Dithered GMD Transform Coding

The geometric mean decomposition (GMD) transform coder (TC) was recently introduced and was shown to achieve the optimal coding gain without bit loading under the high bit rate assumption. However, the performance of the GMD transform coder is degraded in the low rate case. There are mainly two reasons for this degradation. First, the high bit rate quantizer model becomes invalid. Second, the quantization error is no longer negligible in the prediction process when the bit rate is low. In this letter, we introduce dithered quantization to tackle the first difficulty, and then redesign the precoders and predictors in the GMD transform coders to tackle the second. We propose two dithered GMD transform coders: the GMD subtractive dithered transform coder (GMD-SD) where the decoder has access to the dither information and the GMD nonsubtractive dithered transform coder (GMD-NSD) where the decoder has no knowledge about the dither. Under the uniform bit loading scheme in scalar quantizers, it is shown that the proposed dithered GMD transform coders perform significantly better than the original GMD coder in the low rate case

Generalized Nearest Neighbor Decoding

It is well known that for Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between a codeword and the received channel output vector, is the maximum likelihood solution and hence capacity-achieving. Nearest neighbor decoding remains a convenient and yet mismatched solution for general channels, and the key message of this paper is that the performance of the nearest neighbor decoding can be improved by generalizing its decoding metric to incorporate channel state dependent output processing and codeword scaling. Using generalized mutual information, which is a lower bound to the mismatched capacity under independent and identically distributed codebook ensemble, as the performance measure, this paper establishes the optimal generalized nearest neighbor decoding rule, under Gaussian channel input. Several {restricted forms of the} generalized nearest neighbor decoding rule are also derived and compared with existing solutions. The results are illustrated through several case studies for fading channels with imperfect receiver channel state information and for channels with quantization effects.Comment: 30 pages, 8 figure

The role of identity and interest in the evolution and sustenance of Sino-Zambian relations: a constructivist perspective.

Doctor of Philosophy in Political Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Relying on China-Zambia relations as a case study, this dissertation uses the constructivist approach to international relations to look at the role that identity and interests play in shaping relations between nations. The study has established that the Chinese and Zambian governments have assumed and constructed relations that have kept the intimacy between the two countries largely undisturbed for fifty-three years. Factors such as third world solidarity and a concerted struggle against colonial and foreign domination were major points of unity between the two countries. After the Cold War, these ideological leanings have been largely replaced by a more economically inclined basis for relations but the two countries remain cordial and intimate allies. The constructivist/interpretivist paradigm of research was used to arrive at the findings made. The research approach used was qualitative, the research style used was case study and the research methods included interviews, focus group discussions and document analysis. In order to answer the research questions satisfactorily and to draw credible conclusions, purposive sampling was used to draw participants from various fractions of the Zambian society – ranging from the civil society, a congress of trade unions, political commentary, Zambian workers in a Chinese firm to ordinary Zambians. The research findings suggest that at state level, Zambia and China have constructed very influential intersecting identities and common interests that have fortified relations between the two countries. However, the findings also suggested that these identities and interests should not be conflated with identities and interests of non-state actors which are – at some points – at variance with national identities and interests. Primary data exudes an amount of apprehension among ordinary Zambians about the growing presence of Chinese nationals who are not coming at the behest of their government and hence whose interests differ from those stated by the Chinese government. The study recommends that more attention be paid to how dynamics between ordinary Chinese and Zambians are likely to influence relations at a state level because of Zambians’ concerns about “the Chinese.” This notwithstanding, the study argues that as things stand, national identities and interests, crafted at the highest echelons of both countries and the practical and developmental importance the two countries have for each other are the most overriding factor in insulating China-Zambia relations from renunciation or any substantial decline

Multipoint gas detection using range resolved interferometry

The ability to detect and quantify gas in multiple locations is important in environmental and safety monitoring situations. This thesis describes the first application of Range Resolved Interferometry to the problem of gas sensing at multiple locations. Range resolved interferometry (RRI) is an interferometric signal processing technique that allows the separation of individual interferometric signals from superpositions of multiple interferometers and the rejection of interferometers other than those of interest. This allows the interrogation of the light intensity passing through each interferometer of interest which in turn allows a measure of the absorption of light by gas present within the interferometer arms. The application of the Beer-Lambert Law allows the measurement of a gas concentration from this information. Unlike previous interferometric techniques for multipoint gas measurement, RRI uses injection current modulation of a DFB laser and is therefore, cost effective. The process of applying a ramp modulation to RRI in order to extract spectroscopic information is described along with the post-processing needed to extract gas concentrations from multiple locations simultaneously. Three sensing regions ² < 0.95) and with the ability to measure methane at a concentration of 200ppm with no averaging time. Allen-Werle analysis showed that with sufficient averaging time, a limit of detection as low as 4ppm could be achieved. Cross talk experiments showed that the presence of gas in other sensing regions had no effect on gas concentration measurements. The first use of RRI for spectroscopic measurements required extensive postprocessing to account for the DFB laser’s non-uniform response to sinusoidal modulation as the driving injection current was varied to sweep the laser output wavelength. Application of an envelope function to the sinusoidal modulation provided a stable wavelength response to the sinusoidal modulation and so allowed real-time gas detection with no post processing required. Experiments were performed to establish that the most suitable deployment topology for multipoint sensing is a serial-bus topology and that the amplitude of the sinusoidal modulation must be chosen to provide the chosen balance between the spatial resolution of the system and the signal strength provided by the measurement of light absorption by the gas under test. The ability of RRI to distinguish between interferometers of interest and parasitic interferometers was used to extract the absorption measurements from a gas detection system with optical fringing and was shown to reduce the unwanted signal by a factor of 18

Earth Observatory Satellite system definition study. Report no. 3: Design/cost tradeoff studies

The key issues in the Earth Observatory Satellite (EOS) program which are subject to configuration study and tradeoff are identified. The issue of a combined operational and research and development program is considered. It is stated that cost and spacecraft weight are the key design variables and design options are proposed in terms of these parameters. A cost analysis of the EOS program is provided. Diagrams of the satellite configuration and subsystem components are included

The Roles of Majorization and Generalized Triangular Decomposition in Communication and Signal Processing

Signal processing is an art that deals with the representation, transformation, and manipulation of the signals and the information they contain based on their specific features. The field of signal processing has always benefited from the interaction between theory, applications, and technologies for implementing the systems. The development of signal processing theory, in particular, relies heavily on mathematical tools including analysis, probability theory, matrix theory, and many others. Recently, the theory of majorization, which is an extremely useful tool for deriving inequalities, was introduced to the signal processing society in the context of MIMO communication system design. This also led many researchers to develop a fundamental matrix decomposition called generalized triangular decomposition (GTD), which was general enough to include many existing matrix orthogonal decompositions as special cases. The main contribution of this thesis is toward the use of majorization and GTD to the theory and many applications of signal processing. In particular, the focus is on developing new signal processing methods based on these mathematical tools for digital communication, data compression, and filter bank design. We revisit some classical problems and show that the theories of majorization and GTD provide a general framework for solving these problems. For many important new problems not solved earlier, they also provide elegant solutions. The first part of the thesis focuses on transceiver design for multiple-input multiple-output (MIMO) communications. The first problem considered is the joint optimization of transceivers with linear precoders, decision feedback equalizers (DFEs), and bit allocation schemes for frequency flat MIMO channels. We show that the generalized triangular decomposition offers an optimal family of solutions to this problem. This general framework incorporates many existing designs, such as the optimal linear transceiver, the ZF-VBLAST system, and the geometric mean decomposition (GMD) transceiver, as special cases. It also predicts many novel optimal solutions that have not been observed before. We also discuss the use of each of these theoretical solutions under practical considerations. In addition to total power constraints, we also consider the transceiver optimization under individual power constraints and other linear constraints on the transmitting covariance matrix, which includes a more realistic individual power constraint on each antenna. We show the use of semi-definite programming (SDP), and the theory of majorization again provides a general framework for optimizing the linear transceivers as well as the DFE transceivers. The transceiver design for frequency selective MIMO channels is then considered. Block diagonal GMD (BD-GMD), which is a special instance of GTD with block diagonal structure in one of the semi-unitary matrices, is used to design transceivers that have many desirable properties in both performance and computation. The second part of the thesis focuses on signal processing algorithms for data compressions and filter bank designs. We revisit the classical transform coding problem (for optimizing the theoretical coding gain in the high bit rate regime) from the view point of GTD and majorization theory. A general family of optimal transform coders is introduced based on GTD. This family includes the Karhunen-Lo\'{e}ve transform (KLT), and the prediction-based lower triangular transform (PLT) as special cases. The coding gain of the entire family, with optimal bit allocation, is maximized and equal to those of the KLT and the PLT. Other special cases of the GTD-TC are the GMD (geometric mean decomposition) and the BID (bidiagonal transform). The GMD in particular has the property that the optimum bit allocation is a uniform allocation. We also propose using dither quantization in the GMD transform coder. Under the uniform bit loading scheme, it is shown that the proposed dithered GMD transform coders perform significantly better than the original GMD coder in the low rate regime. Another important signal processing problem, namely the filter bank optimization based on the knowledge of input signal statistics, is then considered. GTD and the theory of majorization are again used to give a new look to this classical problem. We propose GTD filter banks as subband coders for optimizing the theoretical coding gain. The orthonormal GTD filter bank and the biorthogonal GTD filter bank are discussed in detail. We show that in both cases there are two fundamental properties in the optimal solutions, namely, {\it total decorrelation} and {\it spectrum equalization}. The optimal solutions can be obtained by performing the frequency dependent GTD on the Cholesky factor of the input power spectrum density matrices. We also show that in both theory and numerical simulations, the optimal GTD subband coders have superior performance than optimal traditional subband coders. In addition, the uniform bit loading scheme can be used in the optimal biorthogonal GTD coders with no loss of optimality. This solves the granularity problem in the conventional optimum bit loading formula. The use of the GTD filter banks in frequency selective MIMO communication systems is also discussed. Finally, the connection between the GTD filter bank and the traditional filter bank is clearly indicated.</p