1,721,592 research outputs found
Integer-modulated filter banks providing perfect reconstruction
Full text linkPublication in the conference proceedings of EUSIPCO, Tampere, Finland, 200
Tensor representation in high-frequency financial data for price change prediction
No full textNowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones
Fused Geometry Augmented Images for Analyzing Textured Mesh
No full textIn this paper, we propose a multi-modal mesh surface representation by fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images. This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by employing standard convolutional neural networks in a transfer-learning mode. In contrast to existing methods, the proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representations by effectively fusing shape and texture information at the data level. The efficacy is demonstrated on the task of facial expression classification, showing competitive performance with state-of-the-art methods.</p
A power-efficient current generator with common mode signal autozero feedback for bioimpedance measurement applications
Full text linkThis paper describes the design of fully differential sine pulse-width-modulation (SPWM) wave current generator for bioimpedance measurement applications. The current generator has been designed in a 0.18-µm CMOS technology. Its analog front-end operates from ±1.65 V and has a current consumption of + + ( ×. ) where is the output current and is the operating frequency. It can provide outputs from to of SPWM current up to 98 kHz with a maximum voltage compliance of ±1.25 V. Using linear current feedback, the current generator has a designed transconductance of /. Feedback also enables cancellation of common mode signals and a high output impedance
Live demonstration: A wearable torso shape detection belt for lung respiration monitoring
Full text linkA 32 channel wearable torso shape detection belt will be demonstrated. The belt is designed to measure the torso shape of a neonate and provide real-time boundary information to assist the electrical impedance tomography (EIT) system to produce high quality lung respiration images. The system is fully integrated on a flexible printed circuit board which is encapsulated in a silicon wearable cover. During the live demonstration, while EIT images are reconstructed, the boundary shape can be changed to improve the image
Small sample properties of the Yule-Walker method for autoregressive parameter estimation
Full text linkIn this paper we will give the expectation of (the square of) the reflection coefficient, residual variance and prediction error in small sample statistics in white noise. We will construct approximations of these expectations which are more accurate than the known first order Taylor approximations. We need these better approximations because in some applications (radar applications for example) the number of observations is small
Estimation and structural based approach for the design of optimal stack filters
No full textTwo approaches have been used in the past to design or choose a rank-order based filter to estimate a signal from a noise corrupted observation of that signal: the structural and the estimation approaches. In the structural approach, the goal is to find a filter which preserves those shapes that are part of the signal while removing those that are part of the noise. In the estimation approach, the goal is to find a filter which best estimates the desired signal, given the noise corrupted version of the signal as the filter\u27s input. The first part of this thesis develops a theory for the structural behavior of stack filters. This theory provides: a test which can determine if a given stack filter has any root signals; a method for classifying the root signal behavior of any stack filter found to have roots; and, perhaps most importantly, a method for designing stack filters with specific root signals or other structural behavior. This theory of root signals for stack filters is then combined with the theory of minimum mean absolute error stack filtering. This new, unified theory allows the designer to pick a filter which minimizes noise subject to constraints on its structural behavior. The second part of the thesis deals with the convergence behavior of stack filters. First, stack filters (or positive Boolean functions (PBFs), in the binary case) are classified into four different types, called type-0 through type-3. It is shown that PBFs of type-0 through type-2 possess the convergence property, while type-3 PBFs do not all share this property. The rates of convergence for a subset of convergent PBFs, namely stack filters of of type-0 through type-2, is also determined. The convergence behavior and rates of convergence is then generalized to include stack filters of type-0 through type-2 with index i. In the final part of this thesis, a new optimization theory for stack filters is presented. This new theory is based on the minimax error criterion rather than the mean absolute error criterion used in (Coy) and (CoL). In the binary case, a methodology is designed to find the optimal stack filter that minimizes the maximum absolute error between the input and the output signals. The most interesting feature of this optimization procedure is the fact that it can be solved using a linear program, just like in the MMAE case (CoL). When generalizing to multiple inputs, complexity problems will arise and two alternative approaches will be suggested
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