Deep Neural Network Architectures for Modulation Classification

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture of [1] and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR accuracies of approximately 83.5% and 86.6%, respectively. Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88.5% at high SNR.Comment: 5 pages, 10 figures, In proc. Asilomar Conference on Signals, Systems, and Computers, Nov. 201

ECG Signal Wavelet Filtering

Cílem této práce bylo seznámit se s vlnkovou transformací a následné využití této transformace k filtraci zarušeného EKG signálu myopotenciály. První část práce obsahuje základní informace o měření, průběhu a rušení EKG signálu. V další částí je popsána metoda vlnkové transformace a její využití pro filtraci signálu. V Poslední části je popsaná praktická část práce, kde se hodnotí výsledky filtrace při použití různých nastavení filtru, zejména různých druhů prahování. Na konci této části je srovnání výsledků vlnkové a lineární filtrace.The aim of this work is introduction to problems about the wavelet transform and then to use this transformation to filter ECG signal disturbed with myopotentials. The first part of this thesis contains basic information about the measurement, waveform and disturbance of ECG signal. The next part describes the method of wavelet transform and its use for the signal filtering The last part describes the practical part of work, where the results of filtering with different filter settings are evaluated, especially the various types of thresholding. At the end of this part is a comparison of results from wavelet and linear filter.

In vivo measurements with robust silicon-based multielectrode arrays with extreme shaft lengths

In this paper, manufacturing and in vivo testing of extreme-long Si-based neural microelectrode arrays are presented. Probes with different shaft lengths (15–70 mm) are formed by deep reactive ion etching and have been equipped with platinum electrodes of various configurations. In vivo measurements on rats indicate good mechanical stability, robust implantation, and targeting capability. High-quality signals have been recorded from different locations of the cerebrum of the rodents. The accompanied tissue damage is characterized by histology

Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems

Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized