Contemporary white light-emitting diodes (LEDs) are much more efficient than compact fluorescent lamps and hence are rapidly capturing the market for general illumination. LEDs are also replacing halogen lamps or even newer xenon based lamps in automotive headlamps. Because laser diodes are inherently much brighter and often more efficient than corresponding LEDs, there is great research interest in developing laser diode based illumination systems. Operating at higher current densities and with smaller form factors, laser diodes may outperform LEDs in the future. This article reviews the possibilities and challenges in the integration of visible laser diodes in future illumination systems.

Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet.

A combined optical coherence tomography (OCT)-Raman probe was designed and built into a spectral domain OCT head, and its performance was evaluated and compared to the most common Raman probe setups, based on a fiber bundle and confocal free space optics. Due to the use of the full field of view of an OCT scanning lens, the combined probe has a superior performance within maximum permissible exposure limits, compared to the other two probes. Skin Raman spectra, recorded in vivo, further prove the feasibility of the OCT-Raman probe for the future in vivo clinical applications in skin cancer screening.

Resonance Raman microspectroscopy in combination with hierarchical cluster analysis (HCA) is one of the most promising tools for the rapid examination of complex biological and medical samples. HCA is a ready, computerized tool for examining large sets of data for common characteristics, and a multitude of algorithms for this purpose have been developed over the years. However, resonance Raman spectra obtained from complex biological samples may originate from different chromophores as well as from a common chromophore found in different host environments, i.e., bacteria. Therefore, algorithms applied to resonance Raman spectra must handle data of high intrinsic similarity, i.e., spectra originating from a common chromophore, and data with highly dissimilar features, i.e., spectra from different chromophores, in the same unsupervised analysis. We examined the performance of eight widely used algorithms for hierarchical cluster analysis in clustering resonance Raman spectra of bacteria: Single-Linkage (Nearest-Neighbor), Complete-Linkage (Farthest-Neighbor), Average-Linkage, Weighted-Average-Linkage, Centroid, Median, and the Ward algorithm. Algorithm performance was evaluated by comparing the results of clustering a set of high-quality reference spectra with the results obtained when clustering a set of spectra recorded from single cells. References were formed by averaging 100 spectra of individual cells. While all algorithms returned highly similar results when clustering the reference spectra, their performance differed significantly when applied to single spectra. The best-performing algorithm, Weighted-Average-Linkage, correctly grouped single spectra with a reliability of above 95% while the spectral distances between the clusters deviated less than 10% from the results obtained with reference spectra. In contrast, the algorithm performing worst showed no similarity to the reference clustering at all. The widely used Ward algorithm deviated up to 30% from the reference in the spectral distances and returned a different spectral relation between bacteria expressing the same chromophore.