Research on swarming has primarily focused on applying swarming behavior with physics-derived or ad-hoc models to tasks requiring collective intelligence in robotics and optimization. In contrast, applications in signal processing are still lacking. The purpose of this paper is to investigate the use of biologically-inspired swarm methods for signal filtering. The signal, in the case of images ...

Swarm is a storage system that provides scalable, reliable, and cost-effective data storage. Swarm is based on storage servers, rather than file servers; the storage servers are optimized for cost-performance and aggregated to provide high-performance data access. Swarm uses a striped log abstraction to store data on the storage servers. This abstraction simplifies storage allocation, improves ...

In this paper a general Morse potential model of self-propelling particles is considered in the presence of a time-delayed term and a spring potential. It is shown that the emergent swarm behavior is dependent on the delay term and weights of the time-delayed function, which can be set to induce a stationary swarm, a rotating swarm with uniform translation, and a rotating swarm with a stationar...

This paper explores the potential intersection of two fascinating and increasingly burgeoning fields: Swarm Intelligence and Educational Data Mining. A thorough review of the existing works in both fields has revealed a lack of Swarm Intelligence applications in Educational Data Mining, despite its successful applications in other data mining domain. Consequently, this paper launches the inters...

Error detection and recovery are important issues in swarm robotics research, as they are a means by which fault tolerance can be achieved. Our previous work has looked at error detection for single failures in a swarm robotics scenario with the Receptor Density Algorithm. Three modes of failure to the wheels of individual robots was investigated and comparable performance to other statistical ...

The work reported in this paper proposes Swarm-Array computing, a novel technique inspired by swarm robotics, and built on the foundations of autonomic and parallel computing. The approach aims to apply autonomic computing constructs to parallel computing systems and in effect achieve the self-ware objectives that describe self-managing systems. The constitution of swarm-array computing compris...

In the seismic inversion model, as the neural network algorithm there are some problems, the convergence bad, accuracy is not high. This paper presents a seismic inversion based on artificial fish Swarm Optimization neural network models. First to initialize fish mapping of chaos optimization and ergodicity of artificial fish-swarm search and adaptive strategies of artificial fish-swarm algorit...

In practical applications of robot swarms with bio-inspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm. In this pa...

Swarm intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. The intelligence emerges from a chaotic balance between individuality and sociality. The chaotic balances are a characteristic feature of the complex system. This ...

Swarm intelligence deals with natural and arti cial systems composed of many individuals that coordinate using decentralized control and self-organization [1, 2, 5]. The main focus of swarm intelligence research is on the collective behaviour that results from local interactions of individuals with each other and with their environment. There are many examples of natural systems that are studie...