Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.

This paper studies the consensus control problem faced with three essential demands, namely, discrete control updating for each agent, discrete-time communications among neighboring agents, and the fully distributed fashion of the controller implementation without requiring any global information of the network topology. Note that the existing related results only meet one or two demands at most, which are essentially not applicable to this problem. In this paper, a novel dynamic triggering condition is first presented, which is independent of the relative information among neighboring agents, providing potential opportunities to conquer the difficulties caused by the interaction of the above-mentioned three demands. Based on the proposed triggering condition, a framework on fully distributed adaptive consensus protocol with discrete communication and control updating is established. The thorough analysis of the adaptive gain is made by introducing a virtual adaptive gain to generate the Lyapunov function candidate, which significantly facilitates the consensus verification. Finally, numerical examples and comparisons are provided to verify the effectiveness of the proposed adaptive dynamic event-triggered protocols.

Robots, particularly unmanned aerial vehicles (UAVs), offer significant advantages in challenging environments. Their application in searching and exploring underground areas has garnered considerable attention. However, subsurface environments present various challenges, such as a lack of localization signals, weak illumination, and severe electromagnetic interference, which make positioning, detecting, and communicating difficult for UAVs. The ability to address these challenges is crucial for successful underground UAV applications. In this article, we highlight high-performance perception and communication as key technologies for underground UAVs. We first summarize the current state of UAV perception and communication, and then analyse the supporting role of multi-source fusion perception technology and joint communication optimization methods for underground UAVs. We also evaluate existing subsurface UAV applications and discuss potential research challenges and future development directions to overcome these challenges in underground UAV application scenarios.

The key pillar of developing digital city is the ubiquitous sensing of people and the environment. Crowdsensing requires a large number of users to participate in the collection of sensing data, and these data may carry sensitive information, such as identity and location related to the users or sensing object. If this information is eavesdropped, intercepted, and leaked, this may seriously harm the interests of individuals, organizations, and even countries. Therefore, from a privacy perspective, users may be reluctant to open data. While relying on mobile devices used by a large number of ordinary users as the basic sensing unit, it is necessary to include a variety of communication methods to realize the distribution of sensing tasks and to collect the sensing data. Then, to complete the complex crowdsensing tasks, it is important to ensure privacy security in the context of crowdsensing because it is a key problem. In this article, we comb through the development status of crowdsensing in the digital city, emphatically analyze the privacy protection in crowdsensing under the background of digital city, and qualitatively evaluate the existing privacy protection technologies for crowdsensing. Finally, this article presents research challenges and future directions that should be addressed to improve the performance of privacy protection technologies for crowdsensing systems.

We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns. The architecture represents a highly abstracted model of existing Spiking Neural Network (SNN) architectures. The proposed Optimized Deep Event-driven Spiking neural network Architecture (ODESA) can simultaneously learn hierarchical spatio-temporal features at multiple arbitrary time scales. ODESA performs online learning without the use of error back-propagation or the calculation of gradients. Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using an error measure. These adaptive selection thresholds are the central feature of ODESA, ensuring network stability and remarkable robustness to noise as well as to the selection of initial system parameters. Network activations are inherently sparse due to a hard Winner-Take-All (WTA) constraint at each layer. We evaluate the architecture on existing spatio-temporal datasets, including the spike-encoded IRIS, latency-coded MNIST, Oxford Spike pattern and TIDIGITS datasets, as well as a novel set of tasks based on International Morse Code that we created. These tests demonstrate the hierarchical spatio-temporal learning capabilities of ODESA. Through these tests, we demonstrate ODESA can optimally solve practical and highly challenging hierarchical spatio-temporal learning tasks with the minimum possible number of computing nodes.