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1.
针对高帧频、全局曝光和光谱平坦等成像应用需求,设计了一款高光谱成像用CMOS图像传感器。其光敏元采用PN型光电二极管,读出电路采用5T像素结构。采用列读出电路以及高速多通道模拟信号并行读出的设计方案来获得低像素固定图像噪声(FPN)和非均匀性抑制。芯片采用ASMC 0.35μm三层金属两层多晶硅标准CMOS工艺流片,为了抑制光电二极管的光谱干涉效应,后续进行了光谱平坦化VAE特殊工艺,并对器件的光电性能进行了测试评估。电路测试结果符合理论设计预期,成像效果良好,像素具备积分可调和全局快门功能,最终实现的像素规模为512×256,像元尺寸为30μm×30μm,最大满阱电子为400 ke^(-),FPN小于0.2%,动态范围为72 dB,帧频为450 f/s,相邻10 nm波段范围内量子效率相差小于10%,可满足高光谱成像系统对CMOS成像器件的要求。 相似文献
2.
基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数,这将增加网络的深度,使得运行网络花费大量的时间,从而很难将模型部署到现实生活中。为了解决上述问题,本文设计一种轻量级超分辨率网络,对中间特征的关联性及重要性进行学习,且在重建部分结合高分辨率图像的特征信息。首先,引入层间注意力模块,通过考虑层与层之间的相关性,自适应地分配重要层次特征的权重。其次,使用增强重建模块提取高分辨率图像中更精细的特征信息,以此得到更加清晰的重建图片。通过大量的对比实验表明,本文设计的网络与其他轻量级模型相比,有更小的网络参数量,并且在重建精度和视觉效果上都有一定的提升。 相似文献
3.
北海道では1980年代から2010年代まで,水稲圃場栽培期間である5–9月の気温は年代とともに上昇した。そこで,直近の2010年代(2010—2019年平均)と比べて, 2つの2030年代の予測気象から,既報の関係式より水稲生育を予測した。その結果,2030年代では2010年代に比べ,限界移植日(移植早限)が水稲栽培17地域の平均で8~9日早い。また,早限出穂期が1~5日早く,晩限出穂期が1~5日遅く,安全出穂期間が2~10日長い。出穂期は1~3日早い。出穂期から晩限出穂まで2~9日長いため,遅延型冷害の発生がやや少ない。生育期別気象は,出穂前24日以降30日間では生育が早いため平均気温が同じかやや低い。出穂前10日以降40日間および出穂期以降40日間では平均気温がやや高く,日射量はやや少ない。そのため,玄米収量は96~98%とやや低く,潜在収量性を示す気候登熟量示数は同じである。障害不稔発生に関係する穂ばらみ期冷害危険期の平均気温はわずかに低いかほぼ同じであるため,冷害発生の危険性は残る。一方,不稔発生をもたらす低温域の出現頻度には,地域間で差異がある。精米蛋白質含有率は同じであるが,アミロース含有率はやや低く,やや良食味である。米粒外観品質では被害粒歩合と着色粒歩合は一定の傾向がなく,未熟粒歩合はやや高い。精米白度は同じであるが,玄米白度はやや高い。以上の予測に対する技術的対応方向を示した。 相似文献
4.
This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa. 相似文献
5.
6.
The aim of this study was to identify the textural features of apple seeds with the highest discriminatory power for distinguishing the seeds of different apple cultivars with the use of discriminative classifiers. The seeds of apple cvs. Gala, Jonagold and Idared were scanned with the use of a flatbed scanner, and the acquired images were processed to calculate textural features from color channels: L, a, b, R, G, B, Y, U, V, H, S, I, X, Y and Z. The selected textures were used to develop discriminative models and distinguish the seeds of the examined apple cultivars. The analyses were performed for color spaces and color channels. The seeds of apple cvs. Gala and Idared were discriminated with 100% accuracy in models based on the textures from Lab and YUV color spaces and color channel L for the Naive Bayes, Multilayer Perceptron and Multi Class classifiers. The discriminatory accuracies of the seeds of all analyzed apple cultivars (Gala, Idared and Jonagold) ranged from 72% to 85%. The discriminatory accuracy of the textures selected from Lab color space for the Naive Bayes classifier reached 85%. The seeds of apple cvs. Gala and Jonagold were discriminated with 78–90% accuracy, and the discriminatory accuracy of the textures from Lab color space and color channel b for the Naive Bayes classifier reached 90%. The seeds of apple cvs. Idared and Jonagold were distinguished with 80–94% accuracy. The models based on textures from Lab color space and color channel b for the Naive Bayes classifier were characterized by 94% discriminatory accuracy. The study demonstrated that textural features are useful for discriminating the seeds of different apple cultivars. 相似文献
7.
针对聚晶金刚石复合片(PDC)钻头在钻井破岩过程中因受力不平衡,导致钻井倾斜、井径扩大、钻头产生横向振动和涡动,造成钻头早期失效的问题,基于已有PDC钻头切削力学知识,构建了PDC钻头弯曲力矩及全局力平衡布齿优化设计模型,并提出模型求解方法。布齿设计实例结果表明,采用全局力平衡布齿设计方法得到的布齿结构可使PDC钻头在不同进尺条件下均能保持全局力平衡状态,尤其低进尺(小于1 mm)时的力平衡状态得到极大改善,提高了PDC钻头的钻井稳定性,对于改善钻头受力、提升钻头使用寿命、提高钻井质量与效率具有重要意义。 相似文献
8.
Julie Babyar 《Drug development and industrial pharmacy》2018,44(11):1868-1870
Substandard, falsified, and counterfeit medications are a concern for the industry and for the public’s health. Data collection and research on these illegitimate drugs must continue to be a priority and common recommendations must be implemented immediately. The health of the public continues to be at risk, and as such global consensus and collaboration must be swift. Process improvements and policy decisions can support expert recommendations while technology and research continue to drive home change. Addressing counterfeit and substandard drugs is cost effective, feasible, and the right thing to do. 相似文献
9.
Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset. 相似文献
10.
This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size. 相似文献