Animal nervous systems can detect changes in their environments within hundredths of a second. They do so by discerning abrupt shifts in sensory neural activity. Many neuroscience studies have employed change-point detection (CPD) algorithms to estimate such abrupt shifts in neural activity. But very few studies have suggested that spiking neurons themselves are online change-point detectors. We show that a leaky integrate-and-fire (LIF) neuron implements an online CPD algorithm for a compound Poisson process. We quantify the CPD performance of an LIF neuron under various regions of its parameter space. We show that CPD can be a recursive algorithm where the output of one algorithm can be input to another. Then we show that a simple feedforward network of LIF neurons can quickly and reliably detect very small changes in input spiking rates. For example, our network detects a 5% change in input rates within 20 ms on average, and false-positive detections are extremely rare. In a rigorous statistical context, we interpret the salient features of the LIF neuron: its membrane potential, synaptic weight, time constant, resting potential, action potentials, and threshold. Our results potentially generalize beyond the LIF neuron model and its associated CPD problem. If spiking neurons perform change-point detection on their inputs, then the electrophysiological properties of their membranes must be related to the spiking statistics of their inputs. We demonstrate one example of this relationship for the LIF neuron and compound Poisson processes and suggest how to test this hypothesis more broadly. Maybe neurons are not noisy devices whose action potentials must be averaged over time or populations. Instead, neurons might implement sophisticated, optimal, and online statistical algorithms on their inputs.

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical performance. Some drawbacks are the computational overhead as well as the limitation to a point-source data model with fewer sources than sensors. In this work, we propose a Sieved Maximum Likelihood (SiML) method. It uses a general functional data model, allowing an unrestricted number of arbitrarily-shaped sources to be recovered. To this end, we leverage functional analysis tools and express the data in terms of an infinite-dimensional sampling operator acting on a Gaussian random function. We show that SiML is computationally more efficient than traditional SML, resilient to noise, and results in much better accuracy than spectral-based methods.

The starting point for deconvolution methods in radio astronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the intensity field is only a partial summary statistic of the matched filtered interferometric data, which we prove is spatially correlated on the celestial sphere. This allows us to define a sky covariance function. This previously unexplored quantity brings us additional information that can be leveraged in the process of removing dirty image artefacts. We demonstrate this using a novel unsupervised learning method. The problem is formulated on a graph: each pixel interpreted as a node, linked by edges weighted according to their spatial correlation. We then use spectral clustering to separate the artefacts in groups, and identify physical sources within them.

We propose a recurrent neural-network for real-time reconstruction of acoustic camera spherical maps. The network, dubbed DeepWave, is both physically and algorithmically motivated: its recurrent architecture mimics iterative solvers from convex optimisation, and its parsimonious parametrisation is based on the natural structure of acoustic imaging problems. Each network layer applies successive filtering, biasing and activation steps to its input, which can be interpreted as generalised deblurring and sparsification steps. To comply with the irregular geometry of spherical maps, filtering operations are implemented efficiently by means of graph signal processing techniques. Unlike commonly-used imaging network architectures, DeepWave is moreover capable of directly processing the complex-valued raw microphone correlations, learning how to optimally back-project these into a spherical map. We propose moreover a smart physically-inspired initialisation scheme that attains much faster training and higher performance than random initialisation. Our real-data experiments show DeepWave has similar computational speed to the state-of-the-art delay-and-sum imager with vastly superior resolution. While developed primarily for acoustic cameras, DeepWave could easily be adapted to neighbouring signal processing fields, such as radio astronomy, radar and sonar.

Delay-And-Sum (DAS), the state-of-art in ultrasound imaging, is known to be sub-optimal, resulting in low resolution and contrast. Most proposed improvements involve ad-hoc re-weighting, or hit computational bottlenecks given real-time requirements. This paper takes a fresh perspective on the problem, leveraging a functional framework to obtain a regularized least-squares estimate of the tissue reflectivity function. An explicit solution is derived, which - for specific cases - can be efficiently implemented, making it suitable for real-time imaging. In our formulation, DAS appears as a back-projection without any optimal properties. We illustrate the framework through first a one-dimensional set-up, and then a two-dimensional extension with Synthetic Aperture Focusing Technique (SAFT). The one-dimensional simulations show a 77% resolution improvement with respect to DAS, which artificially limits the available bandwidth. On a standard performance-assessment phantom, simulations show that SAFT depth resolution can be improved by 71%.

In our demo, we present two hardware platforms for prototyping audio array signal processing. Pyramic is a 48-channel microphone array fitted on an FPGA and Compact Six is a portable microphone array with six microphones, closer to the technical constraints of consumer electronics. A browser based interface was developed that allows the user to interact with the audio stream from the arrays in real time. The software component of this demo is a Python module with implementations of basic audio signal processing blocks and popular techniques like STFT, beamforming, and DoA. Both the hardware design files and the software are open source and freely shared. As part of a collaboration with IBM Research, their beamforming and imaging technologies will also be portrayed. The hardware will be demonstrated through an installation processing the microphone signals into light patterns on a circular LED array. The demo will be interactive and let visitors play with different algorithms for DoA (SRP, FRIDA [1], Bluebild) and beamforming (MVDR, Flexibeam [2]). The availability of an open platform with reference implementations encourages reproducible research and minimizes setup-time when testing and benchmarking new audio array signal processing algorithms. It can also serve as a useful educational tool, providing a means to work with real-life signals.

We propose a method for designing phased-arrays according to a given, analytically-specified, target beam shape. Building on the flexibeam framework, antenna locations are sampled from a probabilistic density function. Naturally scalable with the number of antennas, it is also computationally efficient and numerically stable, as it relies on analytical derivation. We prove that, under mild conditions, the achieved beamshapes converge uniformly to the target beamshapes as the number of antennas increases. We illustrate the technique through a number of examples. For instance, by use of the Laplace filter, beams with extremely fast decay away from the centre of focus are achieved. Some macroscopic observations result. We observe that matched beamforming weights may, for a given layout, achieve beamshapes targeting regions, rather than isolated directions as commonly believed. Additionally, the convergence analysis can be used to forecast the growth of future large phased arrays such as the Square Kilometre Array (SKA).

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. Furthermore, only the end image is cleaned, reducing flexibility substantially. We propose instead a method to remove the noise explicitly before imaging. To this end, we developed an algorithm that first decomposes the sensor signals into components using Singular Spectrum Analysis and then cluster these components using graph Laplacian matrix. We show through simulation the potential for radio astronomy, in particular, illustrating the benefit for LOFAR, the low frequency array in Netherlands. From telescopic data to least-squares image estimates, far higher accuracy with low computation cost results without the need for long observation time.

We propose a sparse reconstruction method based on compressed sensing theory for aperture synthesis imaging. Our algorithm directly works on observational data without grid-ding. We achieve fast convergence by introducing an adaptive tolerance parameter based on the noise level and a thresholding value based on the cumulative sum of the power of the estimated source components. We demonstrate the accuracy in estimating the source positions and intensities in extremely low signal-to-noise (SNR) scenarios in Monte Carlo simulation. We could recover both point sources and extended sources with our algorithm using a Dirac basis from real data.

Imaging techniques in radio interferometry often face a significant challenge posed by the large number of antenna signals received, from which the image information needs to be extracted. Beamforming is envisaged to reduce the rate required for transporting data from groups of antennas to a central site for further processing. We propose a novel method for image reconstruction based on the iterative scanning of a region of interest, combined with randomized beamforming. A modified approximate message-passing algorithm is adopted to extract relevant image information from beamformed signals received at the antenna stations. The method is illustrated by simulations, with reference to the LOFAR radio interferometer, and compared with the CLEAN algorithm.

We propose a new, general, method for spatial filtering by beam-forming. The desired filter, specified analytically on an n-dimensional sphere, is extended to n+1-dimensional Euclidean space. A continuous beamforming function is then obtained by the n+1-dimensional Fourier transform of the extended filter. The beamforming weight at a given array element corresponds then to a sample of the function at the array element location., The scheme is a generalisation of focused beamforming on a single point by phase difference alignment. The analytic framework allows tractable, stable determination of beamforming weights, and for clear filter specification. By avoiding approximating a Dirac, desired areas can be covered with reduced side lobes. Multiple areas may be targeted simultaneously. In communications applications, channel information updates can be reduced, and movement accounted for. A WiFi demonstration shows that more flexible beam-shapes can be beneficial for real-life examples, factoring in attenuation.

The Square Kilometre Array (SKA) will form the largest radio telescope ever built and such a huge instrument in the desert poses enormous engineering and logistic challenges. Algorithmic and architectural breakthroughs are needed. Data is collected and processed in groups of antennas before transport for central processing. This processing includes beamforming, primarily so as to reduce the amount of data sent. The principal existing technique points to a region of interest independently of the sky model and how the other stations beam-form. We propose a new collaborative beamforming algorithm in order to maximize information captured at the stations (thus reducing the amount of data transported). The method increases the diversity in measurements through randomized beamforming. We demonstrate through numerical simulation the effectiveness of the method. In particular, we show that randomized beamforming can achieve the same image quality while producing 40% less data when compared to the prevailing method matched beamforming.

Full-chip printability simulations for VLSI layouts use analytical and heuristic physical process models, and require an explicit creation of a mask and image. This is a computationally expensive task, often prohibitively so, especially when prototyping new designs. In this paper we show that using orthogonal transform-based fixed-length feature vector representations of 22nm VLSI layouts to perform classification based rapid printability prediction, can help in avoiding or reducing the number of simulations. Furthermore, in order to overcome the problem of scarcity of training data, we show how re-scaled, abundant 45nm designs can train error prediction models for new, native 22nm designs. Our experiments, run on M1 layer data and line width errors, demonstrate the viability of the proposed approach.

Real-time performance analysis of processor behaviour requires the efficient gathering of micro-architectural information from processor cores. Such information can be expected to be highly structured allowing it to be compressed, but the computational burden of conventional compression techniques exclude their use in this environment. We consider the use of new mathematical techniques that allow a signal to be compressed and recovered from a relatively small number of samples. These techniques, collectively termed compressive sampling, are asymmetric in that compression is simple, but recovery is complex. This makes them appropriate for applications in which the simplicity of the sensor can be offset against complexity at the ultimate recipient of the sensed information. We evaluate the practicality of using such techniques in the transfer of signals representing one or more micro-architectural counters from a processor core. We show that compressive sampling is usable to recover such performance signals, evaluating the trade-off between efficiency, accuracy and practicability within its various variants.

A new construction method for codes using encodings from group rings is presented. They consist primarily of two types, zero-divisor and unit-derived codes. Previous codes from group rings focused on ideals; e.g. cyclic codes are ideals in the group ring over a cyclic group. The fresh focus is on the encodings themselves, which only under very limited conditions result in ideals. Using an isomorphism between group rings and a certain well- defined ring of matrices, equivalent matrix codes are established with resulting generator and check matrices. Group rings are a fruitful source of units and zero-divisors from which new codes result. Many code properties may more easily be expressed in terms of group ring properties.

Interest in distributed storage is fueled by demand for reliability and resilience combined with decreasing hardware costs. Peer-to-peer storage networks based on distributed hash tables are attractive for their efficient use of resources and resulting performance. The placement and subsequent efficient location of replicas in such systems remain open problems, especially (1) the requirement to update replicated content, (2) working in the absence of global information, and (3) determination of the locations in a dynamic system without introducing single points of failure. We present and evaluate a novel and versatile technique, replica enumeration, which allows for controlled replication and replica access. The possibility of enumerating and addressing individual replicas allows dynamic updates as well as superior performance without burdening the network with state information, yet taking advantage of locality information when available. We simulate, analyze, and prove properties of the system, and discuss some applications.

Bloom filters impress by their sheer elegance and have become a widely and, perhaps, indiscriminately used tool in network applications, although, as we show, their performance can often be far from optimal. Notably in application areas where false negatives are tolerable, other techniques can clearly be better. We show that, at least for a specific area in the parameter space, Bloom filters are significantly outperformed even by a simple scheme. We show that many application areas where Bloom filters are deployed do not require the strong policy of no false negatives and sometimes even prefer false negatives. We analyze, through modelling, how far Bloom filters are from the optimal and then examine application specific issues in a distributed web caching scenario. We hope to open up and seed discussion towards domain-specific alternatives to Bloom filters while perhaps sparking ideas for a general-purpose alternative.

Interest in distributed storage is fueled by demand for reliability and resilience combined with ubiquitous availability. Peer-to-peer (P2P) storage networks are known for their decentralized control, self-organization, and adaptation. Advanced searching for documents and resources remains an open problem. The flooding approach favored by some P2P networks is inefficient in resource usage, but more scalable and resource-efficient solutions based on distributed hash tables (DHT) lack in query expressiveness and flexibility. In this paper, we address this issue and introduce new efficient, scalable, and completely distributed methods that strive to keep resource consumption by queries and index information as low as possible. We describe how to improve the handling of multiple subqueries combined through Boolean set operators. The need for these operators is intensified by applications to go beyond simple exact keyword matches. We discuss, optimize, and analyze appropriate extensions to support range and prefix matching in DHT.

We present an admission control scheme which provides per-flow delay and bandwidth guarantees based solely upon simple class-based strict priority queueing. We derive basic properties of the worst-case behaviour in strict priority queueing systems using network calculus. Building upon these properties, a flow admission control scheme is devised. The rationale behind this work is the appealing simplicity as well as the almost ubiquitous availability of strict priority queueing in today's routers and the thus promising applicability of our results for practical purposes in providing quality of service (QoS) in the Internet.

A model is developed for determining the optimal policy for processing a given relational model query. The model is based on operating cost (processing cost and communication cost), which is a function of selection of sites for processing query operations, sequence of operations, file size, and data reduction functions. The optimal policy specifies the site selection and sequence of operations that yield minimum operating cost.