Hierarchical Microswarms with Leader–Follower‐Like Structures: Electrohydrodynamic Self‐Organization and Multimode Collective Photoresponses

Swarming micro/nanomotors can self‐organize into cohesive groups to execute cooperative tasks. To date, research work has focused on the construction of egalitarian microswarms composed of similar individuals. The construction and collective behaviors of hierarchical leader–follower‐like microswarms are demonstrated. By inducing converging electrohydrodynamic flows under an AC electric field, dielectric microparticles with different sizes and dielectric properties can hierarchically self‐organize into leader–follower‐like microswarms under attractive electrohydrodynamic interactions, and show novel emergent collective behaviors. First, different from immobile single constituents or egalitarian clusters, the hierarchical microswarms autonomously move with tunable speed. Second, they exhibit multimode collective photoresponses emerging from different behaviors of the constituents in response to light signals. With a vertical UV signal, the photoresponsive followers tend to surround the leader and stop the microswarm. In response to sidewise UV signals, the constituents with stronger phototaxis would migrate to the position away from light stimuli, and thus the microswarms reorient parallel/antiparallel to the light direction and perform collective positive/negative phototaxis. Due to differential roles and huge design spaces of constituents, the hierarchical microswarms are envisioned to possess merits of high‐efficiency, multiresponsiveness, and multifunctions, and may serve as intelligent micro/nanorobot systems for biomedicine and microengineering.     

无人机“蜂群”的蜂拥涌现行为识别与抑制机理

为了抵消无人机"蜂群"所具有的非对称作战优势，从反控制其协同飞行的角度出发，将"蜂群"描述为具有涌现性特征的复杂系统，剖析无人机"蜂群"蜂拥涌现行为的产生机理，首次建立基于f-散度的"蜂群"涌现性度量模型.根据蜂拥控制算法所遵循的启发式规则提出"蜂群"蜂拥涌现行为的抑制机理，首次建立了干扰条件下蜂拥控制的失效判别模型.通过仿真实验，分析和讨论了干扰强度、干扰时机对抑制"蜂群"蜂拥涌现行为的影响.     

Collective behavior coordination with predictive mechanisms

In natural flocks/swarms, it is very appealing that low-level individual intelligence and communication can yield advanced coordinated collective behaviors such as congregation, synchronization and migration. In the past few years, the discovery of collective flocking behaviors has stimulated much interest in the study of the underlying organizing principles of abundant natural groups, which has led to dramatic advances in this emerging and active research field. Inspired by previous investigations on the predictive intelligence of animals, insects and microorganisms, we seek in this article to understand the role of predictive mechanisms in the forming and evolving of flocks/swarms by using both numerical simulations and mathematical analyses. This article reviews some basic concepts, important progress, and significant results in the current studies of collective predictive mechanisms, with emphasis on their virtues concerning consensus improvement and communication cost reduction. Due to these advantages, such predictive mechanisms have great potential to find their way into industrial applications.     

Smart microrobots for mechanical cell characterization and cell convoying

This paper deals with the effective design of smart microrobots for both mechanical cell characterization and cell convoying for in vitro fertilization. The first microrobotic device was developed to evaluate oocyte mechanical behavior in order to sort oocytes. A multi-axial micro-force sensor based on a frictionless magnetic bearing was developed. The second microrobotic device presented is a cell convoying device consisting of a wireless micropusher based on magnetic actuation. As wireless capabilities are supported by this microrobotic system, no power supply connections to the micropusher are needed. Preliminary experiments have been performed regarding both cell transporting and biomechanical characterization capabilities under in vitro conditions on human oocytes so as to demonstrate the viability and effectiveness of the proposed setups.     

Luminescent Silicon Diatom Replicas: Self‐Reporting and Degradable Drug Carriers with Biologically Derived Shape for Sustained Delivery of Therapeutics

Current development of drug microcarriers is mainly based on spherical shapes, which are not biologically favorable geometries for complex interactions with biological systems. Scalable synthesis of drug carriers with nonspherical and anisotropic shapes featuring sustained drug‐releasing performances, biocompatibility, degradability, and sensing capabilities is challenging. These challenges are addressed in this work by employing Nature's optimized designs obtained from low‐cost diatomaceous earth mineral derived from single‐cell algae diatoms. Silica diatoms with unique shapes and 3D microcapsule morphology are converted into silicon diatom replicas with identical structure by a magnesiothermic reduction process. The results reveal that prepared silicon diatoms have a set of unique properties including favorable microcapsule structure with high surface area and micro/mesoporosity providing high drug loading, fast biodegradability, and intrinsic luminescence, which make them highly suitable for low‐cost production of advanced drug microcarriers. Their sustained drug release >30 days combined with self‐reporting function based on silicon luminescence properties using nonluminescent and luminescent drugs for intravitreal drug therapy is successfully demonstrated. These silicon diatoms offer promising potential toward scalable production of low‐cost and advanced microcarriers for broad medical therapies, including theranostics and microrobotic guided drug delivery devices.     

Temporal signal processing with high-speed hybrid analog-digital neural networks

There are many problems which fall into the class of temporal signal processing. These problems have in common the need to relate the temporal properties of their inputs. Conventional solutions to these problems often have high hardware overhead, complex algorithmic solutions, or loss of information through the transformation of temporal properties of the input. To this end, a biologically motivated artificial and neural processing element has been developed. As in biological neurons, processing is time dependent and is implemented using both analog and digital techniques. These characteristics make the PE directly applicable a large class of temporal signal processing problems typically encountered in engineering and science. Multiple aspects of the PE behavior are adjustable, which produces a very wide range of behaviors from simple systems with only a few moderately connected processing elements. The processing element models are custom designed electric circuits based on basic CMOS components and therefore all developed systems can be directly implemented in any standard integrated CMOS technology. The integrated implementation, custom design, and a wide range of adaptable behaviors join to produce a very fast, low-hardware solution to complex spatiotemporal signal processing problems. Seven novel systems based on the hybrid PE are discussed as they relate to commonly encountered temporal signal processing problems.     

面向群体快速融合的仿生无人机集群架构

摘 要：无人机集群作战是未来战场的重要组成部分。集群内的无人机通过协作，进行载荷的互补与任务协调，以提升系统整体性能。自然界中如蚂蚁、狼、鸽子、鱼等群体，其个体与单架无人机行为类似，而蚁群、狼群、鸽群又能依靠个体间的规则，通过协调与组织实现更为复杂的行为。不同生物集群展现的能力不同，在战场上能承担的任务也各不相同。从生物集群的群体特征出发，考虑生物间的拓扑和交流机制，提出了一种仿生无人机集群的融合架构。不同种类的仿生无人机集群自由地融合，增强了无人机集群的异构性，提高仿生无人机集群架构的协调能力。     

3D‐Printed Microrobotic Transporters with Recapitulated Stem Cell Niche for Programmable and Active Cell Delivery

Poor retention rate, low targeting accuracy, and spontaneous transformation of stem cells present major clinical barriers to the success of therapies based on stem cell transplantation. To improve the clinical outcome, efforts should focus on the active delivery of stem cells to the target tissue site within a controlled environment, increasing survival, and fate for effective tissue regeneration. Here, a remotely steerable microrobotic cell transporter is presented with a biophysically and biochemically recapitulated stem cell niche for directing stem cells towards a pre‐destined cell lineage. The magnetically actuated double‐helical cell microtransporters of 76 µm length and 20 µm inner cavity diameter are 3D printed where biological and mechanical information regarding the stem cell niche are encoded at the single‐cell level. Cell‐loaded microtransporters are mobilized inside confined microchannels along computer‐controlled trajectories under rotating magnetic fields. The mesenchymal stem cells are shown retaining their differentiation capacities to commit to the osteogenic lineage when stimulated inside the microswimmers in vitro. Such a microrobotic approach has the potential to enable the development of active microcarriers with embedded functionalities for controlled and precisely localized therapeutic cell delivery.     

Advances in Chemically Powered Micro/Nanorobots for Biological Applications: A Review

Micro/nanorobots (MNRs) are capable of autonomous motion, breaking through the limitations of traditional passive transport of nanocarriers. Among them, chemically driven MNRs are the earliest MNRs studied and have received extensive attention from researchers. This review first focuses on the material properties, preparation, driving forms, and mechanisms of chemically driven MNRs. The current status of research on chemically driven MNRs in biomedicine is summarized for various biological applications (drug delivery, diagnostics, anti-inflammatory, antibacterial, and disease treatment). In terms of biosafety, possible safety issues are analyzed in the context of chemically driven microrobotic applications in terms of three aspects: component characteristics, chemical engines and biological environment. Finally, the challenges and possible future directions of chemically driven MNRs are presented.     

A message keyword extraction approach by accurate identification of field boundaries

With the recent exponential increase in internet speeds, the traditional network environment is evolving into a high-capacity network environment. Network traffic usage is also increasing exponentially, as are new malicious behaviors and related applications. Most of these applications and malicious behaviors use unknown protocols for which the structure is inaccessible; hence, protocol reverse engineering is receiving increasing attention in the field of network management. Various approaches have been proposed, but they still suffer from misidentification of field boundaries. To understand message structures properly, it is important to identify accurately the boundaries of the fields constituting the protocol message; accurate keyword extraction based on this approach leads to the correct inference of message types, semantics, and state machine. In this study, we propose a message keyword extraction method using accurate identification of field boundaries from delimiter inference and statistical analysis. Through the identification of field boundaries, messages can be subdivided into fields. We evaluate the efficacy of the proposed method by applying it to several textual and binary protocols. The proposed method showed better results than did other previous studies for both textual and binary protocols.     

Photosynthetic Biohybrid Nanoswimmers System to Alleviate Tumor Hypoxia for FL/PA/MR Imaging‐Guided Enhanced Radio‐Photodynamic Synergetic Therapy

Biohybrid microswimmers have recently shown to be able to actively perform in targeted delivery and in vitro biomedical applications. However, more envisioned functionalities of the microswimmers aimed at in vivo treatments are still challenging. A photosynthetic biohybrid nanoswimmers system (PBNs), magnetic engineered bacteria‐Spirulina platensis, is utilized for tumor‐targeted imaging and therapy. The engineered PBNs is fabricated by superparamagnetic magnetite (Fe₃O₄ NPs) via a dip‐coating process, enabling its tumor targeting ability and magnetic resonance imaging property after intravenous injection. It is found that the PBNs can be used as oxygenerator for in situ O₂ generations in hypoxic solid tumors through photosynthesis, modulating the tumor microenvironment (TME), thus improving the effectiveness of radiotherapy (RT). Furthermore, the innate chlorophyll released from the RT‐treated PBNs, as a photosensitizer, can produce cytotoxic reactive oxygen species under laser irradiation to achieve photodynamic therapy. Excellent tumor inhibition can be realized by the combined multimodal therapies. The PBNs also possesses capacities of chlorophyll‐based fluorescence and photoacoustic imaging, which can monitor the tumor therapy and tumor TME environment. These intriguing properties of the PBNs provide a promising microrobotic platform for TME hypoxic modulation and cancer theranostic applications.     

Light‐Driven and Light‐Guided Microswimmers

Light‐driven swimming particles hold great potential in wide applications ranging from next‐generation drug delivery to versatile microrobotic devices. It is desired that the self‐propelled microparticles should swim not only autonomously but also directionally to achieve their goals in their potential applications. This paper presents the first example of fully organic colloidal particles of a spiropyran‐terminated hyperbranched polymer that can be driven and meanwhile steered by a UV light source, swimming straight towards the UV source. The mean‐square velocities of the photochromic suspension particles are about 20 μm s^?1, and increase to about 54 μm s^?1 with the addition of NaCl of 0.5%. The phototactic propulsion is supposed to be originated from the UV irradiation‐induced interfacial tension gradient on the surface of the colloidal particles. This finding allows for the design of new microengines for next‐generation drug delivery systems, microrobotic devices, and self‐adaptive photocatalysts, etc.     

Synthetic organisms and self-designing systems

This paper examines the need for complex, adaptive solutions to certain types of complex problems typified by the Strategic Defense System and NASA's Space Station and Mars Rover. Since natural systems have evolved with capabilities of intelligent behavior in complex, dynamic situations, it is proposed that biological principles be identified and abstracted for application to certain problems now facing industry, defense, and space exploration. Two classes of artificial neural networks are presented — a nonadaptive network used as a genetically determined “retina,” and a frequency-coded network used as an adaptive “brain.” The role of a specific environment coupled with a system of artificial neural networks having simulated sensors and effectors is seen as an ecosystem. Evolution of synthetic organisms within this ecosystem provides a powerful optimization methodology for creating intelligent systems able to function successfully in any desired environment. A complex software system involving a simulation of an environment and a program designed to cope with that environment are presented. Reliance on adaptive systems, as found in nature, is only part of the proposed answer, though an essential one. The second part of the proposed method makes use of an additional biological metaphor—that of natural selection—to solve the dynamic optimization problems every intelligent system eventually faces. A third area of concern in developing an adaptive, intelligent system is that of real-time computing. It is recognized that many of the problems now being explored in this area have their parallels in biological organisms, and many of the performance issues facing artificial neural networks may find resolution in the methodology of real-time computing.     

On the behavior-based architectures of autonomous agency

A number of autonomous robots with varying degrees of reactive functionality have been built, based on different architectures. We review the foundations, limitations, and achievements of a number of architectures of such autonomous agents from the three categories: (1) reactive; (2) deliberative; and (3) hybrid. Most of these architectures contain behaviors. The principle of avoiding an explicit representation of goals in the purely behavior-based robots has limited their achievements to simple tasks like box pushing, pipe inspection, and navigation. This paper makes two contributions: (1) reviewing autonomous agent architectures and (2) proposing a new class of architectures where behaviors are coupled and/or markers are introduced in environment, without a planner or sequencer and without an explicit representation of goals and investigating tradeoffs in these architectures. We develop a model of behaviors, environmental modification and goals and then show how the behavior-based robots can be made goal-directed. The tradeoffs in increasing their goal directedness are examined. Defining the notion of coupling that captures dependency within the internal structure of a behavior space, it is shown that more complex goals demand higher coupling or more behaviors or a modification to the environment. These novel tradeoffs show a new spectrum of architectures for integrating goals and the behavior-based reactive functionality.     

Light Controlled Biohybrid Microbots

Biohybrid microbots integrate biological actuators and sensors into synthetic chassis with the aim of providing the building blocks of next-generation micro-robotics. One of the main challenges is the development of self-assembled systems with consistent behavior and such that they can be controlled independently to perform complex tasks. Herein, it is shown that, using light-driven bacteria as propellers, 3D printed microbots can be steered by unbalancing light intensity over different microbot parts. An optimal feedback loop is designed in which a central computer projects onto each microbot a tailor-made light pattern, calculated from its position and orientation. In this way, multiple microbots can be independently guided through a series of spatially distributed checkpoints. By exploiting a natural light-driven proton pump, these bio-hybrid microbots are able to extract mechanical energy from light with such high efficiency that, in principle, hundreds of these systems can be controlled simultaneously with a total optical power of just a few milliwatts.     

Ad Hoc Multicast Routing Algorithm with Swarm Intelligence

Swarm intelligence refers to complex behaviors that arise from very simple individual behaviors and interactions, which is often observed in nature, especially among social insects such as ants. Although each individual (an ant) has little intelligence and simply follows basic rules using local information obtained from the environment, such as ant's pheromone trail laying and following behavior, globally optimized behaviors, such as finding a shortest path, emerge when they work collectively as a group. In this paper, we apply this biologically inspired metaphor to the multicast routing problem in mobile ad hoc networks. Our proposed multicast protocol adapts a core-based approach which establishes multicast connectivity among members through a designated node (core). An initial multicast connection can be rapidly setup by having the core flood the network with an announcement so that nodes on the reverse paths to the core will be requested by group members to serve as forwarding nodes. In addition, each member who is not the core periodically deploys a small packet that behaves like an ant to opportunistically explore different paths to the core. This exploration mechanism enables the protocol to discover new forwarding nodes that yield lower total forwarding costs, where cost is abstract and can be used to represent any metric to suit the application. Simulations have been conducted to demonstrate the performance of the proposed approach and to compare it with certain existing multicast protocols.     

Improving multiple pedestrians tracking with semantic information

This work presents an interacting multiple pedestrian tracking method for monocular systems that incorporates a prior knowledge about the environment and about interactions between targets. Pedestrian motion being ruled by both environment and social aspects, we model these complex behaviors by considering four cases of motion: going straight, finding one’s way, walking around and standing still. They are combined within an interacting multiple model particle filter strategy. We model targets interactions with social forces included as potential functions in the weighting process of the particle filter. We use different social force setups within each motion model to handle high-level behaviors (collision avoidance, flocking...). We evaluate our algorithm on challenging datasets and show that such semantic information improves the tracker performance compared to the literature.     

Associative Memory for Image Recovery with a High‐Performance Memristor Array

Associative memory is one of the significant characteristics of the biological brain. However, it has yet to be realized in a large memristor array due to the high requirements on the memristor device. In this work, the multilevel memristor cell is optimized by employing an electro‐thermal modulation layer. Memristor devices show both high resistance, cell‐to‐cell uniformity, and multilevel resistive switching behaviors with good reliability. A Hopfield neural network is experimentally demonstrated on a 1k memristor array that is capable of realizing the associative memory function for emotion image recovery. By using both asynchronous and synchronous refresh schemes, complete emotion images can be recalled from partial information.     

计算光学成像在散射中的应用

自然界中普遍存在光散射现象。如何通过散射介质实现高分辨率成像是光学成像领域亟待解决的重要问题。在早期研究中,多重光散射被认为是雾霾、云层、生物组织等复杂介质成像中的障碍。然而,最近研究表明,散射并不是成像的基本限制:光子在经过多次散射后仍然包含了大量信息。为了深入了解新兴的计算光学成像是如何解决多重光散射问题的,文中主要介绍了波前整形、散斑相关及深度学习等方法在散射成像领域中的研究进展。最新的研究成果表明:波前整形可以实现动态散射介质内部的高分辨率快速聚焦;散斑相关能够利用单帧散斑实现非侵入式成像;基于深度学习的成像技术能恢复出隐藏在光学厚度为13.4的白色聚苯乙烯平板背后的物体。