Richard H. Middleton

Bio: Richard H. Middleton is an academic researcher from University of Newcastle. The author has contributed to research in topics: Control theory & Linear system. The author has an hindex of 48, co-authored 393 publications receiving 12037 citations. Previous affiliations of Richard H. Middleton include Hamilton Institute & University of California.

A mathematical study on immune activation and related dynamics in HIV infection

Abstract: Over decades, mathematical models have been applied successfully to the investigation of HIV dynamics. However, few of these investigations are able to explain the observation that host (CD4+ T) cell counts reduce, while viral load increases as the infection progresses. Various clinical studies of HIV infection have suggested that high T-cell activation levels are positively correlated with rapid disease development in untreated patients. This activation might be a major reason for the depletion of CD4+ T cells observed in most cases of long term untreated HIV infection. In this paper, we use a simple mathematical model to investigate immune activation and its role in HIV infection. Under reasonable assumptions relating to various HIV infection constants, we show that enhanced activation and reduced reversion in the immune system do result in depleted CD4+ T cell count. We further show that this process is robust to parameter variations. An extended model including viral dynamics illustrates the effects of immune activation on viral persistence and immune response. Simulations are given to verify the theoretical analysis.

On the robust stability of 2D mixed continuous-discrete-time systems with uncertainty

Abstract: This paper addresses the problem of establishing robust exponential stability of 2D mixed continuous-discrete-time systems affected by uncertainty. Specifically, it is supposed that the matrices of the system are polynomial functions of an uncertain vector constrained over a semialgebraic set. First, it is shown that robust exponential stability is equivalent to the existence of a complex Lyapunov functions depending polynomially on the uncertain vector and an additional parameter of degree not greater than a known quantity. Second, a condition for establishing robust exponential stability is proposed via convex optimization by exploiting sums-of-squares (SOS) matrix polynomials. This condition is sufficient for any chosen degree of the complex Lyapunov function candidate, and is also necessary for degrees sufficiently large.

Globally Stable Delay Alignment for Feedback Control Over Wireless Multipoint Connections

Abstract: The 5-G ultra reliable low latency communication standard is intended, for example, to enable new use cases based on networked feedback control. This article argues why additional delay control and alignment are needed, to meet the well-established feedback control requirements on low end-to-end loop delay and jitter. The main contribution of this article is a novel re-engineered delay aligning data flow controller, where a new static decoupling matrix is introduced. The intention is to obtain a multiple single loop control architecture that allows global robust $\mathcal {L}_2$ -stability to be proven. The algorithm is suitable to regulate $n+1$ end-to-end roundtrip delays of a 5-G multiconnectivity connection toward zero pair-wise delay differences. The new stability result is decisive for massive deployment, since weaker results would increase the need for supervision and raise operating costs significantly. A robot arm control loop application operating over three data paths illustrates the performance advantages associated with delay alignment and the proposed algorithm.

Round trip time skew control methods and arrangements

Abstract: A method for assisting in multipoint data flow control in a wireless communication system having a number, n+ 1, where n≥1, of wireless- transmission points. The method comprises obtaining (S 1), for each of the wireless-transmission points, a round trip time of a present sampling period for data travelling to a user equipment via a respective wireless-transmission point and an acknowledge message travelling back. A round trip time skew is computed (S2) for the present sampling period for individual wireless- transmission points. The round trip time skew is a difference between the obtained round trip time of a respective wireless-transmission point and a reference value. A reference round trip time value is provided (S4) for each wireless-transmission point in dependence of the round trip time skews. A rate control signal is generated (S5), for each of the wireless-transmission points, in dependence of a respective reference round trip time value. Corresponding arrangements are also disclosed.

Interval time control for 5g multi-connectivity

Abstract: The solution presented herein controls a transmission timing of data from multiple transmission points to synchronize the data reception at a receiver to within pre-specified limits. To that end, a skew timing is determined from a difference between a second delay (a transmission time between the master node and a second slave node) and a first delay (a transmission time between a master node and a first slave node). A deadzone mapping is applied to the initial timing error (determined from a difference between a reference skew timing and the skew timing) to determine a final timing error. The deadzone mapping is configured to adjust the initial timing error responsive to a comparison between the initial timing error and a timing error range. The first and second delays are controlled using the final timing error to keep the skew timing within the timing error range.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Robust adaptive control

Abstract: 1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.