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BWT Laser Knowledge Sharing: Lecture 38---Achieving Optical Coupling: Key Conditions to Fulfill #laserknowledge #KnowledgeSharing 1. Fiber end faces must be polished to ensure they are clean, smooth, and perpendicular to the axis. 2. The surrounding area must be clean, and free of dust. 3. The incident light beam must be precisely concentric with the fiber. 4. The beam diameter at the fiber end face must be smaller than the core diameter. 5. The incident angle must not exceed the fiber's maximum acceptable angle. 6. Ensure these conditions are met for optimal optical coupling performance. #OpticalTechnology #FiberOptics #CouplingRequirements

Did you hear about the new update to Fimmwave? 🔔 Fimmwave release V7.2 has updated features to the curved waveguides and fibers which allows it to have z-dependant curvature! For all your applications of Fimmwave, such as single-mode and multi-mode optical fibers, photonic crystal fibers (PCFs), and more, this update will provide you with the ability to go above and beyond. Learn more about Fimmwave release V7.2 here! ✅ - https://ed.gr/drlhc #PhotonDesign #SoftwareRelease #Optics

RAY OPTICS; RAY DIAGRAM OF OPTICAL INSTRUMENTS; REFRACTION AT SPHERICAL SURFACES FOR JEE AND NEET #JEEMADEEASYKOTA, #NEETMADEEASYKOTA, #PHYSICSMADEEASYKOTA, #NDAMADEEASYKOTA, #CETMADEEASYKOTA, #AIIMSMADEEASYKOTA

This paper proposes a parametric #maximum #likelihood #estimation (#MLE) framework for jointly estimating the #direct #channel between the #user and the #base #station (#BS) and the line-of-sight channel between the user and the #reconfigurable #intelligent #surface (#RIS), in both #far-#field and #near-#field #scenarios. The MLE framework is first developed for the case of #single-#antenna BS and later extended to the scenario where the BS is equipped with multiple antennas. A novel adaptive RIS configuration strategy is proposed to select the RIS configuration for the next pilot to actively refine the estimate. They design a #minimal-#sized #codebook of #orthogonal #RIS #configurations to choose from during pilot transmission with a dimension much smaller than the number of RIS elements. ----Mehdi Haghshenas, Parisa Ramezani, Maurizio Magarini, Emil Björnson More details can be found at this link: https://lnkd.in/gasVAGMQ

This paper presents an #efficient #channel #estimation algorithm for #multi-#user #reconfigurable #intelligent #surface (#RIS)-aided #millimeter-#wave (#mmWave) systems. In this paper, the concept of #low #rank #matrix #completion (#LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRMC with less beam training overhead. Then, the beam training is followed by #multi-#user #channel #estimation. For computationally efficient channel estimation, the proposed algorithm exploits the property that all the channel matrices share the same low-rank subspace in multi-user RIS-aided systems. The shared subspace is derived by combining candidate subspaces, which are estimated by #fast #alternating #least #squares (#FALS) from partially observed channels. With the shared subspace, all the missing entries of channels are recovered via computationally efficient linear estimation.----Hyeonjin Chung, Songnam Hong, Sunwoo Kim More details can be found at this link: https://lnkd.in/duxNYE2Y

What is Discrete Fracture Network #DFN A Discrete Fracture Network (DFN) is a model used to represent the geometry and properties of fractures within rock masses. The DFN approach models fractures as individual, discrete entities rather than as a continuum, providing a more realistic depiction of how fluids flow or how stress propagates in a fractured rock mass. The Key Characteristics of DFN modelling include Fracture Geometry, Fracture Connectivity, Fracture Properties, and Stochastic Nature.   #WSP #FracMan #DFN FracMan® is the leading Discrete Fracture Network (DFN) software https://lnkd.in/gFNxMjJn

This paper explores the performance of #Orthogonal #time #sequency #multiplexing (#OTSM), considering the joint effects of #multiple #hardware impairments (#HWIs) such as #in-#phase #and #quadrature #imbalance (#IQI), #direct #current #offset (#DCO), phase noise, power amplifier non-linearity, carrier frequency offset, and synchronization timing offset for the first time in the area. First, the discrete-time baseband signal model is obtained in vector form under all mentioned HWIs. Second, the system input-output relations are derived in time, delay-time, and #delay-#sequency (#DS) domains in which the parameters of all mentioned HWIs are incorporated. Third, analytical expressions are derived for the pairwise and average bit error probability under imperfect #channel #state #information (#CSI) as a function of the parameters of all mentioned HWIs. ---- Abed Doosti-Aref, Christos Masouros More details can be found at this link: https://lnkd.in/guesJTdR

Coverage (also known as noise-like) jammers try to disrupt the radar receiver functionalities by modulating and transmitting noise signals. The effect is that the radar system becomes blind over certain angular sectors due to 1. uncontrolled increase of the number of false alarms (nonadaptive threshold or wrongly adapted threshold); 2. increase of the adaptive detection threshold to maintain the false alarm rate constant with a consequent impairment of the probability of detection. Possible counter-countermeasures consist in detecting the presence of such intentional interference and then in placing a null along the attack direction. The conventional (or, at least, most common) detection procedures rely on the identification of noise power jumps within the radar receiver (see for instance https://lnkd.in/g2zUFjz or https://lnkd.in/dAmcS7gy and references therein). However a question naturally arises: how do such detection algorithms behave when the noise jammer slowly and continuously increases the transmitted power to avoid any discontinuity in the receiver noise? The answer is that they might fail in detecting the jamming attack and different decision schemes are required to account for this power variation pattern. In https://lnkd.in/dAmcS7gy, we design two innovative noise jammer detection architectures by assuming at the design stage specific models for the noise power variation. The first model is based on a linear variation law over the observation time, whereas in the second model, the noise power experiences unconstrained fluctuations in the window under test. It is shown how such design assumptions can improve the detection performance of smart noise-like jammers.

Learn about the detection capabilities of Dual Linear Array (DLA) and Dual Matrix Array (DMA) phased array probes in this video from Evident. Click here to watch. #Sponsored https://zurl.co/TTxz