motivate the search for the gravitational wave signals from coalescing black hole binary systems with total mass between 25 and 100 solar masses. The mechanisms for formation of such systems are not well-understood, and we do not have many observational constraints on the parameters that guide the formation scenarios. Detection of gravitational waves from such systems — or, in the absence of detection, the tightening of upper limits on the rate of such coalescences — will provide valuable information that can inform the astrophysics of the formation of these systems. I review the search for these systems and place upper limits on the rate of black hole binary coalescences with total mass between 25 and 100 solar masses. I then show how the sensitivity of this search can be improved by up to 40% by the the application of the multivariate statistical classifier known as a random forest of bagged decision trees to more effectively discriminate between signal and non-Gaussian instrumental noise. I also discuss the use of this classifier in the search for the ringdown signal from the merger of two black holes with total mass between 50 and 450 solar masses and present upper limits. I also apply multivariate statistical classifiers to the problem of quantifying the non-Gaussianity of LIGO data. Despite these improvements, no gravitational-wave signals have been detected in LIGO data so far. However, the use of multivariate statistical classification can significantly improve the sensitivity of the Advanced LIGO detectors to such signals.