10 research outputs found
Rakeness in the design of Analog-to-Information Conversion of Sparse and Localized Signals
Design of Random Modulation Pre-Integration systems based on the
restricted-isometry property may be suboptimal when the energy of the signals
to be acquired is not evenly distributed, i.e. when they are both sparse and
localized. To counter this, we introduce an additional design criterion, that
we call rakeness, accounting for the amount of energy that the measurements
capture from the signal to be acquired. Hence, for localized signals a proper
system tuning increases the rakeness as well as the average SNR of the samples
used in its reconstruction. Yet, maximizing average SNR may go against the need
of capturing all the components that are potentially non-zero in a sparse
signal, i.e., against the restricted isometry requirement ensuring
reconstructability. What we propose is to administer the trade-off between
rakeness and restricted isometry in a statistical way by laying down an
optimization problem. The solution of such an optimization problem is the
statistic of the process generating the random waveforms onto which the signal
is projected to obtain the measurements. The formal definition of such a
problems is given as well as its solution for signals that are either localized
in frequency or in more generic domain. Sample applications, to ECG signals and
small images of printed letters and numbers, show that rakeness-based design
leads to non-negligible improvements in both cases
Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain
Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data
Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways
Principal component analysis (PCA) is a powerful data reductionmethod for
Structural Health Monitoring. However, its computa-tional cost and data memory
footprint pose a significant challengewhen PCA has to run on limited capability
embedded platformsin low-cost IoT gateways. This paper presents a
memory-efficientparallel implementation of the streaming History PCA
algorithm.On our dataset, it achieves 10x compression factor and 59x
memoryreduction with less than 0.15 dB degradation in the
reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over,
the algorithm benefits from parallelization on multiple cores,achieving a
maximum speedup of 4.8x on Samsung ARTIK 710
Rakeness-Based Compressed Sensing of Multiple-graph Signals for IoT Applications
Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness
Communication channel analysis and real time compressed sensing for high density neural recording devices
Next generation neural recording and Brain-
Machine Interface (BMI) devices call for high density or distributed
systems with more than 1000 recording sites. As the
recording site density grows, the device generates data on the
scale of several hundred megabits per second (Mbps). Transmitting
such large amounts of data induces significant power
consumption and heat dissipation for the implanted electronics.
Facing these constraints, efficient on-chip compression techniques
become essential to the reduction of implanted systems power
consumption. This paper analyzes the communication channel
constraints for high density neural recording devices. This paper
then quantifies the improvement on communication channel
using efficient on-chip compression methods. Finally, This paper
describes a Compressed Sensing (CS) based system that can
reduce the data rate by > 10x times while using power on
the order of a few hundred nW per recording channel
Design of Scalable Hardware-Efficient Compressive Sensing Image Sensors
This work presents a new compressive sensing (CS) measurement method for image sensors, which limits pixel summation within neighbor pixels and follows regular summation patterns. Simulations with a large set of benchmark images show that the proposed method leads to improved image quality. Circuit implementation for the proposed CS measurement method is presented with the use of current mode pixel cells; and the resultant CS image sensor circuit is significantly simpler than existing designs. With compression rates of 4 and 8, the developed CS image sensors can achieve 34.2 dB and 29.6 dB PSNR values with energy consumption of 1.4 mJ and 0.73 mJ per frame, respectively
On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis
Despite the linearity of its encoding, compressed sensing may be used to
provide a limited form of data protection when random encoding matrices are
used to produce sets of low-dimensional measurements (ciphertexts). In this
paper we quantify by theoretical means the resistance of the least complex form
of this kind of encoding against known-plaintext attacks. For both standard
compressed sensing with antipodal random matrices and recent multiclass
encryption schemes based on it, we show how the number of candidate encoding
matrices that match a typical plaintext-ciphertext pair is so large that the
search for the true encoding matrix inconclusive. Such results on the practical
ineffectiveness of known-plaintext attacks underlie the fact that even
closely-related signal recovery under encoding matrix uncertainty is doomed to
fail.
Practical attacks are then exemplified by applying compressed sensing with
antipodal random matrices as a multiclass encryption scheme to signals such as
images and electrocardiographic tracks, showing that the extracted information
on the true encoding matrix from a plaintext-ciphertext pair leads to no
significant signal recovery quality increase. This theoretical and empirical
evidence clarifies that, although not perfectly secure, both standard
compressed sensing and multiclass encryption schemes feature a noteworthy level
of security against known-plaintext attacks, therefore increasing its appeal as
a negligible-cost encryption method for resource-limited sensing applications.Comment: IEEE Transactions on Information Forensics and Security, accepted for
publication. Article in pres
Adapted Compressed Sensing: A Game Worth Playing
Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing
Conversor configurável analógico para informação.
Nos conversores Analógicos Digitais (ADC) com frequência de conversão baseada no Teorema de Nyquist, o parâmetro básico para orientar a aquisição é a largura de banda do sinal. O tratamento da informação e a remoção da redundância são realizados após a representação digital obtida do sinal. A Amostragem Compressiva foi proposta como uma técnica de digitalização que explora a esparsidade do sinal em um determinado domÃnio, para capturar apenas seu conteúdo de informação, com uma taxa que pode ser menor do que a preconizada pelo Teorema de Nyquist. As arquiteturas em hardware para implementar a Amostragem Compressiva são chamadas de Conversores Analógicos para Informação (AIC). Os AIC propostos na bibliografia exploram a esparsidade do sinal em um determinado domÃnio, e por isso cada arquitetura é especifica para uma classe de sinais. Nesta tese propõe-se um AIC configurável, baseado em arquiteturas conhecidas, capaz de adquirir sinais de várias classes, alterando seus parâmetros de configuração. No trabalho desenvolveu-se um modelo computacional, que permite analisar o comportamento dinâmico do AIC, e dos parâmetros de hardware propostos, bem como foi feita a implementação fÃsica da arquitetura proposta. Verificou-se a adaptabilidade dessa arquitetura a partir dos resultados obtidos, pois foi possÃvel fazer a aquisição de mais de uma classe de sinais.In analog-to-digital converters (ADC) based on Nyquist Theorem, the basic parameter to guide acquisition is the bandwidth of the signal. The information processing and redundancy removal are performed after the digital representation obtained from the signal. Compressed Sensing was proposed as a digitalization technique that exploits the sparsity of the signal in a given domain to capture only its information content, at a rate that may be lower than that advocated by the Nyquist Theorem. The hardware architectures to implement Compressed Sensing are called Analog to Information Converters (AIC). The AICs proposed in the bibliography exploit the sparsity of the signal in a given domain, and therefore each architecture is specific for a class of signals. This thesis proposes a configurable AIC, based on known architectures, capable of acquiring signals from several classes, changing its configuration parameters. A computational model was developed to analyze the dynamic behavior of AIC and proposed hardware parameters, as well as the physical implementation of the proposed architecture. It was verified the adaptability of the proposed architecture from the obtained results, since it was possible to perform the acquisition of more than one class of signals.Cape
Algorithms and Systems for IoT and Edge Computing
The idea of distributing the signal processing along the path that starts with the acquisition and ends with the final application has given light to the Internet of Things and Edge Computing, which have demonstrated several advantages in terms of scalability, costs, and reliability. In this dissertation, we focus on designing and implementing algorithms and systems that allow performing a complex task on devices with limited resources.
Firstly, we assess the trade-off between compression and anomaly detection from both a theoretical and a practical point of view. Information theory provides the rate-distortion analysis that is extended to consider how information content is processed for detection purposes. Considering an actual Structural Health Monitoring application, two corner cases are analysed: detection in high distortion based on a feature extraction method and detection with low distortion based on Principal Component Analysis.
Secondly, we focus on streaming methods for Subspace Analysis. In this context, we revise and study state-of-the-art methods to target devices with limited computational resources. We also consider a real case of deployment of an algorithm for streaming Principal Component Analysis for signal compression in a Structural Health Monitoring application, discussing the trade-off between the possible implementation strategies.
Finally, we focus on an alternative compression framework suited for low-end devices that is Compressed Sensing. We propose a different decoding approach that splits the recovery problem into two stages and effectively adopts a deep neural network and basic linear algebra to reconstruct biomedical signals. This novel approach outperforms the state-of-the-art in terms of quality of reconstruction and requires lower computational resources