Energy efficient routing in wireless sensor network based on mobile sink guided by stochastic hill climbing

In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio

Study of Electron Transfer between Amines and Biologically Active 4 - Aryloxymethylcoumarin

Electron transfer from aliphatic and aromatic amines to biologically    active    4-aryloxymethyl    coumarin1-(4- iodophenoxymethyl)-benzo[ f ]coumarin (1IPMBC)    has been investigated in acetonitrile solvent. The variation of quenching rate parameter with reduction potential of amines indicates the electron transfer from amines to investigated      coumarin molecule.Experimentally determined values of quenching rate parameter k q are well correlated with the standard free energy changes ( ∆G 0 ) within the framework of Marcus electron transfer theory. In the investigatedsystems,solvent reorganization energy appears to play a major role in governing electron transfer dynamics

COMPARATIVE ESTIMATION OF SALIVARY TOTAL ANTIOXIDANT CAPACITY IN PERIODONTAL HEALTH AND CHRONIC PERIODONTITIS - A PILOT STUDY

Objective: Gram-negative bacteria provoke polymorphonuclear leukocyte (PMN) to release reactive oxygen species in chronic periodontitis (CP). Inability to maintain a balance between oxidative stress and antioxidant levels makes patients more susceptible to periodontal disease. The present study aims to estimate and compare salivary total antioxidant capacity (TAOC) in subjects with clinically healthy periodontium and patients with CP.Methods: After fulfilling the selection criteria, a total of 20 subjects (10 with clinically healthy periodontium and 10 with CP) were subjected to unstimulated salivary sample collection for biochemical estimation of TAOC by spectrophotometric assay using Kovacevic method. Analysis of data was done with unpaired student t-test, using SPSS version 22 statistical program.Results: Salivary TAOC was significantly higher in subjects with clinically healthy periodontium compared to CP patients. It was statistically significant (p<0.001).Conclusion: This study indicated increased levels of salivary TAOC in patients with CP compared to clinically healthy periodontium. Alteration in defensive antioxidant status could be a risk factor in the progression of periodontal disease

A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis

Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques

Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student's t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100.log(10 )(SigFea /root 2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset

Experimental Studies on Current, Susceptance, Impedance and Electrical Modulus of Polypyrrole/Molybdenum Trioxide Composites

Polypyrrole/molybdenum trioxide composites (PPy/MoO3) were synthesized by chemical oxidation method, which involved the polymerization of pyrrole (PPy) with molybdenum trioxide (MoO3). This process involved ammonium persulphate which acted as an oxidizing agent.Diverse compositions of MoO3 such as 10, 20, 30, 40 and 50 wt. % in pyrrole were used to synthesize PPy/MoO3 composites.Scanning Electron Microscopy (SEM) image of the above composites has revealed the presence of multiple phases comprising of MoO3 particles embedded in PPy chain. The observed increase in current could be due to hopping of a large number of charge carriers between favorable localized sites and is attributed to change in the distribution pattern of MoO3 particles.The present study also involved the measurement of susceptance, impedance and electrical modulus, and has disclosed the major influence of dimensions of MoO3 particles present in the matrix on all the properties. The composites discussed in the present study may throw some light on their applications in various areas namely humidity sensor, micro power generator, dielectrics and as semiconductors

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

A New U-Net Based License Plate Enhancement Model in Night and Day Images

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas