High Performance Real-time Pre-Processing for Fixed-Complexity Sphere Decoder

Pre-processing (PP) of received symbol vector and channel matrices is an essential pre-requisite operation for Sphere Decoder (SD)-based detection of Multiple-Input Multiple-Output (MIMO) wireless systems. PP is a highly complex operation, but relative to the total SD workload it represents a relatively small fraction of the overall computational cost of detecting an OFDM MIMO frame in standards such as 802.11n. Despite this, real-time PP architectures are highly inefficient, dominating the resource cost of real-time SD architectures. This paper resolves this issue. By reorganising the ordering and QR decomposition sub operations of PP, we describe a Field Programmable Gate Array (FPGA)-based PP architecture for the Fixed Complexity Sphere Decoder (FSD) applied to 4 × 4 802.11n MIMO which reduces resource cost by 50% as compared to state-of-the-art solutions whilst maintaining real-time performance.

Design Methodology for Real-Time FPGA-Based Sound Synthesis

Explicit finite difference (FD) schemes can realise highly realistic physical models of musical instruments but are computationally complex. A design methodology is presented for the creation of FPGA-based micro-architectures for FD schemes which can be applied to a range of applications with varying computational requirements, excitation and output patterns and boundary conditions. It has been applied to membrane and plate-based sound producing models, resulting in faster than real-time performance on a Xilinx XC2VP50 device which is 10 to 35 times faster than general purpose and DSP processors. The models have developed in such a way to allow a wide range of interaction (by a musician) thereby leading to the possibility of creating a highly realistic digital musical instrument.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

A prospective clinical trial of a real-time polymerase chain reaction assay for the diagnosis of candidemia in nonneutropenic, critically ill adults

Background. Invasive Candida infection among nonneutropenic, critically ill adults is a clinical problem that has received increasing attention in recent years. Poor performance of extant diagnostic modalities has promoted risk-based, preemptive prescribing in view of the poor outcomes associated with inadequate or delayed antifungal therapy; this risks unnecessary overtreatment. A rapid, reliable diagnostic test could have a substantial impact on therapeutic practice in this patient population.

Methods. Three TaqMan-based real-time polymerase chain reaction assays were developed that are capable of detecting the main medically important Candida species, categorized according to the likelihood of fluconazole susceptibility. Assay 1 detected Candida albicans, Candida parapsilosis, Candida tropicalis, and Candida dubliniensis. Assays 2 and 3 detected Candida glabrata and Candida krusei, respectively. The clinical performance of these assays, applied to serum, was evaluated in a prospective trial of nonneutropenic adults in a single intensive care unit.

Results. In all, 527 specimens were obtained from 157 participants. All 3 assays were run in parallel for each specimen; they could be completed within 1 working day. Of these, 23 specimens were obtained from 23 participants categorized as having proven Candida infection at the time of sampling. If a single episode of Candida famata candidemia was excluded, the estimated clinical sensitivity, specificity, and positive and negative predictive values of the assays in this trial were 90.9%, 100%, 100% and 99.8%, respectively.

Conclusions. These data suggest that the described assays perform well in this population for enhancing the diagnosis of candidemia. The extent to which they may affect clinical outcomes, prescribing practice, and cost-effectiveness of care remains to be ascertained.

A Novel Packet Marking Function for Real-Time Interactive MPEG-4 Video Applications in a Differentiated Services Network

The future convergence of voice, video and data applications on the Internet requires that next generation technology provides bandwidth and delay guarantees. Current technology trends are moving towards scalable aggregate-based systems where applications are grouped together and guarantees are provided at the aggregate level only. This solution alone is not enough for interactive video applications with sub-second delay bounds. This paper introduces a novel packet marking scheme that controls the end-to-end delay of an individual flow as it traverses a network enabled to supply aggregate- granularity Quality of Service (QoS). IPv6 Hop-by-Hop extension header fields are used to track the packet delay encountered at each network node and autonomous decisions are made on the best queuing strategy to employ. The results of network simulations are presented and it is shown that when the proposed mechanism is employed the requested delay bound is met with a 20% reduction in resource reservation and no packet loss in the network.

Real-time error detection but not error correction drives automatic visuomotor adaptation

We investigated the role of visual feedback of task performance in visuomotor adaptation. Participants produced novel two degrees of freedom movements (elbow flexion-extension, forearm pronation-supination) to move a cursor towards visual targets. Following trials with no rotation, participants were exposed to a 60A degrees visuomotor rotation, before returning to the non-rotated condition. A colour cue on each trial permitted identification of the rotated/non-rotated contexts. Participants could not see their arm but received continuous and concurrent visual feedback (CF) of a cursor representing limb position or post-trial visual feedback (PF) representing the movement trajectory. Separate groups of participants who received CF were instructed that online modifications of their movements either were, or were not, permissible as a means of improving performance. Feedforward-mediated performance improvements occurred for both CF and PF groups in the rotated environment. Furthermore, for CF participants this adaptation occurred regardless of whether feedback modifications of motor commands were permissible. Upon re-exposure to the non-rotated environment participants in the CF, but not PF, groups exhibited post-training aftereffects, manifested as greater angular deviations from a straight initial trajectory, with respect to the pre-rotation trials. Accordingly, the nature of the performance improvements that occurred was dependent upon the timing of the visual feedback of task performance. Continuous visual feedback of task performance during task execution appears critical in realising automatic visuomotor adaptation through a recalibration of the visuomotor mapping that transforms visual inputs into appropriate motor commands.

Applying an XC6200 to real-time image processing

This implementation of a two-dimensional discrete cosine transform demonstrates the development of a suitable architectural style for a specific technology-in this case, the Xilinx XC6200 FPGA series. The design exploits distributed arithmetic, parallelism, and pipelining to achieve a high-performance custom-computing implementation.

A Low Complexity Real-time MIMO-Preprocessing For Fixed Complexity Sphere Decoder

Modern Multiple-Input Multiple-Output (MIMO) communication systems place huge demands on embedded processing resources in terms of throughput, latency and resource utilization. State-of-the-art MIMO detector algorithms, such as Fixed-Complexity Sphere Decoding (FSD), rely on efficient channel preprocessing involving numerous calculations of the pseudo-inverse of the channel matrix by QR Decomposition (QRD) and ordering. These highly complicated operations can quickly become the critical prerequisite for real-time MIMO detection, exaggerated as the number of antennas in a MIMO detector increases. This paper describes a sorted QR decomposition (SQRD) algorithm extended for FSD, which significantly reduces the complexity and latency
of this preprocessing step and increases the throughput of MIMO detection. It merges the calculations of the QRD and ordering operations to avoid multiple iterations of QRD. Specifically, it shows that SQRD reduces the computational complexity by over 60-70% when compared to conventional
MIMO preprocessing algorithms. In 4x4 to 7x7 MIMO cases, the approach suffers merely 0.16-0.2 dB reduction in Bit Error Rate (BER) performance.

Portfolio of Compositions investigating ensemble improvisation, distributed and real-time scoring, and multi-nodal input and output

This output is a collection of compositions which explore issues of ensemble improvisation, ensemble management and orchestration, real-time and distributed scoring, multi-nodal inputs and outputs, and animated and graphic notation. Compositions include: Activities I; tutti, duet, trio, solo, quartet; Lewitt Notations I; Webwork I; and Sometimes I feel the space between people (voices) in terms of tempos. These compositions are presented in computer animated scores which are synchronized through the network and subject to real-time modification and control. They can be performed by ensembles distributed over large physical spaces connected by the network. The scores for these compositions include software which displays the animations to the performers, software to structure and disseminate score events, and triggering software that allows the control of a performance to be distributed. Scores can also include live electronics which are coordinated with graphic events.

Real-time GPU color-based segmentation of football players

In this paper, we propose a multi-camera application capable of processing high resolution images and extracting features based on colors patterns over graphic processing units (GPU). The goal is to work in real time under the uncontrolled environment of a sport event like a football match. Since football players are composed for diverse and complex color patterns, a Gaussian Mixture Models (GMM) is applied as segmentation paradigm, in order to analyze sport live images and video. Optimization techniques have also been applied over the C++ implementation using profiling tools focused on high performance. Time consuming tasks were implemented over NVIDIA's CUDA platform, and later restructured and enhanced, speeding up the whole process significantly. Our resulting code is around 4-11 times faster on a low cost GPU than a highly optimized C++ version on a central processing unit (CPU) over the same data. Real time has been obtained processing until 64 frames per second. An important conclusion derived from our study is the scalability of the application to the number of cores on the GPU. © 2011 Springer-Verlag.

Real-Time Face Recognition from Surveillance Video

This chapter describes an experimental system for the recognition of human faces from surveillance video. In surveillance applications, the system must be robust to changes in illumination, scale, pose and expression. The system must also be able to perform detection and recognition rapidly in real time. Our system detects faces using the Viola-Jones face detector, then extracts local features to build a shape-based feature vector. The feature vector is constructed from ratios of lengths and differences in tangents of angles, so as to be robust to changes in scale and rotations in-plane and out-of-plane. Consideration was given to improving the performance and accuracy of both the detection and recognition steps.

NanoStreams: Advancing the Hardware and Software Stack for Real-Time Analytics on Fast Data Streams

NanoStreams is a consortium project funded by the European Commission under its FP7 programme and is a major effort to address the challenges of processing vast amounts of data in real-time, with a markedly lower carbon footprint than the state of the art. The project addresses both the energy challenge and the high-performance required by emerging applications in real-time streaming data analytics. NanoStreams achieves this goal by designing and building disruptive micro-server solutions incorporating real-silicon prototype micro-servers based on System-on-Chip and reconfigurable hardware technologies.