Dual-polarized chipless humidity sensor tag

In this letter, a miniaturized, flexible and high data dense dual-polarized chipless radio frequency identification (RFID) tag is presented. The tag is designed within a minuscule footprint of 29 × 29 mm2 and has the ability to encode 38-bit data. The tag is analyzed for flexible substrates including Kapton® HN DuPont™ and HP photopaper. The humidity sensing phenomenon is demonstrated by mapping the tag design, using silver nano-particle based conductive ink on HP photopaper substrate. It is observed that with the increasing moisture, the humidity sensing behavior is exhibited in RF range of 4.1–17.76 GHz. The low-cost, bendable and directly printable humidity sensor tag can be deployed in a number of intelligent tracking applications

Polarization Insensitive Compact Chipless RFID Tag

This research article proposes a highly dense, inexpensive, flexible and compact 29 x 29 mm(2) chipless radio frequency identification (RFID) tag. The tag has a 38-bit data capacity, which indicates that it has the ability to label 238 number of different objects. The proposed RFID tag has a bar-shape slot/resonator based structure, which is energized by dual-polarized electromagnetic (EM) waves. Thus, portraying polarization insensitive nature of the tag. The radar cross-section (RCS) response of the proposed tag design is analyzed using different substrates, i.e., Rogers RT/duroid (R)/5880, Taconic (TLX-0), and Kapton (R) HN (DuPont (TM)). A comparative analysis is done, which reveal the changes observed in the RCS curve, as a result of using different substrates and radiators. Moreover, the effect on the RCS response of the tag is also examined, by bending the tag at different bent radii. The compactness and flexible nature of the tag makes it the best choice for Internet of things (IoT) based smart monitoring applications

Explainable Neural Network for Classification of Cotton Leaf Diseases

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods

A Review on Recent Developments for Detection of Diabetic Retinopathy

Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area

Miniaturized humidity and temperature sensing RFID enabled tags

A compact 27-bit linearly polarized chipless radio frequency identification tag is presented in this research. The proposed tag is designed with an overall tag dimension of 23 × 23 mm2. The tag comprises of metallic (copper) rings-based structure loaded with slots. These slots correspond to a particular sequence of bits. The circular tag is analysed using 2 different substrates, that is, Rogers RT/duroid/5870 and flexible Rogers RT/duroid/5880. The radar cross-section response of frequency signatured tag is analysed for humidity and temperature sensor designs. Humidity sensing is achieved by deploying a DuPont Kapton HN heat resistant sheet on the shortest slot of the tag, that is, the sensing slot. Temperature sensing is attained using Rogers RT/duroid/5870 and Stanyl polyamide as a combined substrate. Hence, the miniaturized, robust, and flexible tag can be deployed over irregular surfaces for sensing purposes

Explainable Neural Network for Classification of Cotton Leaf Diseases

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods

Visual Geometry Group based on U-Shaped Model for Liver/Liver Tumor Segmentation

Liver cancer is the primary reason of death around the globe. Manually detecting the infected tissues is a challenging and time-consuming task. The computerized methods help make accurate decisions and therapy processes. The segmentation accuracy might be increased to reduce the loss rate. Semantic segmentation performs a vital role in infected liver region segmentation. This article proposes a method that consists of two major steps; first, the local Laplacian filter is applied to improve the image quality. The second is the proposed semantic segmentation model in which features are extracted to the pre-trained VGG16 model and passed to the U-shaped network. This model consists of 51 layers: input, 23 convolutional, 4 max pooling, 4 transpose convolutional, 4 concatenated, 8 activation, and 7 batch-normalization. The proposed segmentation framework is trained on the selected hyperparameters that reduce the loss rate and increase the segmentation accuracy. The proposed approach more precisely segments the infected liver region. The proposed approach performance is accessed on two datasets such as 3DIRCADB and LiTS17. The proposed framework provides an average dice score of 0.98, which is far better compared to the existing works

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works

Maternal near miss, mortality and their correlates at tertiary care hospital

OBJECTIVE:  To determine the frequency and causes of maternal near miss and mortality among pregnant women. METHODS: This cross-sectional study was conducted Jan 2016 - Dec 2018. All near miss cases, admitted in Gynecology department of Services Hospital Lahore during the study period, were prospectively recruited. WHO criteria was used to identify maternal near miss cases. Primary outcome measures were frequency and causes of near miss and maternal mortality to near miss ratio. Secondary outcome measures were delays, need for massive blood transfusion, ICU admission, obstetric hysterectomy and hospital stay> 7 days. RESULTS: During the study period, there were 10,739 live births, 305 near miss cases and 29 maternal deaths. Frequency of near miss was 28.4/ 1000 live births and maternal mortality to near miss ratio was 1:10.5. There were 215(70.4%) unbooked patients and 23(79.3%) of them died (p<0.001). Hemorrhage accounted for 150 (49.18%), hypertensive disorders 102 (33.44%),cardiac disease 25 (8.28%) and infection for 12 (3.97%) near miss cases respectively. Maternal mortality was significantly low for hemorrhage, hypertension, sepsis and cardiac disease; 6 vs 150, 8 vs102, 3vs 12 and 10 vs 25 respectively (p<0.001). Massive blood transfusion was given to 20.98%patients, 15.74% underwent hysterectomy, 32.13% required ICU admission. First and second delay was seen in 78.6% of patients with 86.2% deaths (p<0.001) CONCLUSION: Hemorrhage and hypertension are major reasons for near miss but timely intervention can prevent mortality. Strengthening care at primary and secondary level can reduce the burden of maternal morbidity.  Continuous...