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This paper proposes a method to estimate temporally accurate human pulse peaks for noncontact pulse transit time (PTT) measurements. The PTT is considered as a significant diagnostic index for conditions such as blood pressure and arterial stiffness; however, millisecond-order accuracy is required in the determination of each pulse peak. In this study, human pulse waveforms are obtained from wrist...
Pulse rate and rhythm are indicators of the health of a human's blood circulation. Being able to detect one's pulse rate and rhythm in an emergency situation could be the difference between life and death. The work presented in this paper is preliminary work on algorithms that will equip a robot with the necessary skills to assess a human's pulse. Algorithms for pulse detection and the calculation...
Discovering an efficient representation that reflects the structure of a signal ensemble is a requirement of many Machine Learning and Signal Processing methods, and gaining increasing prevalence in sensing systems. This type of representation can be constructed by Convolutive Non-negative Matrix Factorization (CNMF), which finds parts-based convolutive representations of non-negative data. However,...
In the search space of a complex-valued multilayer perceptron (C-MLP) there exist flat areas called singular regions. Although singular regions cause serious stagnation of learning, there exist descending paths from the regions. Based on this observation, a completely new learning method for C-MLP, called C-SSF1.0, was proposed, making good use of singular regions to stably find excellent solutions...
Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a...
Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new...
We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the...
Policy evaluation has long been one of the core issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed...
The recently introduced Dynamic Cortex Memory (DCM) is an extension of the Long Short Term Memory (LSTM) providing a systematic inter-gate connection infrastructure. In this paper the behavior of DCM networks is studied in more detail and their potential in the field of gradient-based sequence learning is investigated. Hereby, DCM networks are analyzed regarding particular key features of neural signal...
Correntropy has been successfully applied in non-Gaussian signal processing, but the superior performance achieved is depends on appropriate selection of the kernel width. How to select a proper kernel width is a crucial problem in correntropy applications. In this paper, we propose an adaptive algorithm to update the kernel width, which is set at a maximum between the absolute value of instantaneous...
This paper presents a multi-layer reproducing kernel Hilbert space (RKHS) approach for probability distribution to real and probability distribution to function regressions. The approach maps the distributions into RKHS by distribution embeddings and, then, constructs a multi-layer RKHS within which the multi-kernel distribution regression can be implemented using an existing kernel regression algorithm,...
Kernel methods provide an efficient nonparametric model to produce adaptive nonlinear filtering (ANF) algorithms. However, in practical applications, standard squared error based kernel methods suffer from two main issues: (1) a constant step size is used, which degrades the algorithm performance in non-stationary environment, and (2) additive noises are assumed to follow Gaussian distribution, while...
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