12 research outputs found
Custom architecture for multicore audio Beamforming systems
The audio Beamforming (BF) technique utilizes microphone arrays to extract acoustic sources recorded in a noisy environment. In this article, we propose a new approach for rapid development of multicore BF systems. Research on literature reveals that the majority of such experimental and commercial audio systems are based on desktop PCs, due to their high-level programming support and potential of rapid system development. However, these approaches introduce performance bottlenecks, excessive power consumption, and increased overall cost. Systems based on DSPs require very low power, but their performance is still limited. Custom hardware solutions alleviate the aforementioned drawbacks, however, designers primarily focus on performance optimization without providing a high-level interface for system control and test. In order to address the aforementioned problems, we propose a custom platform-independent architecture for reconfigurable audio BF systems. To evaluate our proposal, we implement our architecture as a heterogeneous multicore reconfigurable processor and map it onto FPGAs. Our approach combines the software flexibility of General-Purpose Processors (GPPs) with the computational power of multicore platforms. In order to evaluate our system we compare it against a BF software application implemented to a low-power Atom 330, amiddle-ranged Core2 Duo, and a high-end Core i3. Experimental results suggest that our proposed solution can extract up to 16 audio sources in real time under a 16-microphone setup. In contrast, under the same setup, the Atom 330 cannot extract any audio sources in real time, while the Core2 Duo and the Core i3 can process in real time only up to 4 and 6 sources respectively. Furthermore, a Virtex4-based BF system consumes more than an order less energy compared to the aforementioned GPP-based approaches. © 2013 ACM
PERFORMANCE IMPROVEMENT OF MULTICHANNEL AUDIO BY GRAPHICS PROCESSING UNITS
Multichannel acoustic signal processing has undergone major development
in recent years due to the increased complexity of current audio processing
applications. People want to collaborate through communication with the
feeling of being together and sharing the same environment, what is considered
as Immersive Audio Schemes. In this phenomenon, several acoustic
e ects are involved: 3D spatial sound, room compensation, crosstalk cancelation,
sound source localization, among others. However, high computing
capacity is required to achieve any of these e ects in a real large-scale system,
what represents a considerable limitation for real-time applications.
The increase of the computational capacity has been historically linked
to the number of transistors in a chip. However, nowadays the improvements
in the computational capacity are mainly given by increasing the
number of processing units, i.e expanding parallelism in computing. This
is the case of the Graphics Processing Units (GPUs), that own now thousands
of computing cores. GPUs were traditionally related to graphic or image
applications, but new releases in the GPU programming environments,
CUDA or OpenCL, allowed that most applications were computationally
accelerated in elds beyond graphics. This thesis aims to demonstrate
that GPUs are totally valid tools to carry out audio applications that require
high computational resources. To this end, di erent applications in
the eld of audio processing are studied and performed using GPUs. This
manuscript also analyzes and solves possible limitations in each GPU-based
implementation both from the acoustic point of view as from the computational
point of view. In this document, we have addressed the following
problems:
Most of audio applications are based on massive ltering. Thus, the
rst implementation to undertake is a fundamental operation in the audio
processing: the convolution. It has been rst developed as a computational
kernel and afterwards used for an application that combines multiples convolutions
concurrently: generalized crosstalk cancellation and equalization.
The proposed implementation can successfully manage two di erent and
common situations: size of bu ers that are much larger than the size of the
lters and size of bu ers that are much smaller than the size of the lters.
Two spatial audio applications that use the GPU as a co-processor have been developed from the massive multichannel ltering. First application
deals with binaural audio. Its main feature is that this application is able
to synthesize sound sources in spatial positions that are not included in the
database of HRTF and to generate smoothly movements of sound sources.
Both features were designed after di erent tests (objective and subjective).
The performance regarding number of sound source that could be rendered
in real time was assessed on GPUs with di erent GPU architectures. A
similar performance is measured in a Wave Field Synthesis system (second
spatial audio application) that is composed of 96 loudspeakers. The proposed
GPU-based implementation is able to reduce the room e ects during
the sound source rendering.
A well-known approach for sound source localization in noisy and reverberant
environments is also addressed on a multi-GPU system. This
is the case of the Steered Response Power with Phase Transform (SRPPHAT)
algorithm. Since localization accuracy can be improved by using
high-resolution spatial grids and a high number of microphones, accurate
acoustic localization systems require high computational power. The solutions
implemented in this thesis are evaluated both from localization and
from computational performance points of view, taking into account different
acoustic environments, and always from a real-time implementation
perspective.
Finally, This manuscript addresses also massive multichannel ltering
when the lters present an In nite Impulse Response (IIR). Two cases are
analyzed in this manuscript: 1) IIR lters composed of multiple secondorder
sections, and 2) IIR lters that presents an allpass response. Both
cases are used to develop and accelerate two di erent applications: 1) to
execute multiple Equalizations in a WFS system, and 2) to reduce the
dynamic range in an audio signal.Belloch Rodríguez, JA. (2014). PERFORMANCE IMPROVEMENT OF MULTICHANNEL AUDIO BY GRAPHICS PROCESSING UNITS [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/40651TESISPremios Extraordinarios de tesis doctorale
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Demystifying Internet of Things Security
Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. Demystifying Internet of Things Security provides clarity to industry professionals and provides and overview of different security solutions What You'll Learn Secure devices, immunizing them against different threats originating from inside and outside the network Gather an overview of the different security building blocks available in Intel Architecture (IA) based IoT platforms Understand the threat pyramid, secure boot, chain of trust, and the software stack leading up to defense-in-depth Who This Book Is For Strategists, developers, architects, and managers in the embedded and Internet of Things (IoT) space trying to understand and implement the security in the IoT devices/platforms
From insights to innovations : data mining, visualization, and user interfaces
This thesis is about data mining (DM) and visualization methods for gaining insight into multidimensional data. Novel, exploratory data analysis tools and adaptive user interfaces are developed by tailoring and combining existing DM and visualization methods in order to advance in different applications.
The thesis presents new visual data mining (VDM) methods that are also implemented in software toolboxes and applied to industrial and biomedical signals: First, we propose a method that has been applied to investigating industrial process data. The self-organizing map (SOM) is combined with scatterplots using the traditional color linking or interactive brushing. The original contribution is to apply color linked or brushed scatterplots and the SOM to visually survey local dependencies between a pair of attributes in different parts of the SOM. Clusters can be visualized on a SOM with different colors, and we also present how a color coding can be automatically obtained by using a proximity preserving projection of the SOM model vectors. Second, we present a new method for an (interactive) visualization of cluster structures in a SOM. By using a contraction model, the regular grid of a SOM visualization is smoothly changed toward a presentation that shows better the proximities in the data space. Third, we propose a novel VDM method for investigating the reliability of estimates resulting from a stochastic independent component analysis (ICA) algorithm. The method can be extended also to other problems of similar kind. As a benchmarking task, we rank independent components estimated on a biomedical data set recorded from the brain and gain a reasonable result.
We also utilize DM and visualization for mobile-awareness and personalization. We explore how to infer information about the usage context from features that are derived from sensory signals. The signals originate from a mobile phone with on-board sensors for ambient physical conditions. In previous studies, the signals are transformed into descriptive (fuzzy or binary) context features. In this thesis, we present how the features can be transformed into higher-level patterns, contexts, by rather simple statistical methods: we propose and test using minimum-variance cost time series segmentation, ICA, and principal component analysis (PCA) for this purpose. Both time-series segmentation and PCA revealed meaningful contexts from the features in a visual data exploration.
We also present a novel type of adaptive soft keyboard where the aim is to obtain an ergonomically better, more comfortable keyboard. The method starts from some conventional keypad layout, but it gradually shifts the keys into new positions according to the user's grasp and typing pattern.
Related to the applications, we present two algorithms that can be used in a general context: First, we describe a binary mixing model for independent binary sources. The model resembles the ordinary ICA model, but the summation is replaced by the Boolean operator OR and the multiplication by AND. We propose a new, heuristic method for estimating the binary mixing matrix and analyze its performance experimentally. The method works for signals that are sparse enough. We also discuss differences on the results when using different objective functions in the FastICA estimation algorithm. Second, we propose "global iterative replacement" (GIR), a novel, greedy variant of a merge-split segmentation method. Its performance compares favorably to that of the traditional top-down binary split segmentation algorithm.reviewe
Cyber Security and Critical Infrastructures 2nd Volume
The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems