Search Results (14)

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions. Full article

Long-period fiber gratings (LPFGs) functioning as band-reject filters have played a pivotal role in the realm of optical communication. Since their initial documentation in 1996, LPFGs have witnessed rapid advancements in areas such as optical sensing, the equalization of optical amplification, and optical band-pass filtering, etc. The unique attributes of optical fiber-based grating, including their miniaturized size, cost-effectiveness, and immunity to electromagnetic interference, have contributed significantly to various sectors over the last two decades. This paper presents a review of the evolution of LPFGs, with a specific focus on the progression and current trends of mechanically induced long-period fiber gratings. It offers a concise overview of coupled-mode theory, the fabrication processes, the merits, and the limitations associated with mechanically induced LPFGs. Moreover, this review elucidates the application methodologies of mechanically induced LPFGs and anticipates future directions in this field. Full article

Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index. Full article

We propose a mode switch based on hybrid-core vertical directional couplers with an embedded graphene electrode to realize the switching function with low power consumption. We designed the device with Norland Optical Adhesive (NOA) material as the guide wave cores and epoxy polymer material as cladding to achieve a thermo-optic switching for the E₁₁, E₂₁ and E₁₂ modes, where monolayer graphene served as electrode heaters. The device, with a length of 21 mm, had extinction ratios (ERs) of 20.5 dB, 10.4 dB and 15.7 dB for the E₂₁, E₁₂ and E₁₁ modes, respectively, over the C-band. The power consumptions of three electric heaters were reduced to only 3.19 mW, 3.09 mW and 2.97 mW, respectively, and the response times were less than 495 µs, 486 µs and 498 µs. Additionally, we applied such a device into a mode division multiplexing (MDM) transmission system to achieve an application of gain equalization of few-mode amplification among guided modes. The differential modal gain (DMG) could be optimized from 5.39 dB to 0.92 dB over the C-band, together with the characteristic of polarization insensitivity. The proposed mode switch can be further developed to switch or manipulate the attenuation of the arbitrary guided mode arising in the few-mode waveguide. Full article

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms. Full article

Image dehazing is a traditional task, yet it still presents arduous problems, especially in the removal of haze from the texture and edge information of an image. The state-of-the-art dehazing methods may result in the loss of some visual informative details and a decrease in visual quality. To improve dehazing quality, a novel dehazing model is proposed, based on a fractional derivative and data-driven regularization terms. In this model, the contrast constrained adaptive histogram equalization method is used as the data fidelity item; the fractional derivative is applied to avoid over-enhancement and noise amplification; and the proposed data-driven regularization terms are adopted to extract the local and non-local features of an image. Then, to solve the proposed model, half-quadratic splitting is used. Moreover, a dual-stream network based on Convolutional Neural Network (CNN) and Transformer is introduced to structure the data-driven regularization. Further, to estimate the atmospheric light, an atmospheric light model based on the fractional derivative and the atmospheric veil is proposed. Extensive experiments display the effectiveness of the proposed method, which surpasses the state-of-the-art methods for most synthetic and real-world images, quantitatively and qualitatively. Full article

The objective of this paper was to examine the dynamic impedance estimation of electrical systems from online measurements. The paper makes several considerations and highlights the challenges to obtain a precise estimation. Transducer equalization and harmonic synchrophasor estimation (HSpE) are reviewed and discussed. The use of online and adaptive equalization for transducers proves to be a viable solution for improving voltage transducer’s (VT’s) and current transducer’s (CT’s) frequency response. Additionally, the use of oversampling algorithms can mitigate the effects of noise in the HSpE. Furthermore, methods for harmonic impedance estimation are discussed. The independent component analysis ICA-based dynamic impedance estimation is proposed and results presented, which yields excellent agreement. Finally, harmonic modeling and simulation of injected harmonic currents are used to observe resonances through the amplification and attenuations and, consequently, the opportunity to confirm the system self and transfer impedances of a test system. Dynamic impedance estimation will continue to be a great challenge for the power systems engineer as the system complexity increases with the massive insertion of power electronic inverters and the associated required filtering. Real-time signal processing will be an effective tool to determine the dynamic self or transfer impedance. Full article

This paper proposes new digital filter architecture based on a modified multiply-accumulate (MAC) unit architecture called truncated MAC (TMAC), with the aim of increasing the performance of digital filtering. This paper provides a theoretical analysis of the proposed TMAC units and their hardware simulation. Theoretical analysis demonstrated that replacing conventional MAC units with modified TMAC units, as the basis for the implementation of digital filters, can theoretically reduce the filtering time by 29.86%. Hardware simulation showed that TMAC units increased the performance of digital filters by up to 10.89% compared to digital filters using conventional MAC units, but were associated with increased hardware costs. The results of this research can be used in the theory of digital signal processing to solve practical problems such as noise reduction, amplification and suppression of the frequency spectrum, interpolation, decimation, equalization and many others. Full article

Coherent fiber-optic communication systems are limited by the Kerr-induced nonlinearity. Benchmark optical and digital nonlinearity compensation techniques are typically complex and tackle deterministic-induced nonlinearities. However, these techniques ignore the impact of stochastic nonlinear distortions in the network, such as the interaction of fiber nonlinearity with amplified spontaneous emission from optical amplification. Unsupervised machine learning clustering (e.g., K-means) has recently been proposed as a practical approach to the blind compensation of stochastic and deterministic nonlinear distortions. In this work, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed, for the first time, for blind nonlinearity compensation. DBSCAN is tested experimentally in a 40 Gb/s 16 quadrature amplitude-modulated system at 50 km of standard single-mode fiber transmission. It is shown that at high launched optical powers, DBSCAN can offer up to 0.83 and 8.84 dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalisation, respectively. Full article

Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer. Full article

This paper considers a multiuser broadband uplink massive multiple input multiple output (MIMO) millimeter-wave (mmWave) system. The constant envelope orthogonal frequency division multiplexing (CE-OFDM) is adopted as a modulation technique to allow an efficient power amplification, fundamental for mmWave based systems. Furthermore, a hybrid architecture is considered at the user terminals (UTs) and base station (BS) to reduce the high cost and power consumption required by a full-digital architecture, which has a radio frequency (RF) chain per antenna. Both the design of the UT’s precoder and base station equalizer are considered in this work. With the aim of maximizing the beamforming gain between each UT and the BS, the precoder analog coefficients are computed as a function of the average angles of departure (AoD), which are assumed to be known at the UTs. At the BS, the analog part is derived by assuming a system with no multi-user interference. Then, a per carrier basis nonlinear/iterative multi-user equalizer, based on the iterative block decision feedback equalization (IB-DFE) principle is designed, to explicitly remove both the multi-user and residual inter carrier interferences, not tackled in the analog part. The equalizer design metric is the sum of the mean square error (MSE) of all subcarriers, whose minimization is shown to be equivalent to the minimization of a weighted error between the hybrid and the full digital equalizer matrices. The results show that the proposed hybrid multi-user equalizer has a performance close to the fully digital counterpart. Full article

We present a novel optical metro node architecture that exploits the Wavelength Division Multiplexing (WDM) optical cross-connect nodes for interconnecting network elements, as well as computing and storage resources. The photonic WDM cross-connect node based on semiconductor optical amplifiers (SOA) allows switching data signals in wavelength, space, and time for fully exploiting statistical multiplexing. The advantages of using an SOA to realize the WDM cross-connect switch in terms of transparency, switching speed, photonic integrated amplification for loss-less operation, and gain equalization are verified experimentally. The experimental assessment of a 4 × 4 photonic integrated WDM cross-connect confirmed the capability of the cross-connect chip to switch the WDM signal in space and wavelength. Experimental results show lossless operation, low cross-talk <−30 dB, and dynamically switch within few nanoseconds. Moreover, the operation of the cross-connect switch with multiple WDM channels and diverse modulation formats is also investigated and reported. Error-free operation with less than a 2 dB power penalty for a single channel, as well as WDM input operation, has been measured for multiple 10/20/40 Gb/s NRZ-OOK, 20 Gb/s PAM4, and data-rate adaptive DMT traffic. Compensation of the losses indicates that the modular architecture could scale to a larger number of ports. Full article