Binary Masking Research Articles

Advancements and Challenges in Photomask Technologies for Semiconductor Lithography: A Comparative Review of Binary, PhaseShift, and EUV Masks

This report investigates essential photomask technologies in semiconductor manufacturing and their developments. It outlines the principles and uses of binary photomasks, attenuated phase shift masks, and extreme ultraviolet (EUV) photomasks in lithography systems. With technological advancements, masks have evolved to tackle the demand for smaller feature sizes. Despite these advancements, each mask type has specific applications and constraints. The report also touches on other mask types, including ternary, chromeless phase lithography (CPL), and silicon-containing masks, and anticipates future challenges and directions in photomask technology

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.

Visual defect obfuscation based self-supervised anomaly detection

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalies. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: (1) time-consuming inference due to multiple masking, (2) output inconsistency by random masking, and (3) inaccurate reconstruction of normal patterns for large masked areas. Motivated by this, this study proposes a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolves the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the performance than emptying those regions by binary masking, thereby overcomes issue 3. The proposed approach achieves a high performance without any change of the model structure. Promising results are shown through laboratory tests with public industrial datasets. To suggest EAR be possibly adopted in various industries as a practically deployable solution, future steps include evaluating its applicability in relevant manufacturing environments.

Privacy-preserving reservation model for public facilities based on public Blockchain

Ensuring fairness in the utilization of government-funded public facilities, such as co-working spaces, sports fields, and meeting rooms, is imperative to accommodate all citizens. However, meeting these requirements poses a significant challenge due to the high costs associated with maintaining digital infrastructure, employee wages, and cybersecurity expenses. Fortunately, Blockchain smart contracts present an economical and secure solution for managing digital infrastructure. They offer a pay-per-transaction schema, immutable transaction records, and role-based data updates. Despite these advantages, public blockchains raise concerns about data privacy since records are publicly readable. To address this issue, this study proposes a privacy-preserving mechanism for public facilities' reservation systems. The approach involves encrypting the reservation table with fully-homomorphic encryption (FHE). By employing FHE with binary masking and polynomial evaluation, the reservation table can be updated without decrypting the data. Consequently, citizens can discreetly book facilities without revealing their identities and eliminating the risk of overlapping schedules. The proposed system allows anyone to verify reservations without disclosing requested data and table contents. Moreover, the system operates autonomously without the need for human administration, ensuring enhanced user privacy.

AFS-BM: enhancing model performance through adaptive feature selection with binary masking

AFS-BM: enhancing model performance through adaptive feature selection with binary masking

Radial velocities: Direct application of Pierre Connes’ shift-finding algorithm to cross-correlation functions

Context. Pipelines of state-of-the-art spectrographs dedicated to planet detection provide, for each exposure, series of cross-correlation functions (CCFs) built with a binary mask (BM), as well as the absolute radial velocity (RV) derived from the Gaussian fit of a weighted average CCFtot of the CCFs. Aims. Our aim was to test the benefits of the application of the shift-finding algorithm developed by Pierre Connes directly to the total CCFtot, and to compare the resulting RV shifts (DRVs) with the results of the Gaussian fits. In a second step, we investigated how the individual DRV profiles along the velocity grid derived from the shift-finding algorithm can be used as an easy tool for detection of stellar line shape variations. Methods. We developed the corresponding algorithm and tested it on 1151 archived spectra of the K2.5 V star HD 40307 obtained with ESO/ESPRESSO during a one-week campaign in 2018. Tests were performed based on the comparison of DRVs with RVs from Gaussian fits. DRV profiles along the velocity grid (DRV(i)) were scrutinized and compared with direct CCFtot ratios. Results. The dispersion of residuals from a linear fit to RVs from 406 spectra recorded within a single night, a measure of mean error, was found to be σ = 1.03 and 0.83 m s−1 for the Gaussian fit and the new algorithm, respectively, which is a significant 20% improvement in accuracy. The two full one-week series obtained during the campaign were also fitted with a three-planet system Keplerian model. The residual divergence between data and best-fit model is significantly smaller for the new algorithm than for the Gaussian fit. Such a difference was found to be associated in a large part with an increase of ≃1.3 m s−1 in the difference between the two types of RV values between the third and fourth nights. Interestingly, the DRV(i) profiles reveal at the same time a significant variation of line shape. Conclusions. The shift-finding algorithm is a fast and easy tool that provides additional diagnostics on the RV measurements in series of exposures. For observations made in the same instrumental configuration, and if line shapes are not varying significantly, it increases the accuracy of velocity variation determinations. On the other hand, departures from constancy of the DRV(i) profiles, as well as varying differences between RVs from this new method and RVs from a Gaussian fit can detect and report in a simple way line shape variations due to stellar activity.

A GPU-accelerated computational fluid dynamics solver for assessing shear-driven indoor airflow and virus transmission by scale-resolved simulations

We explore the applicability of MATLAB for 3D computational fluid dynamics (CFD) of shear-driven indoor airflows. A new scale-resolving, large-eddy simulation (LES) solver titled DNSLABIB is proposed for MATLAB utilizing graphics processing units (GPUs). In DNSLABIB, the finite difference method is applied for the convection and diffusion terms while a Poisson equation solver based on the fast Fourier transform (FFT) is employed for the pressure. The immersed boundary method (IBM) for Cartesian grids is proposed to model solid walls and objects, doorways, and air ducts by binary masking of the solid/fluid domains. The solver is validated in two canonical reference cases and against experimental data. Then, we demonstrate the validity of DNSLABIB in a room geometry by comparing the results against another CFD software (OpenFOAM). Next, we demonstrate the solver performance in several isothermal indoor ventilation configurations and the implications of the results are discussed in the context of airborne transmission of COVID-19. The novel numerical findings using the new CFD solver are as follows. First, a linear scaling of DNSLABIB is demonstrated and a speed-up by a factor of 3-4 is also noted in comparison to similar OpenFOAM simulations. Second, ventilation in three different indoor geometries are studied at both low (0.1 m/s) and high (1 m/s) airflow rates corresponding to Re=5000 and Re=50000. An analysis of the indoor CO2 concentration is carried out as the room is emptied from stale, high CO2 content air. We estimate the air changes per hour (ACH) values for three different room geometries and show that the numerical estimates from 3D CFD simulations differ by 80%–150% (Re=50000) and 75%–140% (Re=5000) from the theoretical ACH value based on the perfect mixing assumption. Third, the analysis of the CO2 probability distributions (PDFs) indicates a relatively non-uniform distribution of fresh air indoors. Fourth, utilizing a time-dependent Wells-Riley analysis, an example is provided on the growth of the cumulative infection risk which is shown to reduce rapidly after the ventilation is started. The average infection risk is shown to reduce by a factor of 2 for lower ventilation rates (ACH=3.4-6.3) and 10 for the higher ventilation rates (ACH=37-64). Finally, we utilize the new solver to comment on respiratory particle transport indoors. A key contribution of the paper is to provide an efficient, GPU compatible CFD solver environment enabling scale-resolved simulations (LES/DNS) of airflow in large indoor geometries on a single GPU designed for high-performance computing. The demonstrated efficacy of MATLAB for GPU computing indicates a high potential of DNSLABIB for various future developments on airflow prediction.

Deep signal separation for adaptive estimation of instantaneous phase from vibration signals

Accurate extraction of instantaneous phase information is an essential step in condition monitoring and intelligent diagnosis of rotating machinery under variable speed conditions, which will directly affect the reliability of the tacho-less order tracking (TLOT) results. At present, instantaneous phase information extraction techniques are mainly divided into time–frequency analysis and signal decomposition. These methods are constrained because they are not available to extract harmonic components adaptively. Therefore, it is important to develop an intelligent model to accurately separate harmonic component with strict physical meaning for TLOT. In order to address the aforementioned issues, a deep binary mask (BM) signal separation model is presented for rotating machinery TLOT. The deep BM signal separation method can adaptively separate the fundamental harmonic component of vibration signals without prior knowledge. The outcomes indicate that the deep BM signal separation model is more productive and flexible in terms of accuracy and self-adaptation compared to some advanced TLOT algorithms.

Interferenceless Incoherent Holography Optimized by Alternate Iteration of Binary Coded Phase Mask

Interferenceless Incoherent Holography Optimized by Alternate Iteration of Binary Coded Phase Mask

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

Chinese Speech Enhancement and Adaptive Recognition Technology for Complex Language Environments

The development of intelligent technology has also made rapid progress in relevant speech fields. In order to increase the application scenarios of speech recognition systems, the research has improved the traditional Speech enhancement algorithm, namely the Ideal Binary Mask (IBM) algorithm, and combined it with the unimproved IBM algorithm to propose an adaptive IBM algorithm. Based on this algorithm, the research has built a new speech recognition system, The system uses an FIR filter to realize pre-emphasis processing and uses Berouti spectral subtraction to preprocess speech. The Speech enhancement model is built using a deep learning network model. The results showed that the IBM algorithm had the highest score in the Perceptual Evaluation of Speech Quality (PESQ) at 3.5596, followed by the Ideal Ratio Mask (IRM) algorithm at 3.3429. The improvement of the IBM algorithm was feasible when the noise intensity coefficient was greater than 0.008. When the noise intensity coefficient was greater than 0.08, the average score of the improved IBM algorithm was 2.1079, and the average score of the unimproved IBM algorithm was 1.9418. The proposed adaptive IBM algorithm has higher performance in complex speech environments compared to the original system.

Ensemble System of Deep Neural Networks for Single-Channel Audio Separation

Speech separation is a well-known problem, especially when there is only one sound mixture available. Estimating the Ideal Binary Mask (IBM) is one solution to this problem. Recent research has focused on the supervised classification approach. The challenge of extracting features from the sources is critical for this method. Speech separation has been accomplished by using a variety of feature extraction models. The majority of them, however, are concentrated on a single feature. The complementary nature of various features have not been thoroughly investigated. In this paper, we propose a deep neural network (DNN) ensemble architecture to completely explore the complimentary nature of the diverse features obtained from raw acoustic features. We examined the penultimate discriminative representations instead of employing the features acquired from the output layer. The learned representations were also fused to produce a new features vector, which was then classified by using the Extreme Learning Machine (ELM). In addition, a genetic algorithm (GA) was created to optimize the parameters globally. The results of the experiments showed that our proposed system completely considered various features and produced a high-quality IBM under different conditions.

Geoacoustic Inversion with a Single Vector Sensor and Multichannel Dispersion Curves

This paper discusses the value added by using a single vector sensor over a conventional pressure-only hydrophone for geoacoustic inversions. Inversion methods based on genetic algorithms are used to estimate the seabed properties. Synthetic signals of impulsive arrivals first are modeled using KRAKEN and RAM propagation models, each being modified to predict components of the vector field. While KRAKEN is utilized to directly compute dispersion curves, RAM provides full-field results that require the application of time warping to separate the modal arrivals. Combinations of dispersion curves utilizing all vector sensor channels are compared to curves estimated with the pressure-only channel. Within the time warping analysis, both binary masking and band-pass filter masking methods are applied to compare stability of results. The environment modeled for the synthetic analysis and inversion method utilize sound speed profiles measured during the Monterey Bay 2019 at-sea experiment and assume a sediment layer of constant thickness overlying a deeper sub-bottom type. White noise is added to the synthetic data at different signal-to-noise ratios to evaluate the impact of signal excess on the results. A hybrid optimization approach is used to improve the results of the genetic algorithm method. The analysis with synthetic data is consistent with the analysis of broadband, impulsive data collected from the experiment, indicating that the additional information from the vertical velocity channel further improves the geoacoustic parameter estimates.

Improvising grading of cervical cancer using quality assessment method in smart colposcopy images

Smart Colposcopy is a low-cost and highly effective screening device used to detect cervical cancer. However, the captured images can be significantly affected by specular reflection, resulting in reduced accuracy during the grading process. In this paper, proposed a novel approach to enhance the accuracy of cervical cancer grading by addressing the issue of specular reflection. Here the method employs binary masking to identify the glare region and fine-tuned U-Net model to perform segmentation. Partial convolutional inpainting is used to replace the segmented region with neighboring pixels, effectively removing the glare from the images. The resulting enhanced images are then fed into classification models, including DenseNet121, Vgg19, and Efficient Net, which are trained to accurately grade cervical cancer. Experimental results demonstrate that our proposed method significantly improves the accuracy of cervical cancer grading, achieving accuracy rates of 97.32%, 96.25%, and 96.75%, respectively.

How Sensitive Is Thermal Image-Based Orchard Water Status Estimation to Canopy Extraction Quality?

Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management.

Comparison of ideal mask-based speech enhancement algorithms for speech mixed with white noise at low mixture signal-to-noise ratios

The literature shows that the intelligibility of noisy speech can be improved by applying an ideal binary or soft gain mask in the time-frequency domain for signal-to-noise ratios (SNRs) between -10 and +10 dB. In this study, two mask-based algorithms are compared when applied to speech mixed with white Gaussian noise (WGN) at lower SNRs, that is, SNRs from -29 to -5 dB. These comprise an Ideal Binary Mask (IBM) with a Local Criterion (LC) set to 0 dB and an Ideal Ratio Mask (IRM). The performance of three intrusive Short-Time Objective Intelligibility (STOI) variants-STOI, STOI+, and Extended Short-Time Objective Intelligibility (ESTOI)-is compared with that of other monaural intelligibility metrics that can be used before and after mask-based processing. The results show that IRMs can be used to obtain near maximal speech intelligibility (>90% for sentence material) even at very low mixture SNRs, while IBMs with LC = 0 provide limited intelligibility gains for SNR < -14 dB. It is also shown that, unlike STOI, STOI+ and ESTOI are suitable metrics for speech mixed with WGN at low SNRs and processed by IBMs with LC = 0 even when speech is high-pass filtered to flatten the spectral tilt before masking.

Spectral Methods for Single Channel Speech Enhancement in Multi-Source Environment

Speech communication for both humans and automatic devices can be negatively impacted by background noise, which is common in real environments. Among many techniques, speech separation using a single microphone is the most desirable from an application standpoint. The resulting monaural speech separation problem has been a central one in speech processing for several decades. However, its success has been limited thus far. This research presents work that develops speech separation systems using combinations of T-F masking, DNNs, and model-based reconstruction. The aim of each system is to improve the perceptual quality of the speech estimates. The performance of many speech processing applications is severely degraded when both noise and reverberation are present. The proposed solution has been tested in the simulation environment and based on the simulation result, it is observed that the speech enhancement can easily be performed through the integration of the solution. This research suggests two staged noise reducing systems in order to reduce the background noise through a single microphone recording in a low-SNR based on ideal binary masking and Wiener filter. It has two stages. Firstly, for background noise reduction, a Wiener filter with an enhanced SNR is utilised on noisy speech. Secondly, IBM is calculated in each time–frequency channel through utilisation of the pre–processed speech from the first stage and the matching of the time–frequency channels to a pre-selected threshold in order to minimise residual noise. These channels meeting the threshold requirement are conserved while all the other ones are attenuated.

Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection

Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance.

Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network

Speech separation refers to the separation of mixed utterances in which multiple people are speaking simultaneously. This topic was created to address the cocktail party problem, and it has also evolved into a front-end procedure for speech-related research recently. There are two types of models for addressing it: classification methods and regression methods. Classification methods avoid considering the permutation problem, and regression methods are more precise. Deep clustering (DC) is a common deep learning classification technique that uses a deep embedding network to embed signals in the underlying manifold and allows signals with similar properties to cluster together in the embedded space. The ideal binary mask (IBM) supervises the model. Even if it is entirely isolated via supervised binary masking, this setting is susceptible to mistake. To remedy this flaw, this paper proposes a network with two modules based on deep embedding. More precise masks, such as the ideal ratio mask (IRM), can now be used instead of IBM. Furthermore, the cascade structure may preserve DC's high performance. On average, the suggested model beats the original DC model by 1.55dB in SNR, 4.45dB in SDR, 4.41dB in SDRi, 0.16 in STOI, and 0.3 in PESQ on a mixture of Japanese Newspaper Article Sentences (JNAS). Finally, a comparison is made with ChimeraNet which has a similar structure, the results prove the proposed model's superiority over previous DC-related studies.

Multi-illumination 3D holographic display using a binary mask

We introduce a novel, to the best of our knowledge, method to increase the bandwidth in holographic displays. Here, multi-angle illumination using multiple laser diodes (LDs) is adopted to expand the limited diffraction angle of the spatial light modulator (SLM). To solve the problem of signal repetitions caused by sharing the same SLM pattern, we use a random binary mask (BM). We demonstrate via simulations and experiments that our method effectively increases the bandwidth with sufficient image quality. Furthermore, the speckle noise, a critical issue of the holographic display that decreases the contrast and is potentially harmful to eyes, is reduced by the advantage of incoherent summation in the reconstruction plane. We believe that this method is a practical approach that can expand the bandwidth of the holographic display by alleviating the bottleneck of hardware limitations.