Shallow Edge Research Articles

Discriminative feature learning based on multi-view attention network with diffusion joint loss for speech emotion recognition

In speech emotion recognition, existing models often struggle to accurately classify emotions with high similarity. In this paper, we propose a novel architecture that integrates a multi-view attention network (MVAN) and diffusion joint loss to alleviate confusion by placing a stronger focus on emotions that are challenging to classify accurately. First, we use logarithmic Mel-spectrograms (log-Mels), deltas, and delta-deltas of log-Mels as three-dimensional features to minimize external interference. Then, we design the MVAN to extract effective multi-time scale emotion features, where the channel and spatial attention are used to selectively localize the regions in the input features related to the target emotion. A Multi-time view bidirectional long and short-term memory network is used to extract the shallow edge features and deep semantic features, and multi-scale self-attention fuses these features through cross-scale attention fusion to obtain multi-time scale emotion features. Finally, a diffusion joint loss strategy is introduced to distinguish the emotional embeddings with high similarity by the generated complex emotion triplets in a diffusing fashion. We evaluated our proposed method on the Interactive Emotional Mood Binary Motion Capture (IEMOCAP), Chinese Academy of Sciences Automation Institute of Automation (CASIA), and Berlin German Emotion Speech Bank (EMODB) corpus. The results show significant improvements over existing methods, achieving 86.87% WA, 86.60% UA, and 86.82% WF1 on IEMOCAP; 70.74% WA, 70.74% UA, and 70.25% WF1 on CASIA; and 93.65% WA, 91.13% UA, and 92.26% WF1 on EMODB. These results confirm the superiority of our method. Our code and model are available at https://github.com/Littleznnz/MVAN-DiffSEG.

A new global multiplexing structure of original features for road crack detection

ABSTRACT This paper proposes a novel approach, referred to as Global Multiplexing of Original Features (GMOF), to detect cracks in images. The proposed approach leverages the shallow layers of a network to extract crack-edge details, which are then fused with deep features in later stages to improve detection accuracy. Specifically, GMOF utilises the first stage of a backbone network to extract shallow edge information, which is then resized to match the resolution of the subsequent stages. The original feature is then concatenated with the deep feature map, followed by a one-dimensional convolution to ensure channel consistency between stages. The experimental results on image classification and segmentation tasks show that GMOF helps the network learn fine edge details of cracks, resulting in a maximum improvement of 6.06% (classification) and 15.83% (segmentation) in accuracy. This crack detection method is easy to integrate into existing deep learning frameworks.

Evaluation of alternative approaches to PHABSIM modeling of coastal cutthroat trout spawning habitat

AbstractIn the face of a changing climate and increasing human demand for water, an understanding of habitat preference has become critical for managing wild fish populations and projecting potential changes in habitat and populations. Two approaches to Physical Habitat Simulation (PHABSIM) modeling of coastal cutthroat trout Oncorhynchus clarkii clarkii spawning habitat were compared by modeling a reach, consisting of eight transects covering a pool and upstream and downstream riffles, of a small Puget Sound stream with two sets of habitat variables. The reach contained a cluster of redds 2 years in a row, assumed to be an indication of preferred spawning habitat and was located in the area of maximum spawning in the watershed, based on multiple years of redd surveys. Two PHABSIM instream flow models of the study site, one based on standard microhabitat (depth [D], velocity [V], and substrate [S]) suitabilities and the other based on D, V, and channel unit (CU) suitabilities, were developed and compared for their relative ability to correctly predict coastal cutthroat trout spawning habitat selection at the redd cluster within the PHABSIM study site. One approach was the standard use of habitat suitability criteria (HSC) for D, V, and S to indicate spawning habitat quality. The alternate approach was to replace substrate HSC with CU index HSC that incorporated dominant substrate particle diameter, CU (riffle, deep and shallow pool tail, pool body, deep and shallow pool edge, cascade, waterfall, and terrestrial), where deep and shallow units were based on relative residual depth (RRD), size of CU relative to channel width, and position within CU. Spawning habitat quality was calculated for each transect as weighted usable width with the standard HSC metrics (WUWs) as well as the modified CU index (WUWm). WUWm at the transects bracketing the redd cluster exceeded WUWm at the remaining six transects and was outside the 95% confidence interval for WUWm at the remaining transects. In contrast, WUWs varied less between the redd cluster and the remainder of the transects, suggesting the CU index better reflected spawning habitat quality than substrate. Incorporation of elevation relative to SZF addressed vulnerability to declining flow during incubation. Both models resulted in maximum WUA within the range of discharges that coincided with the majority of fresh cutthroat trout redds in Skookum Creek.

Local-enhanced multi-scale aggregation swin transformer for semantic segmentation of high-resolution remote sensing images

ABSTRACT Semantic segmentation of remote sensing images is crucial for various practical applications. In the field of deep learning, convolutional neural network (CNN) has been the primary approach for semantic segmentation over the past decade. Recently, Transformer-based models have achieved superior segmentation performance due to their exceptional global modelling capabilities. However, the Transformer-based models tend to focus more on extracting global contextual information, leading to suboptimal performance in segmenting local edges and difficulties in preserving fine-grained details during the patch token downsampling process. Inspired by the local receptive field of CNN, this article proposes a Local-Enhanced Multi-Scale Aggregation Swin Transformer (LMA-Swin) for semantic segmentation of high-resolution remote sensing images. Specifically, we adopt Swin Transformer as main encoder, introduce convolutional blocks as auxiliary encoder, and design a feature modulation module (FMM) to integrate the local contextual modelling ability of CNN into the Transformer backbone. Additionally, we propose a novel cross-aggregation decoder (CAD) to effectively aggregate shallow edge information and deep semantic information, thereby enhancing the discriminative ability for multi-scale objects. On the ISPRS Vaihingen and Potsdam datasets, experimental results illustrate noteworthy improvement in segmentation performance accomplished through the proposed approach. Code: https://github.com/patricklee16/LMA-Swin.

Survival, cause‐specific mortality, and population growth of white‐tailed deer in western Virginia

AbstractUnderstanding the role of recruitment in population dynamics of white‐tailed deer (Odocoileus virginianus) is important for management. In the central Appalachian Mountains, deer are part of a largely forested ecosystem that supports 3 carnivore species thought to be capable of influencing white‐tailed deer recruitment: black bears (Urus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Yet little is known about predation, how other environmental factors influence recruitment, or the importance of neonate survival to white‐tailed deer population performance in the region. Our objectives were to identify causes of mortality for neonates, analyze effects of landscape attributes on survival of neonates, estimate survival rates for neonates and adult female white‐tailed deer, and to model population growth trends based on current vital rates and hypothetical harvest and neonate survival scenarios. During 2019–2020, we captured 57 neonate deer in Bath County, Virginia, USA, by monitoring 38 pregnant females equipped with global positioning system collars and vaginal implant transmitters and by conducting transect searches for recently born neonates. We observed 37 neonate mortalities and identified cause of death using field and genetic evidence. Mortalities included 28 predation events and 9 deaths from other causes (e.g., abandonment, malnutrition, disease). Black bears accounted for 48.6% of neonate mortalities, and 64.2% of predation events (n = 18), followed by bobcats (n = 5) and coyotes (n = 3). Annual survival for adult female deer was 0.871 and neonate survival to 12 weeks old was 0.310. Elevation was a significant predictor of neonate survival; mortality risk increased 20% for every 100‐m increase in elevation. Models of annual population growth using observed vital rates predicted an increasing population (λ = 1.10). A 10% increase in female harvest would still result in a potential population increase of 2% (λ = 1.02), but a 20% increase in harvest rate would result in a potential 7% decline (λ = 0.93). Neonate survival was higher near fertile valley bottoms and lower along forested ridges characterized by shallow, infertile soils and limited edge or early successional forests. While predation, largely influenced by black bears, was the leading cause of neonate mortality and contributed to low neonate survival, we observed little evidence of population decline, and suggest there is opportunity for a modest increase in harvest of female deer.

Weak Quantum Effects on the Acoustic Raman Mode in Strongly Confined Silicon: Consequence of Shallow Phonon Dispersion Near-Zone Edge

Weak Quantum Effects on the Acoustic Raman Mode in Strongly Confined Silicon: Consequence of Shallow Phonon Dispersion Near-Zone Edge

FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness

Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are widely used in forest fire monitoring tasks due to their flexibility and wide coverage. The key to fire monitoring is to accurately segment the area where the fire is located in the image. However, for early forest fire monitoring, fires captured remotely by UAVs have the characteristics of a small area, irregular contour, and susceptibility to forest cover, making the accurate segmentation of fire areas from images a challenge. This article proposes an FBC-ANet network architecture that integrates boundary enhancement modules and context-aware modules into a lightweight encoder–decoder network. FBC-Anet can extract deep semantic features from images and enhance shallow edge features, thereby achieving an effective segmentation of forest fire areas in the image. The FBC-ANet model uses an Xception network as the backbone of an encoder to extract features of different scales from images. By transforming the extracted deep semantic features through the CIA module, the model’s feature learning ability for fire pixels is enhanced, making feature extraction more robust. FBC-ANet integrates the decoder into the BEM module to enhance the extraction of shallow edge features in images. The experimental results indicate that the FBC-ANet model has a better segmentation performance for small target forest fires compared to the baseline model. The segmentation accuracy on the dataset FLAME is 92.19%, the F1 score is 90.76%, and the IoU reaches 83.08%. This indicates that the FBC-ANet model can indeed extract more valuable features related to fire in the image, thereby better segmenting the fire area from the image.

Role of hydrogen in the post-annealing treatments for amorphous In-Ga-Zn oxide thin films

In-Ga-Zn-O thin films were prepared by magnetron sputtering at different deposition temperatures and annealed in pure hydrogen (H2) and nitrogen (N2), respectively. The carrier concentration increased to 9.6 × 1019 cm−3 and the Hall mobility reached to 9.3 cm2V-1s−1 upon 300 ℃ annealing in N2. However, the Hall mobility decreased to 4.3 cm2V-1s−1 upon thermal annealing in H2 despite of the carrier concentration increased to 1.1 × 1020 cm−3. Spectroscopic ellipsometry results show that the increased carrier concentration is related to the incremental shallow band edge state (D1), and the increasing of deep band edge state (D2) will lead to a rise in trap density and a reduction in Hall mobility.

Edge feature extraction-based dual CNN for LDCT denoising.

In low-dose computed tomography (LDCT) denoising tasks, it is often difficult to balance edge/detail preservation and noise/artifact reduction. To solve this problem, we propose a dual convolutional neural network (CNN) based on edge feature extraction (Ed-DuCNN) for LDCT. Ed-DuCNN consists of two branches. One branch is the edge feature extraction subnet (Edge_Net) that can fully extract the edge details in the image. The other branch is the feature fusion subnet (Fusion_Net) that introduces an attention mechanism to fuse edge features and noisy image features. Specifically, first, shallow edge-specific detail features are extracted by trainable Sobel convolutional blocks and then are integrated into Edge_Net together with the LDCT images to obtain deep edge detail features. Finally, the input image, shallow edge detail, and deep edge detail features are fused in Fusion_Net to generate the final denoised image. The experimental results show that the proposed Ed-DuCNN can achieve competitive performance in terms of quantitative metrics and visual perceptual quality compared with that of state-of-the-art methods.

Elevated loading at the posterior acetabular edge of dysplastic hips during double-legged squat.

Hips with developmental dysplasia (DDH) are at a heightened risk of premature hip osteoarthritis, which is often expedited by mechanically induced articular tissue damage. A prevalent form of damage in DDH is labral tears caused by abnormal loading at the shallow acetabular edge. Although the majority of reported DDH-related labral tears occur in the antero-superior acetabulum, posterior labral tears are prevalent in individuals whose lifestyle involves frequent high hip flexion tasks such as squatting. To better understand region-specific risks for chondrolabral damage during high hip flexion, we used image-based musculoskeletal models to compare acetabular edge loading (AEL) during double-legged squat between hips with symptomatic DDH (n = 10) and healthy controls (n = 10). Compared to controls, hips with DDH had higher instantaneous posterior AEL at the lowest point of squat (2.6 vs. 1.8 ×BW, p ≤ 0.04), and higher accumulative loading across the duration of the squatting motion (2.6 vs. 1.9 ×BW*s, p ≤ 0.04). Elevated posterior AEL coincided with increased net hip extension moments and posterior joint reaction forces, and was correlated with the severity of DDH acetabular deformity. Our findings suggest that posterior AEL is elevated in hips with symptomatic DDH during double-legged squat, which may contribute to chondrolabral damage in individuals who often perform such high hip flexion tasks. Clinical evaluation of DDH should consider patient-specific anatomy and lifestyle factors when establishing diagnoses and planning personalized treatment.

Breeding Behaviour of Black-Winged Stilt, Himantopus himantopus (Linnaeus, 1758) in Agricultural Landscape of Punjab, India

During the breeding seasons (2017-2019), the hitherto little known aspects of breeding behavior of Black-winged Stilt, Himantopus himantopus were studied in the ploughed agricultural fields, village ponds and adjacent shallow pools located in rural outskirts in district Ludhiana, Punjab, India. Observations on nest structure, egg laying, incubation, hatching and other behavioural aspects of the parents/chicks were inferred from videorecords, photographs and direct field observations made on 19 clutches containing 59 eggs. In the study area the breeding season extended from April to July. The mean clutch size was 3.29±0.77 (range 1-4, n=19) that differed between the dry ploughed field clutches (3.4±0.70, range: 2-4, n=10) and wet pool/pond sites (2.78±1.09, range: 1-4, n=9). In the dry ploughed fields, the nest scrapes were lined mainly with the wheat rootlets/ straw pieces only. However, the nest material used in/around ponds and pools included predominantly dry twigs, decaying leaves and some thermocol chips. The egg length, breadth, weight, egg shape index and volume (n=54) measured 42.22±2.50 mm, 30.71±1.13 mm, 19.77±2.34 gm, 72.88±3.19 and 20.39±2.36 cm3, respectively. The eggs hatched mostly synchronously after biparental incubation period of 25 or 26 days (n=4). As per traditional method, only 20.34% (12 out of 59 eggs) of the eggs hatched successfully and the hatching success calculated as per the Mayfield Method showed that it was only 2.07% in post-harvest field sites and 36.26% in non-agricultural sites (shallow water pools and edge waters of village ponds). The use of polluted sites as nesting grounds has been opined as a habitat shift and an adaptation in process

Characteristics and evolution of the Paleogene overpressured top seal in Niuzhuang Sub-sag, Dongying Sag, Bohai Bay Basin

The buried depth of overpressured top seal layer in Niuzhuang sub-sag is mainly 2700–3600m, which is characterized by deep center and shallow edge in plane. Its lithology is a combination of dark grey, grey mudstone and sandy mudstone. There are a large number of carbonate cements and clay minerals in the argillaceous rocks that form the top sealing layer. The diagenesis of mudstone occurs in the rapid transformation period of illite/smectite mixed layer minerals, and corresponds to a large number of carbonate cements. The displacement pressure, thickness and diagenetic intensity of overpressured top seal are important factors affecting its physical sealing ability. There are five evolution stages of overpressured top seal, namely, at the initial stage of settlement, the strata are normally compacted, and the development of overpressured top seal is not obvious; During the first stage of accumulation, the low-amplitude hydrocarbon generation in the deep sag was supercharged, and the overpressured top seal was initially formed. Intermittent period of reservoir formation, overpressure top seal uplift erosion; During the rapid subsidence period, the strata are undercompacted to form overpressure, and the overpressure top seal is re-formed. During the second stage of storage, the thickness of overpressure top seal increased to the maximum.

Acetabular Edge Loading During Gait Is Elevated by the Anatomical Deformities of Hip Dysplasia.

Developmental dysplasia of the hip (DDH) is a known risk factor for articular tissue damage and secondary hip osteoarthritis. Acetabular labral tears are prevalent in hips with DDH and may result from excessive loading at the edge of the shallow acetabulum. Location-specific risks for labral tears may also depend on neuromuscular factors such as movement patterns and muscle-induced hip joint reaction forces (JRFs). To evaluate such mechanically-induced risks, we used subject-specific musculoskeletal models to compare acetabular edge loading (AEL) during gait between individuals with DDH (N = 15) and healthy controls (N = 15), and determined the associations between AEL and radiographic measures of DDH acetabular anatomy. The three-dimensional pelvis and femur anatomy of each DDH and control subject were reconstructed from magnetic resonance images and used to personalize hip joint center locations and muscle paths in each model. Model-estimated hip JRFs were projected onto the three-dimensional acetabular rim to predict instantaneous AEL forces and their accumulative impulses throughout a gait cycle. Compared to controls, subjects with DDH demonstrated significantly higher AEL in the antero-superior acetabulum during early stance (3.6 vs. 2.8 × BW, p ≤ 0.01), late stance (4.3 vs. 3.3 × BW, p ≤ 0.05), and throughout the gait cycle (1.8 vs. 1.4 × BW*s, p ≤ 0.02), despite having similar hip movement patterns. Elevated AEL primarily occurred in regions where the shallow acetabular edge was in close proximity to the hip JRF direction, and was strongly correlated with the radiographic severity of acetabular deformities. The results suggest AEL is highly dependent on movement and muscle-induced joint loading, and significantly elevated by the DDH acetabular deformities.

Hybrid deep learning architecture for rail surface segmentation and surface defect detection

AbstractRail surface defects (RSDs) are a major problem that reduces operation safety. Unfortunately, the existing RSD detection systems have very limited accuracy. Current image processing methods are not tailored for the railway track and many fully convolutional networks (FCN)‐based methods suffer from the blurry rail edges (RE). This paper proposes a new rail boundary guidance network (RBGNet) for salient RS detection. First, a novel architecture is proposed to fully utilize the complementarity between the RS and the RE to accurately identify the RS with well‐defined boundaries. The newly developed RBGNet injects high‐level RS object information into shallow RS edge features by a progressive fused way for obtaining fine edge features. Then, the system integrates the refined edge features with RS features at different high‐level layers to predict the RS precisely. Second, an innovative hybrid loss consisting of binary cross entropy (BCE), structural similarity index measure (SSIM), and intersection‐over‐union (IoU) is proposed and equipped into the RBGNet to supervise the network and learn the transformation between the input and ground truth. The input and ground truth then further refine the RS location and edges. Conveniently, an image‐based model for RSD detection and quantification is also developed and integrated for an automatic inspection purpose. Finally, experiments conducted on the complex unmanned aerial vehicle (UAV) rail dataset indicate the system can achieve a high detection rate with good adaptation capability in complicated environments.

Weakly Supervised Surface Defect Detection Based on Attention Mechanism

<p indent=0mm>Nowadays, many defect detection algorithms based on deep learning are trained using a supervised learning strategy, which largely depends on the number of required samples and the quality of annotations. A weakly supervised attention network for surface defect detection is proposed, named SDD-Net, which can simultaneously predict both the location and probability of defects only by using image-level labels. Firstly, the feature fusion module is used to extract multi-scale edges features from the output of the multi-scale receptive field. Then, the deep semantic information of features is mined by multilevel auto-coder. Meanwhile, the trilinear global context attention module is used to further refine the spatial location information of shallow features. Finally, the SDD-Net is used to integrate the shallow edge features and deep semantic features to obtain the final fine defect features. The results of evaluation on KolektorSDD dataset demonstrate that the SDD-Net based on the PyTorch framework has better detection performance than other detection methods such as U-Net. It can retain more detailed texture information and effectively expand the feature difference between small defects and complex background. The experimental results show that the proposed model is more accurate than the other models in the complex scene, the precision, the <italic>F</italic>₁-score and the classification accuracy are significantly improved.

STEG-Net: Spatiotemporal Edge Guidance Network for Video Salient Object Detection

Over the past decades, video salient object detection (VSOD) has achieved rapid development. However, the effect of edge information on spatial and temporal features extraction has not been explored. The method STEG-Net is proposed to solve the problem, we use the extracted edge information to guide the extraction of spatial and temporal features simultaneously. We combine deep texture information with shallow edge information, which can not only retain the edge of the object but also enhance the global information, leading to the more accurate location of the object. At the same time, the shallow edge information and the deep texture information are complementary. As a result, edge information and salient features could advance each other while extracting features. Finally, we evaluate the 16 state-of-the-art VSOD methods on ViSal and FBMS data sets. At the same time we also evaluate the 13 state-of-the-art VSOD methods on UVSD and MCL data sets. The experiment results indicate that the proposed method outperforms the state-of-the-art approaches.

Application of back-propagation neural networks in the distribution of oil sand reservoirs\u2014a case study from the western slope of the Songliao Basin, China

High-quality hydrocarbon source rocks are present in the upper Cretaceous layer in the western slope of the Songliao Basin. Oil and gas have accumulated in these rocks at the shallow edge of the basin, which has led to the formation of oil sand resources. This study uses the back-propagation (BP) neural network method to predict the distribution of oil sand reservoirs and is the first study of its kind in China. First, based on the basic data collected by core sample, well log and geochemical analyses, and the reasonable selection of samples, the cores are divided into mudstone, siltstone, fine sandstone, medium sandstone, and sand, according to lithology. Second, a three-layer BP neural network model is constructed with two hidden S-type layers and one linear output layer. Third, through a comparison of the effect of different numbers of training sessions of the sample data, we demonstrate that the accuracy of the model can be increased to 90% after training the network 100,000 times. Then, the log-derived data of rocks with unknown lithologies are input into the neural network to predict whether they contain oil sands. We show that the BP neural network method can predict the distribution of oil sand reservoirs in the target horizon of the study area, and the results are consistent with research results on the corresponding sand reservoirs and sedimentary facies. Thus, we conclude that it is feasible to use the BP neural network method to predict the distribution of oil sand reservoirs.

Lightweight Channel Attention and Multiscale Feature Fusion Discrimination for Remote Sensing Scene Classification

High-resolution remote sensing image scene classification has attracted widespread attention as a basic earth observation task. Remote sensing scene classification aims to assign specific semantic labels to remote sensing scene images to serve specified applications. Convolutional neural networks are widely used for remote sensing image classification due to their powerful feature extraction capabilities. However, the existing methods have not overcome the difficulties of large-scene remote sensing images of large intraclass diversity and high interclass similarity, resulting in low performance. Therefore, we propose a new remote sensing scene classification method that combines lightweight channel attention and multiscale feature fusion discrimination, called LmNet. First, ResNeXt is used as the backbone; second, a new lightweight channel attention mechanism is constructed to quickly and adaptively learn the salient features of important channels. Furthermore, we designed a multiscale feature fusion discrimination framework, which fully integrates shallow edge feature information and deep semantic information to enhance feature representation capabilities and uses multiscale features for joint discrimination. Finally, a cross-entropy loss function based on label smoothing is built to reduce the influence of interclass similarity on feature representation. In particular, our lightweight channel attention and multiscale feature fusion mechanism can be flexibly embedded in any advanced backbone as a functional module. The experimental results on three large-scale remote sensing scene classification datasets show that compared with the existing advanced methods, our proposed high-efficiency end-to-end scene classification method has reached state-of-the-art. Moreover, our method has a weaker dependence on labeled data and provided better generalization performance.

Influence of Fast Neutron Irradiation on the Electrical and Optical Properties of Li Doped ZnSnO Thin Film Transistor

The effects of fast neutron irradiation on the electrical and optical properties of Li (3 at%) doped ZnSnO (ZTO) thin films fabricated using a sol-gel process are investigated. From the results of Li-ZTO TFT characteristics according to change of neutron irradiation time, the saturation mobility is found to increase and threshold voltage values shift to a negative direction from 1,000 s neutron irradiation time. X-ray photoelectron spectroscopy analysis of the O 1s core level shows that the relative area of oxygen vacancies is almost unchanged with different irradiation times. From the results of band alignment, it is confirmed that, due to the increase of electron carrier concentration, the Fermi level (EF) of the sample irradiated for 1,000 s is located at the position closest to the conduction band minimum. The increase in electron concentration is considered by looking at the shallow band edge state under the conduction band edge formed by fast neutron irradiation of more than 1,000 s.

Improvement of Electrical Performance by Neutron Irradiation Treatment on IGZO Thin Film Transistors

The effects of the neutron irradiation treatment on indium-gallium-zinc oxide (IGZO) are investigated as a function of the neutron irradiation time. With an increase in neutron irradiation time, the oxygen vacancies associated the oxygen deficient states increase, and both shallow and deep band edge states below the conduction band also increase. Moreover, the conduction band offset continuously decreases because of the increase in the oxygen vacancies with increasing the neutron irradiation time. In IGZO TFTs with the neutron irradiation time for 10 s, superior device performance demonstrates such as the lower threshold voltage, higher field effect mobility, smaller sub-threshold gate swing, larger on-off current ratio, and improved bias stability, comparing those of other IGZO TFTs.