In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.

A novel application-dependent interconnect testing scheme of Xilinx Field Programmable Gate Arrays (FPGAs) based on line branches partitioning is presented. The targeted line branches of the interconnects in FPGAs' Application Configurations (ACs) are partitioned into multiple subsets, so that they can be tested with compatible Configurable Logic Blocks (CLBs) configurations in multiple Test Configurations (TCs). Experimental results show that for ISCAS89 and ITC99 benchmarks, this scheme can obtain a stuck-at fault coverage higher than 99% in less than 11 TCs.

An R-peak detection method with a high noise tolerance is presented in this paper. This method utilizes a customized deep convolution neural network (DCNN) to extract morphological and temporal features from sliced electrocardiogram (ECG) signals. The proposed network adopts multiple parallel dilated convolution layers to analyze features from diverse fields of view. A sliding window slices the original ECG signals into segments, and then the network calculates one segment at a time and outputs every point's probability of belonging to the R-peak regions. After a binarization and a deburring operation, the occurrence time of the R-peaks can be located. Experimental results based on the MIT-BIH database show that the R-peak detection accuracies can be significantly improved under high intensity of the electrode motion artifact or muscle artifact noise, which reveals a higher performance than state-of-the-art methods.

In this paper, we propose one low-computation cycle and power-efficient recursive discrete Fourier transform (DFT)/inverse DFT (IDFT) architecture adopting a hybrid of input strength reduction, the Chebyshev polynomial, and register-splitting schemes. Comparing with the existing recursive DFT/IDFT architectures, the proposed recursive architecture achieves a reduction in computation-cycle by half. Appling this novel low-computation cycle architecture, we could double the throughput rate and the channel density without increasing the operating frequency for the dual tone multi-frequency (DTMF) detector in the high channel density voice over packet (VoP) application. From the chip implementation results, the proposed architecture is capable of processing over 128 channels and each channel consumes 9.77 µW under 1.2 V@20 MHz in TSMC 0.13 1P8M CMOS process. The proposed VLSI implementation shows the power-efficient advantage by the low-computation cycle architecture.

In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.

In conventional preamble based channel estimation in OFDM/offset QAM (OFDM/OQAM) system, both the even index subcarriers and the odd index subcarriers are with identical value selected from { 1 } respectively to avoid inter-carrier interference (ICI), if and only if channel frequency response in neighbor few subcarriers remain invariable. However, it requires larger coherent bandwidth. In this paper, we propose an effective preamble design with ICI cancellation for channel estimation in OFDM/OQAM system. With this structure, we only utilize even (or odd) index of subcarriers as reference signal to avoid ICI, and then the channel information of remaining subcarriers can be estimated by the interpolation approach. Based on the sampling theorem, the mean square error (MSE) performance of the proposed preamble design is analyzed, where channel estimation performance is same for all subcarriers. Simulation and analytical results demonstrate that the proposed preamble design with ICI cancellation method outperforms the conventional one in term of channel estimation accuracy in OFDM/OQAM system.