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Nano/microswimmers represent the persistent endeavors of generations of scientists towards the ultimate tiny machinery for device manufacturing, targeted drug delivery, and noninvasive surgery. In many of these envisioned applications, multiple microswimmers need to be controlled independently and work cooperatively to perform a complex task. However, this multiple channel actuation remains a challenge as the controlling signal, usually a magnetic or electric field, is applied globally over all microswimmers, which makes it difficult to decouple the responses of multiple microswimmers. Here, we demonstrate that a photoelectrochemically driven nanotree microswimmer can be easily coded with a distinct spectral response by loading it with dyes. By using different dyes, an individual microswimmer can be controlled and navigated independently of other microswimmers in a group. This development demonstrates the excellent flexibility of the light navigation method and paves the way for the development of more functional nanobots for applications that require high-level controllability.

a b s t r a c tImage restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In the literature, however, it is also well known for producing staircase artifacts. In this work we extend the total variation with overlapping group sparsity, which we previously developed for one dimension signal processing, to image restoration. A convex cost function is given and an efficient algorithm is proposed for solving the corresponding minimization problem. In the experiments, we compare our method with several state-of-the-art methods. The results illustrate the efficiency and effectiveness of the proposed method in terms of PSNR and computing time.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate the time synchronization and localization problems in underwater sensor networks, where more challenges are introduced because of the unique characteristics of the water environment. These challenges include long propagation delay and transmission delay, low bandwidth, energy constraint, mobility, etc. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm IMM (interactive multiple model) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

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The total variation (TV) regularization method is an effective method for image deblurring in preserving edges. However, the TV based solutions usually have some staircase effects. In order to alleviate the staircase effects, we propose a new model for restoring blurred images under impulse noise. The model consists of an ℓ1-fidelity term and a TV with overlapping group sparsity (OGS) regularization term. Moreover, we impose a box constraint to the proposed model for getting more accurate solutions. The solving algorithm for our model is under the framework of the alternating direction method of multipliers (ADMM). We use an inner loop which is nested inside the majorization minimization (MM) iteration for the subproblem of the proposed method. Compared with other TV-based methods, numerical results illustrate that the proposed method can significantly improve the restoration quality, both in terms of peak signal-to-noise ratio (PSNR) and relative error (ReE).

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