Image watermarking is a key technology for copyright protection, and how to better balance the invisibility and robustness of algorithms is a challenge. To tackle this challenge, a watermarking optimization method based on matrix decomposition and discrete wavelet transform (DWT) for multi-size images is proposed. The DWT, Hessenberg matrix decomposition (HMD), singular value decomposition (SVD), particle swarm optimization (PSO), Arnold transform and logistic mapping are combined for the first time to achieve an image watermarking optimization algorithm. The multi-level decomposition of DWT is used to be adapted to multi-size host images, the Arnold transform, logistic mapping, HMD and SVD are used to enhance the security and robustness, and the PSO optimized scaling factor to balance invisibility and robustness. The simulation results of the proposed method show that the PSNRs are higher than 44.9 dB without attacks and the NCs are higher than 0.98 under various attacks. Compared with the existing works, the proposed method shows high robustness against various attacks, such as noise, filtering and JPEG compression and in particular, the NC values are at least 0.44% higher than that in noise attacks.

There is considerable interest in the development of ultra-miniature and low-power sensor microsystems for use in applications such as medical diagnostics, environmental monitoring and other industrial applications. Such ultra-miniature sensor microsystems must contain a large diversity of complex electronics, including sensor interfaces, signal conditioning, a microprocessor core, digital signal processing, and wireless transmission technology. In this paper, we will describe the first steps towards the development of a System on Chip for such a sensor microsystem and the methodology employed to build such a microsystems.

Electric heat storage (EHS) located on the grid side can improve the flexibility of combined heat and power (CHP) systems to reduce wind power curtailment (WPC), and its capacity is correlated with investment costs. However, a significant amount of distributed electric heat storage (DEHS) on the load side has already been funded and constructed by users and businesses. To effectively utilize DEHS for diverse user types in different geographic locations, a generalized scheduling method is required to address these issues. To fill the technology gap, an innovative optimal scheduling strategy for energy allocation in DEHS is proposed. Firstly, to consider the heat comfort of DEHS users and their willingness to serve grid scheduling, the equivalent energy allocation strategy (EEAS) and increased energy allocation strategy (IEAS) are developed, respectively. Secondly, a general model is proposed to schedule DEHS for different types of users. The electric heat time-shift characteristics of DEHS are used to reallocate the working time of energy storage. Thirdly, numerical simulations are employed to validate the proposed strategy. The proposed scheduling strategies of EEAS and IEAS contribute to the reduction of WPC. The WPC rates are reduced by 14.04% and 29.49%, respectively, when compared to the conventional scheduling strategy of time-of-use. Moreover, compared with EEAS, IEAS makes a significant contribution by providing 27.05% more scheduling space for the grid, thus greatly improving its flexibility in consuming wind power. The WPC can be reduced through the energy allocation of DEHS, while the flexibility of grid regulation is enhanced by increasing the deviation of energy allocation.