A blind symbol synchronization scheme for MIMO and Multi-User OFDM systems is proposed, which utilizes short-time Fourier Transformation (STFT) to obtain 2D (time and frequency) timing information from the received signals. By analyzing the obtained 2D time-frequency amplitude spectrum, intervals where no inter-symbol interference (ISI) exists are checked out for symbol synchronization, and samples during these intervals are used to carry out carrier frequency offset estimation. Theoretical analysis and simulation results show that the proposed method is more robust and provides more accurate carrier frequency offset estimation than traditional schemes.

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

A Verilog-AMS model of a fractional-N frequency synthesizer is presented that is capable of predicting spurious tones as well as noise and jitter performance. The model is based on a voltage-domain behavioral simulation. Simulation efficiency is improved by merging the voltage controlled oscillator (VCO) and the frequency divider. Due to the benefits of Verilog-AMS, the ΔΣ modulator which is incorporated in the synthesizer is modeled in a fully digital way. This makes it accurate enough to evaluate how the performance of the frequency synthesizer is affected by cyclic behavior in the ΔΣ modulator. The spur-minimizing effect of an odd initial condition on the first accumulator of the ΔΣ modulator is verified. Sequence length control and its effect on the fractional-N frequency synthesizer are also discussed. The simulated results are in agreement with prior published data on fractional-N synthesizers and with new measurement results.

Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.

Orthogonal frequency division multiplexing (OFDM) is very sensitive to the frequency errors caused by phase noise and Doppler shift. These errors will disturb the orthogonality among subcarriers and cause intercarrier interference (ICI). A simple method to combat ICI is proposed in this letter. The main idea is to map each data symbol onto a couple of subcarriers rather to a single subcarrier. Different from the conventional adjacent coupling and symmetric coupling methods, the frequency diversity can be utilized more efficiently by the proposed adaptive coupling method based on optimal subcarrier spacing. Numerical results show that our proposed method provides a robust signal-to-noise ratio (SNR) improvement over the conventional coupling methods.

Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.

A simple DFT-based noise variance estimator for orthogonal frequency division multiplexing access (OFDMA) systems is proposed. The conventional DFT-based estimator differentiates the channel impulse response and noise in the time domain. However, for partial frequency response, its time domain signal will leak to all taps due to the windowing effect. The noise and channel leakage power become mixed. In order to accurately derive the noise power, we propose a novel symmetric extension method to reduce the channel leakage power. This method is based on the improved signal continuity at the boundaries introduced by symmetric extension. Numerical results show that the normalized mean square error (NMSE) of our proposed method is significantly lower than that of the conventional DFT method.

A highly parallel turbo decoder for 3GPP LTE/LTE-Advanced systems is presented. It consists of 32 radix-4 soft-in/soft-out (SISO) decoders. Each SISO decoder is based on the proposed full-parallel sliding window (SW) schedule. Implemented in a 0.13 µm CMOS technology, the proposed design occupies 12.96 mm2 and achieves 1.5 Gb/s while decoding size-6144 blocks with 5.5 iterations. Compared with conventional SW schedule, the throughput is improved by 30–76% with 19.2% area overhead and negligible energy overhead.

An indirectly reactive sputtering coater has been developed to deposit various high quality metallic and metal oxide films at high deposition rate. In this letter, several kinds of filters such as antireflection (AR) coating, IR-cut filter, and Rugate filter were deposited for the benchmark test of implemental capabilities. Our coater was established to be a powerful tool for both discrete multilayer and Rugate filters due to high stability and reproducibility of the refractive index and the deposition rate.