Quaternary optical ASK-DPSK and receivers with direct detection | IEEE Journals & Magazine | IEEE Xplore
We present a novel quaternary optical-modulation scheme based on the combination of amplitude and phase modulation. The modulator has a simple structure and can be realized by using standard components. We present two simple receivers, each one using photodiodes for direct detection. The performance of the modulation scheme is investigated.
Assigning a wavelength label as well as a label in a DPSK modulation format orthogonal to the data payload significantly increases the forwarding and routing capabilities of optical packet routers in IP-over-WDM networks.
Nonlinear crosstalk and compensation in QDPASK optical communication systems | IEEE Journals & Magazine | IEEE Xplore
Nonlinear crosstalk in the quaternary differential-phase amplitude-shift-keying (QDPASK) modulation format is analyzed. Significant crosstalk penalty is measured on a QDPASK signal with a 20-Gb/s aggregate capacity for nonlinear phase shifts of 0.17/spl pi/ rad and above. Two different compensation techniques are demonstrated based on either prechirping or postchirping of the optical signal, increasing the nonlinear tolerance by 6.1 and 4.5 dB, respectively.
Analytical evaluation of bit error rates for hard detection of optical differential phase amplitude shift keying (DPASK) | IEEE Journals & Magazine | IEEE Xplore
Recently introduced extensions of differential phase-shift keying (DPSK), referred to here as optical differential phase amplitude shift keying (DPASK), explore an increase in the data throughput for a given bandwidth by effectively multiplexing differential phase encoding and amplitude modulation onto the same fiber link. The DPASK systems proposed and demonstrated so far apply phase and amplitude modulation in tandem, jumping between either two or four equispaced phase values as well as independently selecting between two amplitude levels. In this paper, closed-form expressions for the quantum limits of bit error rate (BER) for such DPASK optical transmission systems are derived for the first time, verifying the analytic expressions by numerical multicanonical Monte Carlo simulations. The resulting quantum-limit sensitivities indicate that the two-level binary phase DPASK incurs a considerable photonic sensitivity penalty in return for its improved spectral efficiency. On the positive side, the more complex quaternary phase DPASK format exceeds the performance of its 8-ary DPSK scheme counterpart.
High Performance Flexible Protocol for Backscattered-Based Neural Implants | IEEE Conference Publication | IEEE Xplore
This work presents a custom high-performance protocol for bi-directional communication with neural implants, that will eventually enable closed-loop operation. This protocol presents a flexible configuration to communicate to neural implants with different characteristics. It can support different uplink data rates, a variable number of neural channels from 2 to 16, two types of digital signal modulation (Amplitude Shift-Keying, ASK, and Binary Shift-Keying, PSK), and different RF operation frequencies (915MHz being the default). The proposed protocol is implemented in C++ (preferred to Python because it enables fast signal processing algorithms), using GNU-Radio toolkit with custom communication blocks.
Neuromagnetic Decoding of Simultaneous Bilateral Hand Movements for Multidimensional Brain–Machine Interfaces | IEEE Journals & Magazine | IEEE Xplore
To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG | IEEE Journals & Magazine | IEEE Xplore
Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep .
An embedded EEG analyzing system based on μC/os-II | IEEE Conference Publication | IEEE Xplore
An EEG analyzing system based on advanced RISC machines (ARM) and μC/os-II real time operating system is discussed in this paper. The detailed system design including the producing of event signals and the synchronization between event signals and EEG signals is described. The details of data acquisition, data preprocessing, data transmitting through USB and system configurations are also contained in the system design. In this paper the design of high capability amplifier and the software of embedded subsystem are discussed. Also the design of realizing multi-task system in μC/os-II, the definition of communicating protocols between PC and the equipment and the detail configurations of USB are given out. The final test shows that the filter behaviors of this equipment are feasible.
Prediction of Seizure via Residual Networks Based on Decision Fusion | IEEE Conference Publication | IEEE Xplore
Two seizure prediction models are built based on a decision fusion strategy and residual network by using spatial coupling features and introducing an attention mechanism. First, eight frequency bands are filtered, and the correlation matrices are computed for each frequency of eighteen channels. Second, the eight 18x18 matrices are input to the residual module for classification, and the results are concatenated to form a vector. A fully connected layer is used for decision fusion. Third, to emphasize the coupling relationship among the different frequency bands, a cubic matrix formed by the eight 18x18 matrices is inputted to an attention network, resulting in the enhanced features. A seizure prediction model is thus proposed by combining the nine decisions. The performance of the model is compared with those from state-of-the-art methods, and the sensitivity of the proposed model is improved by 4.45%.
Artificial intelligence, interpreting data from a device placed at the brain’s surface, enables people who are paralyzed or have severely impaired limb movement to communicate by text.
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