Iet Optoelectronics最新文献

This paper reports on new schemes based on the concept of tri-state switching for routing linear arrays of light-emitting diodes (LEDs) on slim substrates. The schemes use a minimal number of wires in single-metal planar technologies, where wires are not allowed to cross. They have in common that the number of LEDs, N_L, addressable by N_W wires is given by N_L = 4N_W − 6. The designs are built on a family of hierarchically interconnected structures with (2ⁿ⁺¹ − 2) LEDs and (2ⁿ⁻¹ + 1) wires, for positive integers n. A process termed linear expansion straightforwardly extends them to arbitrary values of N_W and adds 4 LEDs with each additional wire. Expressions for series resistances and the average wire length normalized to the array length are derived. In hierarchical designs, the average normalized wire length asymptotically approaches 7/12 for large n. A matrix formulation graphically elucidates the new interconnection schemes.

To solve the problem that the local contrast algorithm is easily influenced by the heavy clutter background or strong noise and does not fully consider the spatial neighbourhood characteristics of the small target, a spatial dissimilarity weighted local contrast-based method (SDWLCM) for infrared small target detection is proposed in this paper. Firstly, the two-dimensional difference of the Gaussian filter with central excitation and lateral suppression characteristics is chosen to preprocess the original infrared image for removing the flat background and improving the signal-to-noise ratio (SNR) of the small target. Secondly, the spatial dissimilarity between the target and its surrounding backgrounds is designed for local contrast weighting to generate the contrast saliency map so that the heavy clutter background is greatly suppressed and the small target is further highlighted. Thirdly, the saliency map is segmented by the adaptive threshold to get the real targets. Experimental results show that, compared with other methods, the SDWLCM, which owns not only a higher SNR gain and a larger background suppression factor but also a higher detection rate and a lower false alarm rate, is confirmed to be an effective method to detect the small targets.

One of the main barriers of free space optical (FSO) communication systems is atmospheric turbulence. Various processing techniques at the transmitter, receiver, and transceiver sides are available for addressing this issue; however, they have either high complexity or low performance. Considering this problem, in this study, deep learning (DL) is deployed at the transmitter, receiver, and transceiver sides of an FSO system for constellation shaping, detection, and joint constellation-shaping detection, respectively. Furthermore, the proposed DL-based structures are deployed in an FSO-multi-input multi-output (MIMO) system. As the first investigation over DL for the FSO-MIMO system, different combining schemes including the maximum ratio combiner, equal gain combiner, and the selection combiner are considered. Considering a wide range of atmospheric turbulence, from the weak to the strong regime, the performance of the proposed structures are compared with that of the maximum likelihood (ML) detection. To the best of the authors' knowledge, the main contributions and novelties of this work include considering transmitter learning in the FSO system, designing low complexity DL structures for FSO system applications, and providing complexity analysis for the proposed DL algorithms. The results indicate that the proposed DL-based FSO systems achieve the optimum performance with lower complexity compared with the state-of-the-art conventional FSO systems. For instance, the proposed DL-based detector is almost 2, 3, and 7.5 times faster than the ML detector for modulation orders of 16, 64, and 256, respectively.