Analysis of ITU-R Performance and Characterization of Ku Band Satellite Downlink Signals during Rainy Season over Chennai Region of India

In this paper, we present the analysis of Ku band Satellite signal reception during rainy season over Chennai region, India (Latitude: 12° 56' 60 N, Longitude: 80° 7' 60 E). We also examine the effectiveness of International Telecommunication Union – Radio communication (ITU-R) model in predicting the rainfall induced attenuation in Ku band, over this region. An improved Simulink model for Digital Video Broadcast – Satellite (DVB-S2) downlink channel incorporating rain attenuation and Cross Polarization Discrimination (XPD) effects is developed to study the rain attenuation effects, by introducing the experimental data in the ITU-R model pertaining to that region. Based on the improved model, a Monte Carlo simulation of the DVB–S2 signal link is carried out and the performance is analyzed by received constellation and Bit Error Rate (BER) parameters

Correlative study of Clinical Findings, Audiological Evaluation and Peroperative findings in patient with Conductive Hearing Loss

Conductive hearing loss occurs when sound conduction is impaired as a result of pathology in the external or middle ear. The external ear includes the pinna, which is receptacle of sound and the external auditory canal through which sound passes onto the tympanic membrane. The middle ear is a space that has laterally tympanic membrane and medially cochlea connected by ossicular chain that helps transmit sound optimally. In this study we are going to correlate preoperative clinical findings, audiological evaluation with peroperative findings of patients presents with conductive hearing loss who came to our institution. AIMS AND OBJECTIVES: 1. To evaluate clinical findings in patients with conductive hearing loss. 2. To evaluate audiological findings in same patients. 3. To correlate above findings with peroperative findings, ossicular chain status and disease process in middle ear and mastoid ear cell system. MATERIALS AND METHODS: This is a retrospective and prospective study carried over period from november 2014 to august 2016. Total number of patients included in this study was 80. RESULTS: Out of 80 patients studied 68.8% had tympanic membrane perforation. 31.3% had retracted TM. 32.5% had Active COM . 36.3% had inactive COM. In perforation group, 12.5% had anterior perforation, 10% had posterior perforation,26.3% had central perforation, 20% had subtotal perforation. In retracted TM group, 25% had adhesive otitis media, 2.5% had attic retraction, 3.8% had posterosuperior retraction. Hearing loss found to be more in patients with active COM with subtotal perforation (43.03db). Overall 17 patients (21.25%) had ossicular involvement. Most commonly involved ossicle was incus. CONCLUSION: Most common cause of conductive hearing loss in our study was chronic suppurative otitis media. Most of the patients had central perforation followed by subtotal perforation. In perforated group, hearing loss was more in Chronic active otitis media with subtotal perforation and these group of patients had more ossicular involvement. It shows patients presents with chronic active otits media with subtotal perforation will have high chance of ossicular involvement compared to chronic inactive otitis media due to these group of patients have more inflammation in middle ear and mastoid & more inflammatory infiltrates. Retracted group of patients should be evaluate more carefully, because of their audiological findings and clinical findings doesn’t correlate with intraoperative findings. Overall, patients with Active COM with subtotal perforation had more hearing loss and more ossicular involvement because of their active disease process and duration of complaint. Most of the patients had mild CHL (26-40 DB)(70%). We can assess the patient with conductive hearing loss pre operatively by using clinical findings & audiological methods which mostly correllates with peroperative findings

IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION

This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot

Comparative histology of human and cow, goat and sheep liver.

Comparative histology deals with the comparison of microscopic structural relations of the various animals with in the ecosystem. Here, we compare the microscopic structure of the human liver with domestic animals like cow, sheep and goat. Human and cow, goat and sheep’s liver were taken and divided in to 3 groups. We kept liver specimen in formalin for fixation. Thin cut sections of specimen were taken after paraffin embedding. Slides were stained by Haematoxylene and Eosin, later observed the histological features under light microscope. The study was undertaken to compare the histological differences like hepatic lobule, connective tissue septa, portal triad, hepatocytes of liver between human and cow, goat and sheep. It plays a useful tool for morphological studies based on the evolution. Hepatic lobule was hexagonal in shape in cow, goat and sheep, but it was not clearly seen in human liver. Hepatocytes were larger in human beings but smaller and polygonal in cow, goat and sheep. Connective tissue septa were scanty in human liver, in comparison to other animals. Central vein was closer to the hepatic lobule in human and goat’s liver, while in case of cow and sheep, it was found to be close to the portal triad. This comparative histological study may be useful to all the research scholars who undertaken similar studies, veterinary scientists and the field of liver transplantation

Minimizing Energy Consumption Using Internet of Things

Now a days using of internet is growing faster. All things are connected using internet. Internet of things means connecting all the devices using internet. These issues become crucial in large scale of IoT environments which are composed of thousands of distributed devices. The more number of distributed systems consumes more amount of energy. This paper is to minimize energy during data transfer and to minimize loss of packets and time delay. In this we are using Ant Colony Optimization algorithm for clustering the data nodes and transferring data with less energy consumption

MULTIVARIATE CALIBRATION TECHNIQUE FOR THE SPECTROPHOTOMETRIC QUANTIFICATION OF IVERMECTIN IN PHARMACEUTICAL FORMULATION

Objective: The present abstract makes the use of multivariate calibration technique for the quantification of ivermectin in pharmaceutical dosage form. Methods: Multivariate calibration technique is based on the use of linear regression equations, by correlating the relation between concentration and absorbance at seven different selected wavelengths. The λmax of ivermectin was found to be 245 nm. The results were treated statistically. This statistical approach gives optimum results by eliminating the fluctuations arising from the instrumental or experimental conditions. Results: The developed method was validated as per the ICH guidelines and was found to be simple, linear, accurate, precise, and reproducible. The method was found to be linear over a concentration range of 5–15 μg/mL with a correlation coefficient (r2) value of about 0.9998. The limit of detection and quantification were found to be 0.029 and 0.087 μg/mL, respectively. The percentage relative standard deviation for intraday and interday precision was found to be in the range of 0.473–1.373 and 0.301–1.617, respectively. The percentage recovery was found within the range of 97.60–101.80% w/w. Conclusion: The results evidence that a simple, linear, precise, accurate, sensitive, and reproducible multivariate calibration technique has been developed and validated for the quantification of ivermectin in bulk and pharmaceutical formulation

The salivary biomarkers: future clinical investigation technique

Human saliva is a clear, slightly acidic biological fluid containing a mixture of secretions from multiple salivary glands, including the parotid, sublingual gland other minor glands beneath the oral mucosa as well as gingival crevice fluid.  Salivary diagnostics has evolved into a sophisticated science and serves as a subset of the larger field of molecular diagnostics, now recognized as a central player in a wide variety of biomedical basic and clinical areas.  Saliva biomarkers are source of indicators for local, systemic, and infectious disorders. The saliva based microbial, immunologic, and molecular biomarkers offers unique opportunities to bypass the painful invasive procedures such as biopsies and repeated blood draws by utilizing oral fluids to evaluate the condition of diseased individuals. Accurate and reliable early stage disease detection is the benefit of salivary biomarkers. Salivary biomarkers represent a promising non-invasive approach for oral cancer detection also. This review explains about the salivary biomarkers and their diagnostic approache

Some derivations among Logarithmic Space Bounded Counting Classes

In this paper we show derivations among logarithmic space bounded counting classes based on closure properties of $\#L$ that leads us to the result that $NL=C_=L\subseteq PL$.Comment: 3 page

An Intelligent Dog Breed Recognition System Using Deep Learning

Image processing has been getting great attention recently in the field of machine learning and deep learning. This technique can be used to process an image in such a way that the computer understands the features of the image and classifies it. Our study focuses on building an efficient CNN model to predict the breed of the dog using its image, giving the best accuracy possible with the least amount of computing resources involved. This CNN model is deployed on cloud service, Google App Engine which identifies certain characteristics or features in an image such as the paw, nose, stout, and ears of a dog, employing a dataset containing 10222 images of different dog breeds or classes of dogs and opening a wide scope for future developments