On stochastic string stability with applications to platooning over additive noise channels

This paper addresses the string stabilization of vehicular platooning when stochastic phenomena are inherent in inter-vehicle communication. To achieve this, we first provide two definitions to analytically assess the string stability in stochastic scenarios, considering the mean and variance of tracking errors as the platoon size grows. Subsequently, we analytically derive necessary and sufficient conditions to achieve this notion of string stability in predecessor-following linear platoons that communicate through additive white noise channels. We conclude that the condition ensuring string stability with ideal communication is essentially the same that achieves stochastic string stability when additive noise channels are in place and guarantees that the tracking error means and variances converge.Comment: 12 pages, 6 figures, preprint submitted to Automatic

SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction

The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regularized objective. We build on the regularized fine-tuning of a pretrained DIP, by adopting a novel strategy that restricts the learning to the adaptation of singular values. The proposed SVD-DIP uses ad hoc convolutional layers whose pretrained parameters are decomposed via the singular value decomposition. Optimizing the DIP then solely consists in the fine-tuning of the singular values, while keeping the left and right singular vectors fixed. We thoroughly validate the proposed method on real-measured $\mu$CT data of a lotus root as well as two medical datasets (LoDoPaB and Mayo). We report significantly improved stability of the DIP optimization, by overcoming the overfitting to noise

Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging

Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution. To reconstruct an image of the tracer concentration, the magnetization dynamics of the particles must be accurately modeled. A popular ensemble model is based on solving the Fokker-Plank equation, taking into account either Brownian or N\'eel dynamics. The disadvantage of this model is that it is computationally expensive due to an underlying stiff differential equation. A simplified model is the equilibrium model, which can be evaluated directly but in most relevant cases it suffers from a non-negligible modeling error. In the present work, we investigate an extended version of the equilibrium model that can account for particle anisotropy. We show that this model can be expressed as a series of Bessel functions, which can be truncated based on a predefined accuracy, leading to very short computation times, which are about three orders of magnitude lower than equivalent Fokker-Planck computation times. We investigate the accuracy of the model for 2D Lissajous MPI sequences and show that the difference between the Fokker-Planck and the equilibrium model with anisotropy is sufficiently small so that the latter model can be used for image reconstruction on experimental data with only marginal loss of image quality, even compared to a system matrix-based reconstruction

Acoustic Event Detection and Localization with Regression Forests

This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events using random forest regression. The audio signals are decomposed into multiple densely overlapping {\em superframes} annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using the displacement information, a multivariate random forest regression model is learned for each event category to map each superframe to continuous estimates of onset and offset locations of the events. In addition, two classifiers are trained using random forest classification to classify superframes of background and different event categories. On testing, based on the detection of category-specific superframes using the classifiers, the learned regressor provides the estimates of onset and offset locations in time of the corresponding event. While posing event detection and localization as a regression problem is novel, the quantitative evaluation on ITC-Irst database of highly variable acoustic events shows the efficiency and potential of the proposed approach

Early Event Detection in Audio Streams

Audio event detection has been an active field of research in recent years. However, most of the proposed methods, if not all, analyze and detect complete events and little attention has been paid for early detection. In this paper, we present a system which enables early audio event detection in continuous audio recordings in which an event can be reliably recognized when only a partial duration is observed. Our evaluation on the ITC-Irst database, one of the standard database of the CLEAR 2006 evaluation, shows that: on one hand, the proposed system outperforms the best baseline system by 16% and 8% in terms of detection error rate and detection accuracy respectively; on the other hand, even partial events are enough to achieve the performance that is obtainable when the whole events are observed

Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it from the deep architectures that have been proposed for the task: varying-size convolutional filters at the convolutional layer and 1-max pooling scheme at the pooling layer. In intuition, the network tends to select the most discriminative features from the whole audio signals for recognition. Our proposed CNN not only shows state-of-the-art performance on the standard task of robust audio event recognition but also outperforms other deep architectures up to 4.5% in terms of recognition accuracy, which is equivalent to 76.3% relative error reduction

Early Prediction of Future Hand Movements Using sEMG Data

We study in this work the feasibility of early prediction of hand movement based on sEMG signals to overcome the time delay issue of the conventional classification. Opposed to the classification task, the objective of the early prediction task is to predict a hand movement that is going to occur in the future given the information up to the current time point. The ability of early prediction may allow a hand prosthesis control system to compensate for the time delay and, as a result, improve the usability. Experimental results on the Ninapro database show that we can predict up to 300 ms ahead in the future while the prediction accuracy remains very close to that of the standard classification, i.e. it is just marginally lower. Furthermore, historical data prior the current time window is shown very important to improve performance, not only for the prediction but also the classification task