Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.

!Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBP), is proposed for defect classification. Firstly, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Secondly, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Thirdly, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting (ARW) method is built for further strengthening the original nearest neighbor classifier (NNC) in the feature matching stage. Extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicate that our proposed SDLBP yields promising performance on both classification accuracy and time efficiency.

Extensive research is now peering into the next generation of wireless technology. The significant talking points are how to effectively manage scarce wireless resources with the spiraling amount of wireless data traffic and the exponential growth of the number of nodes occupying the wireless communication ecosystem. Giant performance strides are offered by promising state‐of‐the‐art technologies, such as Massive MIMO and mm‐Wave technology, in tackling this resource scarcity problem; however, their application has proven prohibitive. Given its energy‐efficient and low‐cost characteristics, RIS has emerged as a promising technique for the beyond 5G networks. Furthermore, when deployed in a wireless communication scenario, the passive nature also means it can reliably extend network coverage and enhance spectral efficiency and security in the physical layer. This paper presents an in‐depth overview of the background of RIS, its applications, and use cases, especially concerning wireless communications. This paper also touches on the RIS application to wireless communication networks and its combination with other emergent wireless technologies such as NOMA, SWIPT, UAVs and autonomous vehicles. Finally, a comparative case study was presented in which comparisons were drawn out for RIS‐aided communication and relay‐aided communication, with direct communication as a benchmark.

The visually secure image encryption scheme is an effective image encryption method, which embeds an encrypted image into a visual image to realize a secure and secret image transfer. This paper proposes a merging compression and encryption chaos image visual encryption scheme. First, a dictionary matrix D is constructed with the plain image by the K-SVD algorithm, which can encrypt the image while sparsing. Second, an improved Zeraoulia-Sprott chaotic map and logistic map are employed to generate three S-Boxes, which are used to complete scrambling, diffusion, and embedding operations. The secret keys of this scheme contain the initial value of the chaotic system and the dictionary matrix D, which significantly increases the key space, plain image correlation, and system security. Simulation shows the proposed image encryption scheme can resist most attacks and, compared with the existing scheme, the proposed scheme has a larger key space, higher plain image correlation, and better image restoration quality, improving image encryption processing efficiency and security.

Direct digital synthesis (DDS) technology has replaced many traditional analog circuit functions. The frequency, amplitude, and phase of a waveform generated using DDS can be precisely controlled. It is possible to achieve remarkably high-frequency resolution, fast frequency switching, and modulation can be easily introduced in a DDS generator. However, pulse waveform generated using DDS requires re-loading of the pulse waveform in the waveform memory to vary the pulse parameters, which causes phase discontinuity, requires prohibitively large memory to generate pulses with narrow widths and long periods, and suffers from trigger uncertainty of up to 1 clock period when the pulse waveform is triggered by an external event. Moreover, it is not possible to modulate the width and delay of the pulse stored in the waveform memory. This article describes a fully functional, low complexity, low cost, memory less, parallel, and DDS-based pulse waveform generator implemented in a low-cost field programmable gate arrays (FPGA) where pulse parameters such as amplitude, period, width, rise time, fall time, and delay can all be varied without any glitches or phase discontinuity, where the amplitude, frequency and phase, as well as period, width and delay of the pulse can be modulated by an internally generated or external modulating signal and where it is possible to trigger the generation of the pulse or burst of pulses by an external event where the time between the trigger event and the pulse output is fixed.

The landscape of communication environments is undergoing a revolutionary transformation, driven by the relentless evolution of technology and the growing demands of an interconnected world