Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.

Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.

Effect of Low Temperature on Chlorophyll and Carotenoid Content on the Seedlings of Some Selected Boro Rice Varieties

A study was conducted to screen out the low-temperature tolerant Boro rice seedlings from November 2012 to January 2013 for facing the upcoming rice production challenge in Northwest Bangladesh. The experimental time was characterized by a prevailing low environmental temperature of below 15°C. Five rice cultivars (V1: BR-2;V2: BR-16;V3: Pariza;V4: Minicate;V5: BRRI dhan 50) were selected for the study. The leaf proline, chlorophyll content and total carotenoid content were investigated. The V2 (BR-16) seedling synthesized the higher leaf proline (1.228 g·g-1) at a low temperature than those of other tested cultivars. Again, the highest amount of chlorophyll-a (3.957 g·g-1), chlorophyll-b (2.118 g·g-1), chlorophyll-a/b ratio (3.6754 mg·g-1) and total chlorophyll (5.051 g·g-1) was measured in V2 (BR-16). The maximum total carotenoid (1.213 g·g-1) was also observed in V2. In this experiment, the V2 (BR-16) showed comparatively better potentiality to survive at low temperatures (below 15°C) than other varieties.

Design of a Porous Cored Hexagonal Photonic Crystal Fiber Based Optical Sensor With High Relative Sensitivity for Lower Operating Wavelength

In this article, highly sensitive and low confinement loss enriching micro structured photonic crystal fiber （PCF） has been suggested as an optical sensor. The proposed PCF is porous cored hexagonal （P-HPCF） where cladding contains five layers with circular air holes and core vicinity is formed by two layered elliptical air holes. Two fundamental propagation characteristics such as the relative sensitivity and confinement loss of the proposed P-HPCF have been numerically scrutinized by the full vectorial finite element method （FEM） simulation procedure. The optimized values are modified with different geometrical parameters like diameters of circular or elliptical air holes, pitches of the core, and cladding region over a spacious assortment of wavelength from 0.8 ktm to 1.8 -m. All pretending results exhibit that the relative sensitivity is enlarged according to decrement of wavelength of the transmission band （O＋E＋S＋C＋L＋U）. In addition, all useable liquids reveal the maximum sensitivity of 57.00%, 57.18%, and 57.27% for n=1.33, 1.354, and 1.366 respectively by lower band. Moreover, effective area, nonlinear coefficient, frequency, propagation constant, total electric energy, total magnetic energy, and wave number in free space of the proposed P-HPCF have been reported recently.

Alcohol Sensing Over O＋E＋S＋C＋L＋U Transmission Band Based on Porous Cored Octagonal Photonic Crystal Fiber

A micro structure porous cored octagonal photonic crystal fiber （P-OPCF） has been proposed to sense aqueous analysts （alcohol series） over a wavelength range of 0.80 μm to 2.0 μm. By implementing a full vectorial finite element method （FEM）, the numerical simulation on the proposed O-PCF has been analyzed. Numerical investigation shows that high sensitivity can be gained by changing the structural parameters. The obtained result shows the sensitivities of 66.78%, 67.66%, 68.34%, 68.72%, and 69.09%, and the confinement losses of 2.42×10^-10 dB/m, 3.28x×10^-11 dB/m, 1.21 ×10^-6 dB/m, 4.79×10^-10 dB/m, and 4.99×10^-9 dB/m at the 1.33 ktm wavelength for methanol, ethanol, propanol, butanol, and pentanol, respectively can satisfy the condition of much legibility to install an optical system. The effects of the varying core and cladding diameters, pitch distance, operating wavelength, and effective refractive index are also reported here. It reflects that a significant sensitivity and low confinement loss can be achieved by the proposed P-OPCE The proposed P-OPCF also covers the wavelength band （O＋E＋S＋C＋L＋U）. The investigation also exhibits that the sensitivity increases when the wavelength increases like SO-band〈SE-band 〈SS-band 〈 SC-band 〈SL-band 〈SU-band. This research observation has much pellucidity which has remarkable impact on the field of optical fiber sensor.

Design and Optimization of Photonic Crystal Fiber for Liquid Sensing Applications

这份报纸为察觉到的液体与高相对的敏感建议六角形的 photonic 水晶纤维(H-PCF ) 结构；在哪个核心和 cladding 是微观结构。数字调查被采用完整的 vectorial 执行有限元素方法(女性) 。分析在建议结构的四个阶段被做了。调查证明建议结构由在 cladding 增加最深处的戒指空气洞的直径完成更高相对的敏感。而且，把一条单个隧道放而不是使用一组微小的隧道有效地增加相对敏感。调查不同参数的效果，优化结构与低监禁损失显示出显著地更高的相对敏感。

Design and Optimization of Photonic Crystal Fiber Based Sensor for Gas Condensate and Air Pollution Monitoring

In this paper, a hexagonal shape photonic crystal fiber （H-PCF） has been proposed as a gas sensor of which both micro-structured core and cladding are organized by circular air cavities. The reported H-PCF has a single layer circular core which is surrounded by a five-layer hexagonal cladding. The overall pretending process of the H-PCF is completed by using a full vectorial finite element method （FEM） with perfectly matched layer （PML） boundary condition. All geometrical parameters like diameters and pitches of both core and cladding regions have fluctuated with an optimized structure. After completing the numerical analysis, it is clearly visualized that the proposed H-PCF exhibits high sensitivity with low confinement loss. The investigated results reveal the relative sensitivity of 56.65% and confinement loss of 2.31×10^-5 dB/m at the 1.33%tm wavelength. Moreover, effective area, nonlinearity, and V-parameter of the suggested PCF are also briefly described.

Single polarization photonic crystal fiber filter based on surface plasmon resonance

In this paper,we propose a photonic crystal fiber(PCF)polarization filter based on surface plasmon resonance(SPR)characteristics.Gold nanowire is used as the active plasmonic material.Light into silica core becomes coupled to gold nanowire stimulating SPR. It soplits light into two orthogonal(x-polarization and y-polarization)polarization in the second order of surface plasmon polarization.Numerical investigations of the proposed PCF filter is finite element method(FEM).By position,the performance of the propsed PCF filter is inspected rigorously.Filtering of any polarization can be obtained by properly placing the metal wires.The maximum cnfinement loss of x-polarization is 692.25dB/cm and y-polarization is 1.13dB/cm offers at resonance position 1.42μm.Such a confinement loss difference between two orthogonal polarizations makes PCF a talented candidate to filter devicees.Consequently,the recommended PCF structure is useful for polarization device.

Benzene Shape Photonic Crystal Fiber Based Plasma Sensor： Design and Analysis

新奇的苯核心 photonic 水晶纤维(BC-PCF ) 为察觉到应用的血浆被建议。建议 BC-PCF 参数被调节了获得高敏感，高数字的孔(NA ) ，和低监禁在 0.7 的广泛的变化上的损失，和形式 ? 敲?瑬 ? 桳睯琠慨 ? 桴 ? 桰獡 ? 敲瑳牯瑡潩 ? 牥潲 ? 獩愠灰潲楸慭整祬瀠潲潰瑲潩慮? 潴琠敨瀠慨敳猠楨瑦攠牲牯愠摮稠牥瑯 ? 牯敤 ? 晥敦瑣 ? 桷湥琠敨瀠慨敳猠楨瑦湩 ? 牥潲 ? 獩氠獥 ? 桴湡㈠ ? 廴 ? 廴吗？？