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 1.9GHz film bulk acoustic resonator (FBAR)-based low-phase-noise complementary cross-coupled voltage-controlled oscillator (VCO) is presented. The FBAR-VCO is designed and fabricated in 0.18µm CMOS process. The DC latch and the low frequency instability are resolved by employing the NMOS source coupling capacitor and the DC blocked cross-coupled pairs. Since no additional voltage headroom is required, the proposed FBAR-VCO can be operated at a low power supply voltage of 1.1V with a wide voltage swing of 0.9V. An effective phase noise optimization is realized by a reasonable trade-off between the output resistance and the trans-conductance of the cross-coupled pairs. The measured performance shows the proposed FBAR-VCO achieves a phase noise of -148dBc/Hz at 1MHz offset with a figure of merit (FoM) of -211.6dB.

Due to the complicated composition of cloud and its disordered transformation, the rendering of cloud does not perfectly meet actual prospect by current methods. Based on physical characteristics of cloud, a physical cellular automata model of Dynamic cloud is designed according to intrinsic factor of cloud, which describes the rules of hydro-movement, deposition and accumulation and diffusion. Then a parallel computing architecture is designed to compute the large-scale data set required by the rendering of dynamical cloud, and a GPU-based ray-casting algorithm is implemented to render the cloud volume data. The experiment shows that cloud rendering method based on physical cellular automata model is very efficient and able to adequately exhibit the detail of cloud.

It is very difficult to know evolution state of ACO in its working. To solve the problem, we propose using colony entropy and mean colony entropy to monitor the algorithm. The two functions show fluctuation and declining trends depended on time t in a tour and iteration number. According to the principle, that each updated edge will get the same increment is improper. Then a weighted algorithm is proposed to calculate each arc's increment based on its selected probability. The strategy can provide more exploration to help to find the global optimum value, and experiments show its improved performance.

One typical phonotactic system for language recognition is parallel phone recognition followed by vector space modeling (PPRVSM). In this system, various phone recognizers are applied in parallel and fused at the score level. Each phone recognizer is trained for a known language, which is assumed to extract complementary information for effective fusion. But this method is limited by the large amount of training samples for which word or phone level transcription is required. Also, score fusion is not the optimal method as fusion at the feature or model level will retain more information than at the score level. This paper presents a new strategy to build and fuse parallel phone recognizers (PPR). This is achieved by training multiple acoustic diversified phone recognizers and fusing at the feature level. The phone recognizers are trained on the same speech data but using different acoustic features and model training techniques. For the acoustic features, Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) are both employed. In addition, a new time-frequency cepstrum (TFC) feature is proposed to extract complementary acoustic information. For the model training, we examine the use of the maximum likelihood and feature minimum phone error methods to train complementary acoustic models. In this study, we fuse phonotactic features of the acoustic diversified phone recognizers using a simple linear fusion method to build the PPRVSM system. A novel logistic regression optimized weighting (LROW) approach is introduced for fusion factor optimization. The experimental results show that fusion at the feature level is more effective than at the score level. And the proposed system is competitive with the traditional PPRVSM. Finally, the two systems are combined for further improvement. The best performing system reported in this paper achieves an equal error rate (EER) of 1.24%, 4.98% and 14.96% on the NIST 2007 LRE 30-second, 10-second and 3-second evaluation databases, respectively, for the closed-set test condition.

The approximation expression about error accumulation of a long-term prediction is derived. By analyzing this formula, we find that the factors that can affect the long-term predictability include the model parameters, prediction errors and the derivates of the used basis functions. To enlarge the maximum attempting time, we present that more suitable basis functions should be those with smaller derivative functions and a fast attenuation where out of the time series range. We compare the long-term predictability of a non-polynomial based algorithm and a polynomial one to prove the success of our method.

A differential on chip oscillator (OCO) is proposed in this paper for low supply voltage, high frequency accuracy and fast startup. The differential architecture helps the OCO achieve a good power supply rejection ratio (PSRR) without using a regulator so as to make the OCO suitable for a low power supply voltage of 1.38V. A reference voltage generator is also developed to generate two output voltages lower than Vbe for low supply voltage operation. The output frequency is locked to 48MHz by a frequency-locked loop (FLL) and a 3.3-ppm/°C temperature coefficient of frequency is realized by the differential voltage ratio adjusting (differential VRA) technique. The startup time is only 1.47μs because the differential OCO is not necessary to charge a big capacitor for ripple reduction.

In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.