Dr Apj Abdul Kalam Images Download [UPDATED]

Mahatma Gandhi may soon no longer be the only personality on the Indian rupee banknote as the Reserve Bank of India is considering adding images of Rabindranath Tagore and APJ Abdul Kalam to the currency.

Shahani has been reportedly asked to choose from the sets and present them for government's consideration, and the decision to pick one or all the images on the banknotes will be taken at the "highest level," the report added.

Dr Apj Abdul Kalam Images Download

DOWNLOAD 🔥 https://shurll.com/2y5Hjw 🔥

Currently, some foreign currencies like the US dollar and Japanese yen carry the images of multiple prominent personalities through their country's history. The US, for instance, has photos of George Washington, Benjamin Franklin, Thomas Jefferson and Abraham Lincoln among others, while the yen carries images of bacteriologist Hideyo Noguchi, female writer Ichiyo Higuchi, and Yukichi Fukuzawa - though they are all slated for replacement in 2024.

Researchers at the NASA's Jet Propulsion Laboratory (JPL) had discovered a new bacterium on the filters of the International Space Station (ISS) and named it Solibacillus kalamii to honour the late president Dr. A. P. J. Abdul Kalam.[161]

This image is owned by National Repository of Open Educational Resources ( ). All material used by this site is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License unless mentioned otherwise. Permission is granted to copy, distribute and/or modify these images, providing the site is attributed and a direct link running to the source on their site is provided.

Very important: Before using this template on a newly uploaded image, please read the template documentation.

A photo through a post is being widely shared on social media claiming that RBI accepted the proposal to have the images of Tagore and Abdul Kalam alongside Mahatma Gandhi on the new currency notes. Let us fact-check the claim made in the post.

Fact: RBI issued a press release clarifying that there would be no change in existing currency and banknotes. There is no such proposal considering changes to the existing currency by replacing the face of Mahatma Gandhi by the Reserve Bank of India. There were reports that the RBI might consider using the images of Tagore and Abdul Kalam alongside Mahatma Gandhi on the new currency notes. But RBI clarified that there were no such proposals yet. Hence, the claim made in the post is FALSE.

RBI issued a press release clarifying that there would be no change in existing currency and banknotes. There is no such proposal considering changes to the existing currency by replacing the face of Mahatma Gandhi by the Reserve Bank of India. There were reports that the RBI might consider using the images of Tagore and Abdul Kalam alongside Mahatma Gandhi on the new currency notes. But RBI clarified that there were no such considerations.

An image processing-based method to count RBCs is proposed by Acharya & Kumar (2018), where normal and abnormal cells were identified for an input blood smear image. For this purpose, the K-medoids algorithm is used to extract WBCs from a blood smear image, and granulometric-based analysis is performed to separate WBCs and RBCs. Circular Hough Transform and labeling are used to count the number of cells. Another image processing-based RBC counting method is presented where thresholding is done for different pixels of HSV converted images (Cruz et al., 2017). Counting blood cells is done using the connected component labeling. Circular Hough transform (Acharjee et al., 2016), CHT (Sarrafzadeh et al., 2015), SVM, and spectral angle imagining (Lou et al., 2016) are also used to count different blood cells. ResNet and Inception net-based pre-trained models were used to count WBC from the image after segmentation using color space analysis (Habibzadeh et al., 2018). An iterative circle detection algorithm is also used to find RBCs and WBCs (Alomari et al., 2014). Another method has been presented for segmenting and counting RBCs using pulse-coupled neural networks (Ma et al., 2016). Counting cells is based on the average size of RBC, which can create mismatching in some cases. The blood cell counting using different deep learning models is compared on BCCD datasets which achieved the highest mean average precision (mAP) of 74.37% (Alam & Islam, 2019).

The main contribution of this paper is an automatic blood cell detection and counting framework. The dataset of 364 images having 4888 blood cells is labeled and divided into training, validation, and test set. The whole dataset is used to train convolutional neural networks in different batch sizes. The performance of the trained model is analyzed in different parameters. Bounding boxes were made around the detected blood cells. Mean average precision is high. The average precision of WBCs is more than 97% for all models. The counting error is much less for detected blood cells with a detection threshold of 0.9, and WBCs cells give 100% accurate results on counting. The detection time for processing one blood smear image of size 640480 pixels takes

In this study, the blood cell images database was collected from an opensource repository; it is the Blood Cell Count Dataset (BCCD), which has 364 images of blood cells. These images contain 4888 different cells: 4155 RBCs cells, 372 WBCs cells, and 361 platelets (BCCD, 2020). Figure 1 shows RBCs, WBCs, and platelets on an input blood smear image. The deep learning model is trained with 256 images, and the model is validated with 54 blood cell images. Once the model is generated, it is tested with 54 blood cell images having 800 different blood cells, and counting is performed.

The input blood cell images are divided into N X N grids. Grid cells are responsible for detecting objects if the centers of the objects lie in those grid cells: they predict bounding boxes and determine the confidence score associated with those boxes. YOLO-v3 calculates bounding boxes on three different scales like features pyramid network (Lin et al., 2017), and these prediction results are more significant for detecting minor-sized blood cell targets. The algorithm was already trained on COCO datasets (Lin et al., 2014) and implemented using pre-trained weights. Predictions were made on three different scales in the proposed work, as shown in Fig. 3. Thus, an input smear image of 416 x 416 dimension was divided into grids of 13 13, 26 26, and 52 52 for the respective stride values of 32, 16, and 8. The confidence score describes the confidence of the model that the object lies in the box and the accuracy of the box it predicted. Each grid in the input image predicts B bounding boxes with confidence scores and C class conditional probabilities. The confidence score formula is given in equation 1:

A convolutional neural network needs to be trained and tested to develop a framework for blood cell detection. For this purpose, an Intel Xenon processor with 64 GB RAM and an NVIDIA Quadro P600 graphics processing unit (GPU) with 24 GB graphics memory are used. Image pre-processing, training, and testing are done on Anaconda3 (Python 3.7), and other libraries, such as TensorFlow and OpenCV, required to train the model. The statistics of the number of images in the datasets are given in Table 1.

A dataset from all sources is divided for training, testing, and validation in 70%, 15%, and 15%, respectively. The deep learning model is trained with blood cell images containing 4888 cells for several iterations until the loss becomes saturated. Generated trained models are analyzed with multiple images in test datasets to obtain overall performance. Yolo-v3 based convolutional neural network is trained for 400 epochs with an initial learning rate of 10-3 and IoU of 50%.

The training is done for different batch sizes of 4, 8, and 16 images. Its steps in each epoch are 512, 256, and 128 while training in a batch size of 4, 8, and 16, respectively. Once trained, models are tested with never-seen blood cell smear images. Some of the tested results are shown in Figure 4, where various blood cells are detected with a higher percentage using the proposed framework.

Different batches achieve the highest accuracy for different cells. Batch size 8 gives the highest AP for WBC and RBC. Instead, batch 16 returns the highest AP for platelets and the highest mAP value that is 85.35% on 0.5 IoU. The performance of the deep learning model depends on the detection and counting of different cells from the input blood cell image. The statistical analysis is done to count different blood cells where the counting of RBC, WBC, and platelets is compared with the ground truth value of different samples. For WBC counting with all three trained CNN models, accurate results reached 100%. The counting range for RBC is -6 to 7 for different batches. Table 3 shows the counting error (CE) for different blood cells in various error ranges; it also displays most test images that gave zero errors.

This paper proposes the robust and fast detection and counting of different blood cells. The images of different blood cells with respective labels are provided to train the deep learning model with multiple parameters. The trained model is analyzed on different parameters. The results show high accuracy while detecting and counting the blood cells.

The proposed framework can scan the image in three different scales, making it easy to detect the small-sized blood cells in an input image frame. Average precision ranges from 0.70 to 0.991 with a mean average precision value of 0.8535. The input images frames were processed very fast; the resulting count of different cells can be utilized by doctors to find disorders based on that report. The complete framework is automatic, and multiple images frames can be processed subsequently to generate the report. The proposed automated framework is significantly more accurate and faster than the traditional methods used by pathologists. 17dc91bb1f