Wavelet Multiresolution Analysis of High-Frequency FX Rates, Summer 1997

FX pricing processes are nonstationary and their frequency characteristics are time-dependent. Most do not conform to geometric Brownian motion, since they exhibit a scaling law with a Hurst exponent between zero and 0.5 and fractal dimensions between 1.5 and 2. This paper uses wavelet multiresolution analysis, with Haar wavelets, to analyze the nonstationarity (time-dependence) and self-similarity (scale-dependence) of intra-day Asian currency spot exchange rates.foreign exchange, anti-persistence, multi-resolution analysis, wavelets, Asia

Wavelet Multiresolution Analysis of High-Frequency Asian FX Rates, Summer 1997

FX pricing processes are nonstationary and their frequency characteristics are time-dependent. Most do not conform to geometric Brownian motion, since they exhibit a scaling law with a Hurst exponent between zero and 0.5 and fractal dimensions between 1.5 and 2. This paper uses wavelet multiresolution analysis, with Haar wavelets, to analyze the nonstationarity (time-dependence) and self-similarity (scale-dependence) of intra-day Asian currency spot exchange rates. These are the ask and bid quotes of the currencies of eight Asian countries (Japan, Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, Thailand), and of Germany for comparison, for the crisis period May 1, 1998 - August 31, 1997, provided by Telerate (U.S. dollar is the numeraire). Their time-scale dependent spectra, which are localized in time, are observed in wavelet based scalograms. The FX increments can be characterized by the irregularity of their singularities. This degrees of irregularity are measured by homogeneous Hurst exponents. These critical exponents are used to identify the fractal dimension, relative stability and long term dependence of each Asian FX series. The invariance of each identified Hurst exponent is tested by comparing it at varying time and scale (frequency) resolutions. It appears that almost all FX markets show anti-persistent pricing behavior. The anchor currencies of the D-mark and Japanese Yen are ultra-efficient in the sense of being most anti-persistent. The Taiwanese dollar is the most persistent, and thus unpredictable, most likely due to administrative control. FX markets exhibit these non- linear, non-Gaussian dynamic structures, long term dependence, high kurtosis, and high degrees of non-informational (noise) trading, possibly because of frequent capital flows induced by non-synchronized regional business cycles, rapidly changing political risks, unexpected informational shocks to investment opportunities, and, in particular, investment strategies synthesizing interregional claims using cash swaps with different duration horizons.foreign exchange markets, anti-persistence, long-term dependence, multi-resolution analysis, wavelets, time-scale analysis, scaling laws, irregularity analysis, randomness, Asia

Development of FPGA based Standalone Tunable Fuzzy Logic Controllers

Soft computing techniques differ from conventional (hard) computing, in that unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind and its ability to address day-to-day problems. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Artificial Neural Networks (ANNs). This thesis presents a generic hardware architecture for type-I and type-II standalone tunable Fuzzy Logic Controllers (FLCs) in Field Programmable Gate Array (FPGA). The designed FLC system can be remotely configured or tuned according to expert operated knowledge and deployed in different applications to replace traditional Proportional Integral Derivative (PID) controllers. This re-configurability is added as a feature to existing FLCs in literature. The FLC parameters which are needed for tuning purpose are mainly input range, output range, number of inputs, number of outputs, the parameters of the membership functions like slope and center points, and an If-Else rule base for the fuzzy inference process. Online tuning enables users to change these FLC parameters in real-time and eliminate repeated hardware programming whenever there is a need to change. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence, the challenge lies in reducing the rule base significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, Modified Rule Active 2 Overlap Membership Function (MRA2-OMF), Modified Rule Active 3 Overlap Membership Function (MRA3-OMF), Modified Rule Active 4 Overlap Membership Function (MRA4-OMF), and Genetic Algorithm (GA) base rule optimization methods are proposed and implemented. These methods reduce the effective rules without compromising system accuracy and improve the cycle time in terms of Fuzzy Logic Inferences Per Second (FLIPS). In the proposed system architecture, the FLC is segmented into three independent modules, fuzzifier, inference engine with rule base, and defuzzifier. Fuzzy systems employ fuzzifier to convert the real world crisp input into the fuzzy output. In type 2 fuzzy systems there are two fuzzifications happen simultaneously from upper and lower membership functions (UMF and LMF) with subtractions and divisions. Non-restoring, very high radix, and newton raphson approximation are most widely used division algorithms in hardware implementations. However, these prevalent methods have a cost of more latency. In order to overcome this problem, a successive approximation division algorithm based type 2 fuzzifier is introduced. It has been observed that successive approximation based fuzzifier computation is faster than the other type 2 fuzzifier. A hardware-software co-design is established on Virtex 5 LX110T FPGA board. The MATLAB Graphical User Interface (GUI) acquires the fuzzy (type 1 or type 2) parameters from users and a Universal Asynchronous Receiver/Transmitter (UART) is dedicated to data communication between the hardware and the fuzzy toolbox. This GUI is provided to initiate control, input, rule transfer, and then to observe the crisp output on the computer. A proposed method which can support canonical fuzzy IF-THEN rules, which includes special cases of the fuzzy rule base is included in Digital Fuzzy Logic Controller (DFLC) architecture. For this purpose, a mealy state machine is incorporated into the design. The proposed FLCs are implemented on Xilinx Virtex-5 LX110T. DFLC peripheral integration with Micro-Blaze (MB) processor through Processor Logic Bus (PLB) is established for Intellectual Property (IP) core validation. The performance of the proposed systems are compared to Fuzzy Toolbox of MATLAB. Analysis of these designs is carried out by using Hardware-In-Loop (HIL) test to control various plant models in MATLAB/Simulink environments

A flexible hardware architecture for 2-D discrete wavelet transform: design and FPGA implementation

The Discrete Wavelet Transform (DWT) is a powerful signal processing tool that has recently gained widespread acceptance in the field of digital image processing. The multiresolution analysis provided by the DWT addresses the shortcomings of the Fourier Transform and its derivatives. The DWT has proven useful in the area of image compression where it replaces the Discrete Cosine Transform (DCT) in new JPEG2000 and MPEG4 image and video compression standards. The Cohen-Daubechies-Feauveau (CDF) 5/3 and CDF 9/7 DWTs are used for reversible lossless and irreversible lossy compression encoders in the JPEG2000 standard respectively. The design and implementation of a flexible hardware architecture for the 2-D DWT is presented in this thesis. This architecture can be configured to perform both the forward and inverse DWT for any DWTfamily, using fixed-point arithmetic and no auxiliary memory. The Lifting Scheme method is used to perform the DWT instead of the less efficient convolution-based methods. The DWT core is modeled using MATLAB and highly parameterized VHDL. The VHDL model is synthesized to a Xilinx FPGA to prove hardware functionality. The CDF 5/3 and CDF 9/7 versions of the DWT are both modeled and used as comparisons throughout this thesis. The DWT core is used in conjunction with a very simple image denoising module to demonstrate the potential of the DWT core to perform image processing techniques. The CDF 5/3 hardware produces identical results to its theoretical MATLAB model. The fixed point CDF 9/7 deviates very slightly from its floating-point MATLAB model with a ~59dB PSNR deviation for nine levels of DWT decomposition. The execution time for performing both DWTs is nearly identical at -14 clock cycles per image pixel for one level of DWT decomposition. The hardware area generated for the CDF 5/3 is -16,000 gates using only 5% of the Xilinx FPGA hardware area, 2.185 MHz maximum clock speed and 24 mW power consumption. The simple wavelet image denoising techniques resulted in cleaned images up to -27 PSNR

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Tuning Water Adhesion on Biomimicking Superhydrophobic MnO2 Films

Ph.DDOCTOR OF PHILOSOPH

Multifunctional Reconfigurable Antennas and Arrays Operating at 60 GHz band

To meet the ever increasing demand of high data rate, millimeter-wave (mm-wave) wireless communication has become an area of intense research due to the capability of offering very broad bandwidth. However, the propagation losses increase as a function of operation frequency. Therefore, there is need for antenna systems with high gain and beam-steering capability at elevated frequencies, which comes at the expense of high cost and increased complexity. This dissertation demonstrates the design, micro-fabrication, and characterization of two different antennas and two different antenna arrays. A broadband patch antenna operating within (57-66) GHz band, which works as a building block to create a multifunctional reconfigurable antenna (MRA) that is capable of beam steering in three directions pertaining to θ ∈{-30°, 0°, 30°}; Φ=90°. These standalone antennas were then put in a linear formation to create a 2x8 planar array and a 4x1 multifunctional reconfigurable antenna array (MRAA) to increase the gain further and to offer wider bandwidth. The proposed novel MRA and MRAA possess variable element factors, which potentially can feature as the main building blocks of mm-wave reconfigurable wireless communication systems with reduced cost and complexity

On Characterization and Optimization of Engineering Surfaces

Swedish manufacturing industry in collaboration with academia is exploring innovative ways to manufacture eco-efficient and resource efficient products. Consequently, improving manufacturing efficiency and quality has become the priority for the manufacturing sector to remain competitive in a sustainable way. To achieve this, control and optimization of manufacturing process and product’s performance are necessary. This has led to increase in demand for functional surfaces, which are engineering surfaces tailored to different applications. With new advancements in manufacturing and surface metrology, investigations are steadily progressing towards re-defining quality and meeting dynamic customer demands. In this thesis, surfaces produced by different manufacturing systems are investigated, and methods are proposed to improve specification and optimization.The definition and interpretation of surface roughness vary across the manufacturing industry and academia. It is well known that surface characterization helps to understand the manufacturing process and its influence on surface functional properties such as wear, friction, adhesivity, wettability, fluid retention and aesthetic properties such as gloss. Manufactured surfaces consist of features that are relevant and features that are not of interest. To be able to produce the intended function, it is important to identify and quantify the features of relevance. Use of surface texture parameters helps in quantifying these surface features with respect to type, region, spacing and distribution. Currently, surface parameters Ra or Sa that represent average roughness are widely used in the industry, but they may not provide adequate information on the surface. In this thesis, a general methodology, based on the standard surface parameters and statistical approach, is proposed to improve the specification for surface roughness and identify the combination of significant surface texture parameters that best describe the surface and extract valuable surface information.Surface topography generated by additive, subtractive and formative processes is investigated with the developed research approach. The roughness profile parameters and areal surface parameters defined in ISO, along with power spectral density and scale sensitive fractal analysis, are used for surface characterization and analysis. In this thesis, the application of regression statistics to identify the set of significant surface parameters that improve the specification for surface roughness is shown. These surface parameters are used to discriminate between the surfaces produced by multiple process variables at multiple levels. By analyzing the influence of process variables on the surface topography, the research methodology helps to understand the underlying physical phenomenon and enhance the domain-specific knowledge with respect to surface topography. Subsequently, it helps to interpret processing conditions for process and surface function optimization.The research methods employed in this study are valid and applicable for different manufacturing processes. This thesis can support the guidelines for manufacturing industry focusing on process and functional optimization through surface analysis. With increase in use of machine learning and artificial intelligence in automation, methodologies such as the one proposed in this thesis are vital in exploring and extracting new possibilities in functional surfaces