Quantum communication for sender anonymity based on single-particle with collective detection

Nowadays, identity protection has turned into a fundamental demand for online activities. Currently, the present quantum anonymous communication protocols mostly rely on multi-entanglement. In this paper, we propose an anonymous communication protocol for anonymous sender by using single-particle states. The protocol can be extended to a communication protocol where the sender and receiver are fully anonymous with the message kept secret. In terms of security, our protocol is designed to comply with the technique of collective detection. Compared to the step-by-step detection, collective detection, in which the participants perform detection only once, reduces the complexity of the protocol to some extent. Moreover, we analytically demonstrate the security of the protocol in the face of active attacks. Any active attack employed by an external or internal attacker cannot reveal any useful information about the sender’s identity. Meanwhile, any malicious behavior will be detected by honest participants.

Collective detection based on visual information in animal groups

We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish ( Notemigonus crysoleucas ) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group.

Collective detection based on visual information in animal groups

The spatio-temporal distribution of individuals within a group (i.e its internal structure) plays a defining role in how individuals interact with their environment, make decisions, and transmit information via social interactions. Group-living organisms across taxa, including many species of fish, birds, ungulates, and insects, use vision as the predominant modality to coordinate their collective behavior. Despite this importance, there have been few quantitative studies examining visual detection capabilities of individuals within groups. We investigate key principles underlying individual, and collective, visual detection of stimuli (which could include cryptic predators, potential food items, etc.) and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish (Notemigonus crysoleucas) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals to do so. Our integrative approach allows us to reveal how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually-detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbors causes detection capability to vary with position within the group. We then formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbors. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group.

New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers

The Collective Detection (CD) technique is a promising approach to meet the requirements for signal acquisition in GNSS-harsh environments. The CD approach has been proposed because of its potential to operate as both a direct positioning method and a high-sensitivity acquisition method. This paper is dedicated to the development of a new CD architecture for processing satellite signals in challenging environments. It proposes the best signal acquisition method used according to the reception conditions of the different receivers that can assist the user in difficulty. Knowing that the CD approach is beneficial in the case where the maximum of satellite signals can be combined, the proposed approach consists in choosing the best receiver(s) from several connected receivers to serve as a reference station, as smart cooperative navigation concept. New metrics of the CD with optimal weighting of visible satellites are exploited. Analysis of optimization method in order to use better satellites according to some defined parameters (elevation, C / N 0 , and GDOP) were carried out. Real GPS L1 C/A signals are exploited to analyze the efficiency of the proposed approach. A comparison of the results through the accumulation of some good satellites among all visible satellites have shown the effectiveness of this method.

A type of collective detection scheme with improved pigeon-inspired optimization

Purpose – With increasing demand of localization service in challenging environments where Global Navigation Satellite Systems (GNSS) signals are considerably weak, a powerful approach, the collective detection (CD), has been developed. However, traditional CD techniques are computationally intense due to the large clock bias search space. Therefore, the purpose of this paper is to develop a new scheme of CD with less computational burden, in order to accelerate the detection and location process. Design/methodology/approach – This paper proposes a new scheme of CD. It reformulates the problem of GNSS signal detection as an optimization problem, and solves it with the aid of an improved Pigeon-Inspired Optimization (PIO). With the improved PIO algorithm adopted, the positioning algorithm arrives to evaluate only a part of the points in the search space, avoiding the problems of grid-search method which is universally adopted. Findings – Faced with the complex optimization problem, the improved PIO algorithm proves to have good performance. In the acquisition of simulated and real signals, the proposed scheme of CD with the improved PIO algorithm also have better efficiency, precision and stability than traditional CD algorithm. Besides, the improved PIO algorithm also proves to be a better candidate to be integrated into the proposed scheme than particle swarm optimization, differential evolution and PIO. Originality/value – The novelty associated with this paper is the proposition of the new scheme of CD and the improvement of PIO algorithm. Thus, this paper introduces another possibility to ameliorate the traditional CD.