1,782 research outputs found
A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection
Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of −1.658 × 10−3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: −0.4156 × 10−3) and DispEn (slope: −0.1675 × 10−3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis
Optimal speed trajectory and energy management control for connected and automated vehicles
Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle).
The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles.
In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation.
The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces
Analytical Models and Artificial Intelligence for Open and Partially Disaggregated Optical Networks
L'abstract è presente nell'allegato / the abstract is in the attachmen
Efficient Deep Learning for Real-time Classification of Astronomical Transients
A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential.
The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy
Low-Complexity Soft-Decision Detection for Combating DFE Burst Errors in IM/DD Links
The deployment of non-binary pulse amplitude modulation (PAM) and soft
decision (SD)-forward error correction (FEC) in future intensity-modulation
(IM)/direct-detection (DD) links is inevitable. However, high-speed IM/DD links
suffer from inter-symbol interference (ISI) due to bandwidth-limited hardware.
Traditional approaches to mitigate the effects of ISI are filters and
trellis-based algorithms targeting symbol-wise maximum a posteriori (MAP)
detection. The former approach includes decision-feedback equalizer (DFE), and
the latter includes Max-Log-MAP (MLM) and soft-output Viterbi algorithm (SOVA).
Although DFE is easy to implement, it introduces error propagation. Such burst
errors distort the log-likelihood ratios (LLRs) required by SD-FEC, causing
performance degradation. On the other hand, MLM and SOVA provide near-optimum
performance, but their complexity is very high for high-order PAM. In this
paper, we consider a one-tap partial response channel model, which is relevant
for high-speed IM/DD links. We propose to combine DFE with either MLM or SOVA
in a low-complexity architecture. The key idea is to allow MLM or SOVA to
detect only 3 typical DFE symbol errors, and use the detected error information
to generate LLRs in a modified demapper. The proposed structure enables a
tradeoff between complexity and performance: (i) the complexity of MLM or SOVA
is reduced and (ii) the decoding penalty due to error propagation is mitigated.
Compared to SOVA detection, the proposed scheme can achieve a significant
complexity reduction of up to 94% for PAM-8 transmission. Simulation and
experimental results show that the resulting SNR loss is roughly 0.3 to 0.4 dB
for PAM-4, and becomes marginal 0.18 dB for PAM-8.Comment: This manuscript has been submitted to JL
Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM
Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes -- passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is important to test the robustness of the already proposed countermeasures in this regard.
In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean () conversion.
We exploit the data dependency of the adder carry chain in and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform
The Sparse Abstract Machine
We propose the Sparse Abstract Machine (SAM), an abstract machine model for
targeting sparse tensor algebra to reconfigurable and fixed-function spatial
dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse
primitives that encompass a large space of scheduled tensor algebra
expressions. SAM dataflow graphs naturally separate tensor formats from
algorithms and are expressive enough to incorporate arbitrary iteration
orderings and many hardware-specific optimizations. We also present Custard, a
compiler from a high-level language to SAM that demonstrates SAM's usefulness
as an intermediate representation. We automatically bind from SAM to a
streaming dataflow simulator. We evaluate the generality and extensibility of
SAM, explore the performance space of sparse tensor algebra optimizations using
SAM, and show SAM's ability to represent dataflow hardware.Comment: 18 pages, 17 figures, 3 table
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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