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The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, and even lasers as weapons (e.g., Airborne Laser). Lasers in the support of telecommunications is also not new. The use of laser light in fiber optics has shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform the U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers, rather than traditional radio frequency sources for satellite uplink and downlink signal carriers, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, and large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploit (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.

This chapter contains sections titled: Introduction Vibrational Spectroscopy: Sampling Techniques Closing Remarks Acknowledgements References

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface–shroud resonances support conscious seeing and action, whereas feature–category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure–ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive–emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

A collective intelligence factor (CI) was introduced by prior research to characterise the cognitive ability of groups. Surprisingly, individual intelligence did not predict CI. Instead, it correlated with individual social sensitivity, the equality of conversational turn-taking, and the proportion of females in a group. However, these findings may depend on the type of tasks completed by groups. Our study re-examined these relationships by (1) testing the robustness of the CI factor in dyads using well-structured tasks guided by the Cattell–Horn–Carroll (CHC) model of intelligence; (2) exploring the relationship between dyadic CI and metacognitive confidence, which is known to influence group processes and outcomes; and (3) identifying the psychological characteristics of distinct dyad types using latent profile analysis. We measured CI in 105 undergraduate dyads using three group tasks aligned with the broad abilities of the CHC model. Individual intelligence was assessed using Raven’s Advanced Progressive Matrices. We also measured social sensitivity, proportion of females, equality of turn-taking, working memory, and personality. Results indicated that individual intelligence and confidence were the strongest predictors of dyadic CI for well-structured tasks, contrasting with previous findings emphasising social factors. While we replicated the relationship with social sensitivity, we did not replicate the findings for equality of turn-taking or gender composition. Latent profile analysis identified three psychological profiles: dyads performing consistently high individually and collectively, those performing consistently low, and those performing better collectively than individually. Our “smarter” dyads consisted of intelligent and confident individuals with higher social sensitivity. These findings suggest that, in dyads performing well-structured tasks, individual cognitive abilities and confidence play significant roles in CI. This challenges the emphasis on social factors and underscores the importance of task selection. Significance Statement Dyads are commonly utilised to make decisions because there is a belief that “ two heads are better than one ”. However, not all dyads outperform individuals, and the factors contributing to their success remain inadequately understood. This knowledge gap limits our ability to systematically develop effective collaboration, which is essential for improving real-world dyadic outcomes. Our findings address this gap by extending collective intelligence (CI) to dyads completing well-structured tasks. Our findings challenge prior research by showing that individual intelligence, coupled with confidence and social sensitivity, plays a more important role in dyadic CI for well-structured tasks than previously recognised. By employing a novel battery of tasks guided by the Cattell–Horn–Carroll model of intelligence and latent profile analysis, we identified three distinct types of dyads: those who consistently performed at a high level; those who consistently performed at a low level; and those who performed better collectively than individually. This last profile enhanced performance through strategic dominance by allowing the more competent member to dominate discussions and decision choices. This challenges prior research that claimed equality of conversational turn-taking was associated with higher CI. Our research refines the theoretical framework of CI by offering a more precise “recipe” for successful dyadic collaboration in well-structured tasks. Our findings can inform dyad selection practices, by emphasising individual intelligence, confidence, and social sensitivity, and guide training programmes aimed at helping members identify and leverage the strengths of the more competent member. These insights have practical implications that can improve dyadic performance in real-world settings.

Maximum likelihood (ML) carrier-frequency offset estimation for orthogonal frequency-division multiple access uplink is a complex multi-parameter estimation problem. The ML approach is a global optima search problem, which is prohibitive for practical applications because of the requirement of multidimensional exhaustive search for a large number of users. There are a few attempts to reduce the complexity of ML search by applying evolutionary optimisation algorithms. In this study, the authors propose a novel canonical particle swarm optimisation (CPSO)-based scheme, to reduce the computational complexity without compromising the performance and premature convergence. The proposed technique is a two-step process, where, in the first step, low resolution alternating projection frequency estimation (APFE) is used to generate a single better positioned particle for CPSO, followed by an actual CPSO procedure in second step. The mean square error performance of the proposed scheme is compared with existing low complexity algorithms namely APFE and linear particle swarm optimisation with mutation. Simulation results presented in this study show that the new scheme completely avoids premature convergence for a large number of users as high as 32.

This chapter contains sections titled: Method Development and Instrumentation Technical Problems in Comprehensive Liquid Chromatography Detection Data Representation Instrumentation Milestones in Comprehensive Liquid Chromatography Applications Beyond Two‐Dimensional Chromatography Comparison of LC X LC and Off‐Line 2D LC Conclusions

This chapter contains sections titled: Key Themes Structure of the Book References Further Reading