Malcolm D. Macleod

Bio: Malcolm D. Macleod is an academic researcher from Qinetiq. The author has contributed to research in topics: Forgetting & Adaptive filter. The author has an hindex of 30, co-authored 105 publications receiving 3386 citations. Previous affiliations of Malcolm D. Macleod include University of Aberdeen & St Mary's College, St Andrews.

Use of minimum-adder multiplier blocks in FIR digital filters

Abstract: The computational complexity of VLSI digital filters using fixed point binary multiplier coefficients is normally dominated by the number of adders used in the implementation of the multipliers. It has been shown that using multiplier blocks to exploit redundancy across the coefficients results in significant reductions in complexity over methods using canonic signed-digit (CSD) representation, which in turn are less complex than standard binary representation. Three new algorithms for the design of multiplier blocks are described: an efficient modification to an existing algorithm, a new algorithm giving better results, and a hybrid of these two which trades off performance against computation time. Significant savings in filter implementation cost over existing techniques result in all three cases. For a given wordlength, it was found that a threshold set size exists above which the multiplier block is extremely likely to be optimal. In this region, design computation time is substantially reduced. >

Fast nearly ML estimation of the parameters of real or complex single tones or resolved multiple tones

Abstract: This paper presents new computationally efficient algorithms for estimating the parameters (frequency, amplitude, and phase) of one or more real tones (sinusoids) or complex tones (cisoids) in noise from a block of N uniformly spaced samples. The first algorithm is an interpolator that uses the peak sample in the discrete Fourier spectrum (DFS) of the data and its two neighbors. We derive Cramer-Rao bounds (CRBs) for such interpolators and show that they are very close to the CRB's for the maximum likelihood (ML) estimator. The new algorithm almost reaches these bounds. A second algorithm uses the five DFS samples centered on the peak to produce estimates even closer to ML. Enhancements are presented that maintain nearly ML performance for small values of N. For multiple complex tones with frequency separations of at least 4/spl pi//N rad/sample, unbiased estimates are obtained by incorporating the new single-tone estimators into an iterative "cyclic descent" algorithm, which is a computationally cheap nonlinear optimization. Single or multiple real tones are handled in the same way. The new algorithms are immune to nonzero mean signals and (provided N is large) remain near-optimal in colored and non-Gaussian noise.

Constant integer multiplication using minimum adders

Abstract: A new method of formulating constant integer multiplication is presented. It requires fewer adders in general than a canonic signed-digit (CSD) representation. Graphs are used to illustrate multiplier implementation. A general suboptimal algorithm for the design of multipliers of any wordlength is presented. For 32-bit words, it achieves an average improvement of 26.6% over CSD. Rules for the generation of graphs with the minimum number of adders and subtracters are presented. An exhaustive search algorithm using these rules is described, and applied for word-lengths up to 12 bits. For 12-bit words, it was found that an average improvement of 16% over CSD is achievable.

Gone but Not Forgotten: The Transient Nature of Retrieval-Induced Forgetting

Abstract: Recent research has demonstrated that the act of remembering can prompt temporary forgetting or, more specifically, the inhibition of particular items in memory. Extending work of this kind, the present research investigated some possible boundary conditions of retrieval-induced forgetting. As expected, a critical determinant of temporary forgetting was the interval between guided retrieval practice and a final recall test. When these two phases were separated by 24 hr, retrieval-induced forgetting failed to emerge. When they occurred in the same testing session, however, retrieval practice prompted the inhibition of related items in memory (i.e., Experiment 1). A delay of 24 hr between the encoding of material and guided retrieval practice reduced but did not eliminate retrieval-induced forgetting (i.e., Experiment 2). These findings are considered in the wider context of adaptive forgetting.

On recollections lost: When practice makes imperfect

Abstract: Recent research has demonstrated that the act of remembering can prompt forgetting or, more specifically, the inhibition of specific items in memory (M. C. Anderson & B. A. Spellman, 1995). This line of inquiry was extended through an investigation of the process and consequences of retrieval-induced forgetting in social cognition. Across 3 studies, the findings clarify several unresolved issues in the psychology of forgetting. First, it was demonstrated that retrieval-induced forgetting extends to issues in social cognition (Experiment 1). Second, forgetting can be elicited even in task contexts in which perceivers are highly motivated to remember the presented material (Experiment 2). Third, forgetting is not moderated by the amount of retrieval practice that perceivers experience (Experiment 3). These findings are considered in the context of recent treatments of cognitive inhibition and goal-directed forgetting.

Stochastic Differential Equations

Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Particle Markov chain Monte Carlo methods

Abstract: Summary. Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Levy-driven stochastic volatility model.

An overview of limited feedback in wireless communication systems

Abstract: It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

Emotional stress and eyewitness memory: a critical review.

Abstract: The eyewitness literature often claims that emotional stress leads to an impairment in memory and, hence, that details of unpleasant emotional events are remembered less accurately than details of neutral or everyday events. A common assumption behind this view is that a decrease in available processing capacity occurs at states of high emotional arousal, which, therefore, leads to less efficient memory processing. The research reviewed here shows that this belief is overly simplistic. Current studies demonstrate striking interactions between type of event, type of detail information, time of test, and type of retrieval information. This article also reviews the literature on memory for stressful events with respect to two major theories: the Yerkes-Dodson law and Easter-brook's cue-utilization hypothesis. To account for the findings from real-life studies and laboratory studies, this article discusses the possibility that emotional events receive some preferential processing mediated by factors related to early perceptual processing and late conceptual processing.