To ensure the security of image transmission, this paper presents a new image encryption algorithm based on a genetic algorithm (GA) and a piecewise linear chaotic map (PWLCM), which adopts the classical diffusion-substitution architecture. The GA is used to identify and output the optimal encrypted image that has the highest entropy value, the lowest correlation coefficient among adjacent pixels and the strongest ability to resist differential attack. The PWLCM is used to scramble pixel positions and change pixel values. Experiments and analyses show that the new algorithm possesses a large key space and resists brute-force, statistical and differential attacks. Meanwhile, the comparative analysis also indicates the superiority of our proposed algorithm over a similar, recently published, algorithm.

A multi-source Tri-Training transfer learning algorithm is proposed by integrating transfer learning and semi-supervised learning. First, multiple weak classifiers are respectively trained by using both weighted source and target training samples. Then, based on the idea of co-training, each target testing sample is labeled by using trained weak classifiers and the sample with the same label is selected as the high-confidence sample to be added into the target training sample set. Finally, we can obtain a target domain classifier based on the updated target training samples. The above steps are iterated till the high-confidence samples selected at two successive iterations become the same. At each iteration, source training samples are tested by using the target domain classifier and the samples tested as correct continue with training, while the weights of samples tested as incorrect are lowered. Experimental results on text classification dataset have proven the effectiveness and superiority of the proposed algorithm.

Steganography aims to hide secret data into an innocuous cover-medium for transmission and to make the attacker cannot recognize the presence of secret data easily. Even the stego-medium is captured by the eavesdropper, the slight distortion is hard to be detected. The LSB-based data hiding is one of the steganographic methods, used to embed the secret data into the least significant bits of the pixel values in a cover image. In this paper, we propose an LSB-based scheme using reflected-Gray code, which can be applied to determine the embedded bit from secret information. Following the transforming rule, the LSBs of stego-image are not always equal to the secret bits and the experiment shows that the differences are up to almost 50 %. According to the mathematical deduction and experimental results, the proposed scheme has the same image quality and payload as the simple LSB substitution scheme. In fact, our proposed data hiding scheme in the case of G1 (one bit Gray code) system is equivalent to the simple LSB substitution scheme.

A new fast-lock, low-power digital delay-locked loop (DLL) is presented. A subranging searching algorithm is employed to effectively make the loop locked within only four clock cycles. A half-delay circuit is utilized to cut down power consumption. The prototype DLL in a standard 0.13-µm CMOS process operates in the range from 50 MHz to 400 MHz with four clock cycle lock time and consumes 2.379 mW with 1-V supply at 400 MHz clock rate. The measured RMS jitter and peak-to-peak jitter at 400 MHz are 1.586 ps and 16.67 ps, respectively. It occupies an active area of 0.038 mm2.

This paper considers a multi-user large-scale multiple-input multiple-output (MIMO) system with single cell working in full-duplex mode. Maximum ratio combining/maximum ratio transmission (MRC/MRT) is applied to maximize the output signal to noise ratio (SNR) of the receiver. Then we deduce the asymptotic uplink and downlink sum rate in full-duplex mode by using the large number theorem, also giving the comparison of traditional half-duplex and full-duplex. Besides, we analyze the influence of Doppler shift on the performance of the system. Finally, the change of the system performance on the user velocity is illustrated.

The efficiency of ubiquitous SIMD (Single Instruction Multiple Data) media processors is seriously limited by the bottleneck effect of the scalar kernels in media applications. To solve this problem, a dual-core framework, composed of a micro control unit and an instruction buffer, is proposed. This framework can dynamically decouple the scalar and vector pipelines of the original single-core SIMD architecture into two free-running cores. Thus, the bottleneck effect can be eliminated by effectively exploiting the parallelism between scalar and vector kernels. The dual-core framework achieves the best attributes of both single-core and dual-core SIMD architectures. Experimental results exhibit an average performance improvement of 33%, at an area overhead of 4.26%. What's more, with the increase of the SIMD width, higher performance gain and lower cost can be expected.

In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.

The poor capability of bandwidth management on the current CATV network hinders the promotion of multi-media streaming services. This paper proposes a solution by applying stream-code partition on the S-CDMA system adopted in the DOCSIS 2.0 standard. The method makes use of the Fine Granularity Scalability source coding and offers an efficient way for uplink rate control, so that bandwidth management can be performed in an extremely flexible manner.

Zero-shot learning refers to the object classification problem where no training samples are available for testing classes. For zero-shot learning, attribute transfer plays an important role in recognizing testing classes. One popular method is the indirect attribute prediction (IAP) model, which assumes that all attributes are independent and equally important for learning the zero-shot image classifier. However, a more practical assumption is that different attributes contribute unequally to the classifier learning. We therefore propose assigning different weights for the attributes based on the relevance probabilities between the attributes and the classes. We incorporate such weighed attributes to IAP and propose a relevance probability-based indirect attribute weighted prediction (RP-IAWP) model. Experiments on four popular attributed-based learning datasets show that, when compared with IAP and RFUA, the proposed RP-IAWP yields more accurate attribute prediction and zero-shot image classification.