Real-time temperature surveillance of the reactions and phase transformations in the flash smelting furnaces burner flame is of vital importance to assess the operational process. For this purpose, a radiometric optical system based on a visible to near infrared (VIS-NIR) spectrometer fitted with a specialized method is proposed as a sensor for industrial flash copper smelters, thus providing real-time information to aid process control. The proposed sensor captures the burners flame irradiance to estimate temperature and emissivity using an optimization based multi-wavelength estimation method rooted around Planck’s radiation model. This multi-wavelength method is capable of calculating radiometric temperature as well as spectral emissivity, without previous knowledge of the emissivity model. In this work, different optimization algorithms were used to solve the multi-wavelength model and the results are compared to the commonly used two-wavelength pyrometric method for data obtained in laboratory and industrial copper smelter scenarios. The method’s robustness in presence of additive white noise is studied between -30 to 30 dB signal-to-noise ratio (SNR). The multi-wavelength method reported minimum temperature error values of 4 °C, as well as a relative error under 10% for an SNR of -30 dB, giving this method essential characteristics for industrial applications, measurement accuracy and robustness.

In this paper, a novel optical technique for following the progress of the blister copper desulfurization process is presented. The technique is based on the changes observed in the continuous spectrum of the visible–near-infrared (VIS–NIR) radiation that the blister melt emits while the chemical reactions of the sulfur elimination process are taking place. Specifically, the proposed technique uses an optical probe composed of an optical fiber, a collimating lens, and a quartz tube, which is immersed in the melt. This optical probe provides a field of view of the blowing zone where the desulfurization reaction occurs. The experimental results show that the melt VIS–NIR total irradiance evolves inversely to the SO₂ concentration reported by a gas analyzer based on differential optical absorption spectroscopy. Furthermore, the blister copper spectral emissivity as well as the total emissivity observed throughout the process show strong correlation with the sulfur content during desulfurization reaction.

Hankel rank reduction (HRR) is a method that, by prearranging the data in a Hankel matrix and performing rank reduction via singular value decomposition, suppresses the noise of a time-history vector comprised of the superposition of a finite number of sinusoids. In this letter, the HRR method is studied for performing clutter suppression in synthetic aperture radar (SAR)-based vibrometry. Specifically, three different applications of the HRR method are presented. First, resembling the SAR slow-time signal model, the HRR method is utilized for separating a chirp signal immersed in a sinusoidal clutter. Second, using simulated airborne SAR data with 10 dB of signal-to-clutter ratio, the HRR method is applied to perform target isolation and to improve the results of an SAR-based vibration estimation algorithm. Finally, the vibrometry approach combined with the HRR method is validated using actual airborne SAR data.

A reference-free image index to jointly assess infrared-imaging fixed-pattern-noise and blur artifacts is proposed in this work. The proposed index is based on tuned-spatial-domain filtering, which works by combining two Laplace operators to simultaneously quantify the global infrared-imaging fixed-pattern-noise and the global or local blur artifacts. The index effectiveness is demonstrated by two task-based image-quality assessments to determine the focused and fixed-pattern-noise free images from sequences captured with both a mid-wave-infrared microscope system and a long-wave-infrared plenoptic system. The index quantitative limits are shown on numerical computations over synthetic corrupted images as well as real black-body radiator calibrated infrared images with representative simulated fixed-pattern noise, from six well known infrared focal plane arrays transducer technologies, along with artificial blur added using real infrared imaging system point spread functions.

The growing number of connected devices has strained traditional radio frequency wireless networks, driving interest in alternative technologies such as optical wireless communications (OWC). Among OWC solutions, optical camera communication (OCC) stands out as a cost-effective option because it leverages existing devices equipped with cameras, such as smartphones and security systems, without requiring specialized hardware. This paper proposes a novel deep learning-based detection and classification model designed to optimize the receiver’s performance in an OCC system utilizing color-shift keying (CSK) modulation. The receiver was experimentally validated using an $8\times 8$ LED matrix transmitter and a CMOS camera receiver, achieving reliable communication over distances ranging from 30 cm to 3 m under varying ambient conditions. The system employed CSK modulation to encode data into eight distinct color-based symbols transmitted at fixed frequencies. Captured image sequences of these transmissions were processed through a YOLOv8-based detection and classification framework, which achieved $98.4$% accuracy in symbol recognition. This high precision minimizes transmission errors, validating the robustness of the approach in real-world environments. The results highlight OCC’s potential for low-cost applications, where high-speed data transfer and long-range are unnecessary, such as Internet of Things connectivity and vehicle-to-vehicle communication. Future work will explore adaptive modulation and coding schemes as well as the integration of more advanced deep learning architectures to improve data rates and system scalability.

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.