We propose a novel bit error rate (BER) analysis model of weighted-type fractional Fourier transform (WFRFT)-based systems with WFRFT order offset Δα. By using the traditional BPSK BER analysis method, we deduce the equivalent signal noise ratio (SNR), model the interference in the channel as a Gaussian noise with non-zero mean, and provide a theoretical BER expression of the proposed system. Simulation results show that its theoretical BER performance well matches the empirical performance, which demonstrates that the theoretical BER analysis proposed in this paper is reliable.

Brain subsystems have a high degree of information processing ability using nonlinear dynamics and although various neuron models and artificial neural networks have been investigated, the information processing functions of biological neural networks have not yet been clarified. Recently, various research efforts have confirmed that dendrites perform an important role in brain information processing. In this paper, we discuss the nonlinear characteristics of a hardware active dendrite model, in order to clarify information encoding and transmission via action potentials. That is to say, we show that our proposed model can reproduce the nonlinear characteristics of a biologically active dendrite. First, the hardware active dendrite model we propose is described. We next discuss the response characteristics for pulse stimuli using the model. As a result, when input pulses are applied to an active line, which is the basic structure of the dendrite model, it is shown clearly that backpropagation characteristics are acquired and that the characteristics are qualitatively in agreement with the characteristics of biological dendrites. Furthermore, we verify that the ratio of input to output frequency at the cell body is influenced by the backpropagation characteristics with two branches, which is the simplest structure in the active dendrite model. Thus, with backpropagation characteristics, the possibility that the model can carry out clearly the information processing of biological neural networks, is suggested.

Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.

Very few existing works about inertial sensor based air-writing focused on writing constraints' effects on recognition performance. We proposed a LSTM-based system and made several quantitative analyses under different constraints settings against CHMM, DTW-AP and CNN. The proposed system shows its advantages in accuracy, real-time performance and flexibility.

We present a novel model named Integrated Latent Topic Model (ILTM), to learn and recognize natural scene category. Unlike previous work, which considered the discrepancy and common property separately among all categories, Our approach combines universal topics from all categories with specific topics from each category. As a result, the model is implemented to produce a few but specific topics and more generic topics among categories, and each category is represented in a different topics simplex, which correlates well with human scene understanding. We investigate the classification performance with variable scene category tasks. The experiments have shown our model outperforms latent-space methods with less training data.

To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.

Detecting small infrared targets is a difficult but important task in highly cluttered coastal surveillance. The paper proposed a method called low-rank and sparse decomposition based frame difference to improve the detection performance of a surveillance system. First, the frame difference is used in adjacent frames to detect the candidate object regions which we are most interested in. Then we further exclude clutters by low-rank and sparse matrix recovery. Finally, the targets are extracted from the recovered target component by a local self-adaptive threshold. The experiment results show that, the method could effectively enhance the system's signal-to-clutter ratio gain and background suppression factor, and precisely extract target in highly cluttered coastal scene.

In this paper, a scramble phase assisting weighted-type fractional Fourier transform (SPA-WFRFT) based system is proposed to guarantee the communication's security. The original transmitting signal is divided into two parts. The first part is modulated by WFRFT and subsequently makes up the constellation beguiling. The other part is used to generate the scramble phase and also to assist in the encryption of the WFRFT modulated signal dynamically. The novel constellation optimal model is built and solved through the genetic algorithm (GA) for the constellation beguiling. And the double pseudo scheme is implemented for the scramble phase generation. Theoretical analyses show that excellent security performances and high spectral efficiency can be attained. Final simulations are carried out to evaluate the performances of the SPA-WFRFT based system, and demonstrate that the proposed system can effectively degrade the unauthorized receivers' bit error rate (BER) performance while maintaining its own communication quality.

The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

A primary-side regulation AC--DC constant voltage control chip is designed, which employs a novel cable compensation technique to improve the precision of the output voltage and pursue a wider load range for regulation. In the proposed controller, constant voltage (CV) is achieved by OSC charging current and current-limiting point adjustment. Meantime, according to different cable lengths, the sampled voltage is regulated by injecting current to pull-down resistance of the system to obtain an accurate output voltage. The proposed chip is implemented in TSMC 0.35,$mu $m 5,V/40,V BCD process, and a 12,V/1,A circuit prototype has been built to verify the proposed control method. Experimental results show that the maximum cable compensation current reaches 43,$mu $A, and the precision of the output voltage is within $pm$ 3% in a wide range of output current from 0 to 1,A.

In this paper, we present an MPEG-4 decoding scheme based on reversible variable length code. The scheme is purely decoder based and compliance with the standard is fully maintained. Moreover, the data recovery scheme suggested in MPEG-4 can still be used as the default scheme. Simulation results show that the proposed scheme achieves better data recovery, both in terms of PSNR and perceptual quality, from error propagation region of a corrupted video packet, as compared to existing MPEG-4 scheme.

It is known that Boolean functions used in stream ciphers should have good cryptographic properties to resist fast algebraic attacks. In this paper, we study a new class of Boolean functions with good cryptographic properties: balancedness, optimum algebraic degree, optimum algebraic immunity and a high nonlinearity.