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Today’s Marine industries are undergoing transformation because of rapid growth of advancement in the field of automation. Shipping industries use hybrid propulsion systems to de-carbonize and orient the path towards zero emission. The renewable energy supply (RES) is utilized by reducing the dependence on imported conventional fossil fuels; greenhouse gas emissions produced by the usage of fossil fuels are reduced. Renewable green energy is used to generate power at the distribution level. Energy sources are distributed around the world. The utility's hybrid (wind/solar) power system has proven to be a reliable source of energy. In this article, PV and wind (hybrid) power used for marine applications with the reduction of fuel consumption is proposed. The hybrid buck boost converter used for regulating DC output voltage. A multi-level H bridge inverter between DC-DC converter and load provides the load's ac voltage requirement in hybrid systems. For a given output waveform quality, MLI topology provide lower THD and EMI output, higher efficiency and better output waveform. In order to design a multilevel inverter, a cascaded H-Bridge structure was adopted. PWM (Pulse Width Modulation) techniques enable the operation of Cascaded H Bridges to generate an approximate sine wave output from a multilayer inverter. To improve the hybrid system's performance, output of converter is supplied to the thirteen level H bridge inverter. This combination can maintain the appropriate voltage to load ratio. Voltage profile is improved by using H-bridge multilevel inverter. The proposed framework is re-enacted utilizing MATLAB/Simulink.

The in-vessel composting is considered as an environmentally sound method for the treatment and disposal of Municipal Solid Waste (MSW) as it produces good quality compost with lesser environmental impacts. In this study, the process was accelerated and the composting time was reduced using various industrial organic wastes as inoculums in a vertical in-vessel reactor with stirrer arrangement. This study examined the process kinetics which aims to show the interdependency between biological, chemical & physical factors. It is also the extension of the previous work which dealt with composting of the organic fraction of municipal solid waste. The rate of degradation of volatile solids, TOC and C/N ratio are presented in First-order kinetic model and reaction rate constants are determined. The different inoculums used are yeast sludge, spent wash, distillery effluent and sugar sludge. Yeast sludge has the highest R2 values such as 0.979, 0.978 and 0.986 for VS, TOC and C/N ratio respectively. The R2 value of C/N ratio for the sugar sludge is 0.983. From the kinetic study, it can be concluded that the yeast sludge can be a better option when compared with other sludge to accelerate the process of composting.

Diseases in plants accounts to major crop losses which affects the economy of farmers and in turn the country. Recent advancements in technologies like Internet of Things and Machine learning has led to the development of automated systems for crucial applications. This paper focuses on developing such a system for agricultural application mainly for early detection of diseases in plant leaves and provides solutions to the farmers. The main contribution is the novel pixel replacement-based segmentation and double feature extraction techniques for enhancing classification process. Classification is done using Support vector machine classifier and the developed system was validated using pomegranate leaves. The system is evaluated using performance metrics such as detection accuracy and classification accuracy.

Agriculture is one of the primary sources of Indian economy as majority of the population depends on agriculture. Early detection of plant stress functions an evident role in smart agriculture. In order to improve the quality and quantity of food crop and to prevent the damage caused to the plant, it is necessary to detect the stress in plant using environmental parameters. Visual sensor along with environmental sensors helps to identify the colour change in leaves of the plant at the initial stage thereby reducing the damage. An efficient plant stress detection framework is proposed which is automated for ease of use for farmers. The framework makes use of camera for capturing the images of the leaves in the field and machine learning for classifying the leaves as healthy or unhealthy. Support vector machine algorithm makes use of the features extracted from the leaves for training the system and the input features are alone transmitted to the cloud. The agricultural consultant at the other end retrieves the features and the stress is detected with the help of classification. Gray level co-occurrence matrix textures are used for extracting the features from the healthy and unhealthy leaves. Metrics such as Accuracy in classification and stress detection is validated in the proposed system.

Digital communication plays a vital role in transmitting and receiving the information. With the advent of advancements in telecommunications, requirement of high data rate is in evident in Digital communication for the transmission of the signal. The information which has to be transmitted over a long distance needed to be error free while receiving in order to obtain the quality of the information. Emphasizing on the high data rate and also error free requirements, the role of Multiple Input Multiple Output and Wavelet as a combination makes progress towards high extent in achieving successful transmission. The proposed work emphasizes on the application of Discrete wavelet transform for filtering the Quadrature Amplitude modulated wave for the internet telephonic communication where the digital communication plays a major role. This proposed work purely concentrates on the digital communication part of the internet telephony communications. Stein’s Unbiased Risk Estimate is used to obtain the information samples with soft thresholding. The Bit to Error rate performance is measured for various modulation order and the simulated results are shown and also the values of the Bit to Error rate are tabulated.

Automatic Speech Recognition plays an evident role in extracting the voice signal in the noisy background. The reduction of noise in the signal is susceptible to the information which is to be transmitted since not all the information is emphasized. This leads to the deterioration in the transmitted information and paved furtherance for automatic speech recognition. Prevalent feature extraction techniques are applied to extract the speech signal with the trade off of complexity, compression ratio. For the application of voice communication, filter bank analysis is applied to extract the voice signals in the noisy environment. This work emphasized on the attributes of the perceptual quality of Loudness, Pitch Intensity, Timing. Band pass filtering provides reliable extraction of the voice signal features in the noisy environment. The power distribution of the extracted signals for the selected audio signal with the length of more than 20 seconds wave file with a sampling rate of 16 Khz along with the background noises has been plotted and its respective spectrogram also been plotted.

Noise in a signal is an unwanted Distribution occurred which disrupts the original signalduring transmission, Depuration or conversion. Also noise may be random, with an unspecific mean or white noise with an independently interleaved distribution, zero mean by the processing algorithms. Performance degradation of Orthogonal Frequency Division Multiplexing system with Multiple Input and Multiple Output would have ensued due to the noise distribution in an original signal, so it is inevitable to reduce the unwanted noise distribution in an original signal to recover the original information which is sent at the transmitter. Various filtering techniques in channel estimation have been proposed in this literature to denoise a signal in Multiple Input Multiple Output – OFDM system effectively. The main intent of this survey is to provide the better insight of various denoising techniques in channel estimation to reduce unknown noise distributions in a signal with the tradeoff complexity in MIMO – OFDM systems.

To determine the rate of occurrence and genotypes of Epstein-Barr Virus (EBV) among pediatric renal and liver transplants recipients.Observational study.Vision Research Foundation referral center and Institute of Liver Disease and Transplantation, Chennai, India.70 pediatric solid organ transplant recipients and 60 voluntary healthy donors.Polymerase chain reaction (PCR) for detection and genotyping of EBV were carried out using genes targeting Viral capsid antigen, Nuclear antigen 1, 2 and 3, followed by real time PCR for viral load determination and further confirmed by phylogenetic analysis.EBV was detected in 35 (51.4%) samples (32 liver and 4 renal transplants) with high viral load. Type A was detected in 33 samples, Type B in 2 liver transplant patients, and co-infection in one liver transplant patient who developed Post-transplant Lymphoproliferative Disorder (PTLD). Real time PCR results correlated with conventional PCR. The mean viral load for patients who did not develop PTLD was 50,424 copies/mL. Overall EBV load in patient with PTLD ranged from 1,40,392 copies/mL prior to PTLD diagnosis to 62,124 copies /mL post treatment.EBV infection is the high risk factor for PTLD after liver transplantation. PCR targeting of EBV can be applied to diagnose EBV infections and monitor treatment for EBV in pediatric solid organ transplant recipients.

Ovarian tumors have occasionally been reported in pregnant women, the incidence as per literature being 1 in 81 to 1 in 2000 pregnancies. Out of this, about 2-5% are malignant, thus contributing to the rarity of malignant ovarian tumors in pregnant women. One such occurrence in a 24-year old female patient is reported here considering its extreme rarity.

Bowen's disease is generally regarded as premalignant dermatoses. The disease affects both skin and the mucosa and has the potential to progress to invasive squamous cell carcinoma. There are descriptions of several histological variants of Bowen's disease like psoriasiform, atrophic, pagetoid, etc. Acantholysis of anaplastic keratinocytes with bullae/cleft formation is described in premalignant condition like actinic keratosis and adenoid variant of squamous cell carcinoma, but there is lack of report describing this phenomena in Bowen's disease. We present a case of unusual acantholytic variant of Bowen's disease with focus of micro-invasive carcinoma.