Efficient (Piecewise) Linear Minmax Approximation of Digital Signals

In this work efficient geometric algorithms are provided for the linear approximation of digital signals under the uniform norm. Given a set of n points (xi, yi)i=1..n, with xi < xj if i < j, we give a new method to find the optimum linear approximation in O(n). Given also an error bound, we demonstrate how to construct in O(n) a non continuous piecewise solution such that the number k of segments is optimal. Furthermore we show that for such number of segments, the solution that is l∞ optimal can also be found in O(n) provided that n/k = O(1)

Optimal PWM control of switched-capacitor DC/DC power converters via model transformation and enhancing control techniques

Abstract—This paper presents an efficient and effective method for an optimal pulse width modulated (PWM) control of switched-capacitor DC/DC power converters. Optimal switching instants are determined based on minimizing the output ripple magnitude, the output leakage voltage and the sensitivity of the output load voltage with respect to both the input voltage and the load resistance. This optimal PWM control strategy has several advantages over conventional PWM control strategies: 1) It does not involve a linearization, so a large signal analysis is performed. 2) It guarantees the optimality. The problem is solved via both the model transformation and the optimal enhancing control techniques. A practical example of the PWM control of a switched-capacitor DC/DC power converter is presented

Efficient automatic correction and segmentation based 3D visualization of magnetic resonance images

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs). These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques. Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates. These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes

H^∞-Optimal Fractional Delay Filters

Fractional delay filters are digital filters to delay discrete-time signals by a fraction of the sampling period. Since the delay is fractional, the intersample behavior of the original analog signal becomes crucial. In contrast to the conventional designs based on the Shannon sampling theorem with the band-limiting hypothesis, the present paper proposes a new approach based on the modern sampled-data $H^{infty}$ optimization that aims at restoring the intersample behavior beyond the Nyquist frequency. By using the lifting transform or continuous-time blocking the design problem is equivalently reduced to a discrete-time $H^{infty}$ optimization, which can be effectively solved by numerical computation softwares. Moreover, a closed-form solution is obtained under an assumption on the original analog signals. Design examples are given to illustrate the advantage of the proposed method

Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations

Securing Multi-Layer Communications: A Signal Processing Approach

Security is becoming a major concern in this information era. The development in wireless communications, networking technology, personal computing devices, and software engineering has led to numerous emerging applications whose security requirements are beyond the framework of conventional cryptography. The primary motivation of this dissertation research is to develop new approaches to the security problems in secure communication systems, without unduly increasing the complexity and cost of the entire system. Signal processing techniques have been widely applied in communication systems. In this dissertation, we investigate the potential, the mechanism, and the performance of incorporating signal processing techniques into various layers along the chain of secure information processing. For example, for application-layer data confidentiality, we have proposed atomic encryption operations for multimedia data that can preserve standard compliance and are friendly to communications and delegate processing. For multimedia authentication, we have discovered the potential key disclosure problem for popular image hashing schemes, and proposed mitigation solutions. In physical-layer wireless communications, we have discovered the threat of signal garbling attack from compromised relay nodes in the emerging cooperative communication paradigm, and proposed a countermeasure to trace and pinpoint the adversarial relay. For the design and deployment of secure sensor communications, we have proposed two sensor location adjustment algorithms for mobility-assisted sensor deployment that can jointly optimize sensing coverage and secure communication connectivity. Furthermore, for general scenarios of group key management, we have proposed a time-efficient key management scheme that can improve the scalability of contributory key management from O(log n) to O(log(log n)) using scheduling and optimization techniques. This dissertation demonstrates that signal processing techniques, along with optimization, scheduling, and beneficial techniques from other related fields of study, can be successfully integrated into security solutions in practical communication systems. The fusion of different technical disciplines can take place at every layer of a secure communication system to strengthen communication security and improve performance-security tradeoff

Sampling from a system-theoretic viewpoint

This paper studies a system-theoretic approach to the problem of reconstructing an analog signal from its samples. The idea, borrowed from earlier treatments in the control literature, is to address the problem as a hybrid model-matching problem in which performance is measured by system norms. \ud \ud The paper is split into three parts. In Part I we present the paradigm and revise the lifting technique, which is our main technical tool. In Part II optimal samplers and holds are designed for various analog signal reconstruction problems. In some cases one component is fixed while the remaining are designed, in other cases all three components are designed simultaneously. No causality requirements are imposed in Part II, which allows to use frequency domain arguments, in particular the lifted frequency response as introduced in Part I. In Part III the main emphasis is placed on a systematic incorporation of causality constraints into the optimal design of reconstructors. We consider reconstruction problems, in which the sampling (acquisition) device is given and the performance is measured by the $L^2$-norm of the reconstruction error. The problem is solved under the constraint that the optimal reconstructor is $l$-causal for a given $l\geq 0,$ i.e., that its impulse response is zero in the time interval $(-\infty,-l h),$ where $h$ is the sampling period. We derive a closed-form state-space solution of the problem, which is based on the spectral factorization of a rational transfer function

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs