This paper attempts to solve the security problems in communication, consensus-making and authentication of nodes in the Internet of vehicles (IoV) for intelligent transport. Considering the defects of the central node and service complexity in the IoV, the blockchain was integrated with the IoV to create a decentralized mechanism for communication and consensus-making. In the architecture of the blockchain-based IoV, the Byzantine consensus algorithm based on time sequence and gossip protocol is used to complete information communication and consensus authentication, which not only ensures communication security, improves the consensus efficiency of nodes, but also improves the fault tolerance of the algorithm. The experimental results show that our algorithm outshined the traditional authentication method in information security and consensus efficiency of the IoV. The research findings provide a reference solution to the authentication problems in the IoV for intelligent transport.INDEX TERMS Blockchain, consensus algorithm, intelligent transport, Internet of vehicles (IoV).

Oxygen level, including blood oxygen saturation (sO 2 ) and tissue oxygen partial pressure (pO 2 ), are crucial physiological parameters in life science. This paper reviews the importance of these two parameters and the detection methods for them, focusing on the application of photoacoustic imaging in this scenario. sO 2 is traditionally detected with optical spectra-based methods, and has recently been proven uniquely efficient by using photoacoustic methods. pO 2 , on the other hand, is typically detected by PET, MRI, or pure optical approaches, yet with limited spatial resolution, imaging frame rate, or penetration depth. Great potential has also been demonstrated by employing photoacoustic imaging to overcome the existing limitations of the aforementioned techniques.

Light scattering inside disordered media poses a significant challenge to achieve deep depth and high resolution simultaneously in biomedical optical imaging. Wavefront shaping emerged recently as one of the most potential methods to tackle this problem. So far, numerous algorithms have been reported, while each has its own pros and cons. In this article, we exploit a new thought that one algorithm can be reinforced by another complementary algorithm since they effectively compensate each other’s weaknesses, resulting in a more efficient hybrid algorithm. Herein, we introduce a systematical approach named GeneNN (Genetic Neural Network) as a proof of concept. Preliminary light focusing has been achieved by a deep neural network, whose results are fed to a genetic algorithm as an initial condition. The genetic algorithm furthers the optimization, evolving to converge into the global optimum. Experimental results demonstrate that with the proposed GeneNN, optimization speed is almost doubled and wavefront shaping performance can be improved up to 40% over conventional methods. The reinforced hybrid algorithm shows great potential in facilitating various biomedical and optical imaging techniques.

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.

Abstract:Since its introduction to the field in mid-1990s, photoacoustic imaging has become a fast-developing biomedical imaging modality with many promising potentials. By converting absorbed diffused light energy into not-so-diffused ultrasonic waves, the reconstruction of the ultrasonic waves from the targeted area in photoacoustic imaging leads to a high-contrast sensing of optical absorption with ultrasonic resolution in deep tissue, overcoming the optical diffusion limit from the signal detection perspective. The generation of photoacoustic signals, however, is still throttled by the attenuation of photon flux due to the strong diffusion effect of light in tissue. Recently, optical wavefront shaping has demonstrated that multiply scattered light could be manipulated so as to refocus inside a complex medium, opening up new hope to tackle the fundamental limitation. In this paper, the principle and recent development of photoacoustic imaging and optical wavefront shaping are briefly introduced. Then we describe how photoacoustic signals can be used as a guide star for in-tissue optical focusing, and how such focusing can be exploited for further enhancing photoacoustic imaging in terms of sensitivity and penetration depth. Finally, the existing challenges and further directions towards in vivo applications are discussed.

Optical focusing and imaging through or inside scattering media, such multimode fiber and biological tissues, has significant impact in biomedicine yet considered challenging due to strong scattering nature of light. In the past decade, promising progress has been made in the field, largely benefiting from the invention of iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing and hence imaging becomes possible. Most of reported iterative algorithms can overcome small perturbations on the noise level but fail to effectively adapt beyond the noise level, e.g. sudden strong perturbations. Re-optimizations are usually needed for significant decorrelation to the medium since these algorithms heavily rely on the optimization performance in the previous iterations. Such ineffectiveness is probably due to the absence of a metric that can gauge the deviation of the instant wavefront from the optimum compensation based on the concurrently measured optical focusing. In this study, a square rule of binary-amplitude modulation, directly relating the measured focusing performance with the error in the optimized wavefront, is theoretically proved and experimentally validated. With this simple rule, it is feasible to quantify how many pixels on the spatial light modulator incorrectly modulate the wavefront for the instant status of the medium or the whole system. As an example of application, we propose a novel algorithm, dynamic mutation algorithm (DMA), which has high adaptability against perturbations by probing how far the optimization has gone toward the theoretically optimal performance. The diminished focus of scattered light can be effectively recovered when perturbations to the medium cause significant drop of the focusing performance, which no existing algorithms can achieve due to their inherent strong dependence on previous optimizations. With further improvement, the square rule and the new algorithm may boost or inspire many applications, such as high-resolution optical imaging and stimulation, in instable or dynamic scattering environments.

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The proliferation of electric vehicles and active distribution network has brought many uncertainties to the power system. If the power system involves battery-swap stations of electric vehicles, it is difficult to ensure the data security during the distributed scheduling. To solve the problem, this paper sets up a collaborative optimization model for distributed scheduling based on blockchain consensus mechanism, considering the battery-swap stations. The power system was divided into three levels: the transmission network level, the distribution network level and the battery-swap station level. Next, the objective functions were constructed to minimize the generation cost and daily load variance on each level, and the optimal scheduling plan for the power system was solved through multi-level collaborative optimization. The blockchain consensus mechanism was adopted to verify the accuracy of the transaction data, and the production data of all entities were encoded by hash function before storage, such that the data are tamper resistant and traceable. The example analysis shows that our model can effectively reduce the generation cost, lower the daily load variance, and enhance system stability. The research findings shed new light on maintaining the optimization efficiency and data confidentiality of modern power network.