Search

Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.

The term multiparametric MRI, is a useful tool in reference to an approach that takes advantage of the added value of different MR imaging acquisitions to yield anatomic and pathophysiologic information about renal space occupying lesions and to evaluate patients with different tumors, including genitourinary malignancies. The role of multiparametric MRI is continuously growing because of its ability to detect and characterize renal space occupying lesions as well as to assess response to treatment. An observational study was carried out in 50 patients who presented with renal mass, based on clinical suspicion and prior imaging diagnosis of neoplastic renal space occupying lesion. Total renal space occupying lesions were 50, of which, 38 were males & 12 were females. The age range of the study population was 30–80 years. In our study, Agreement analysis between mpMRI diagnosis and HPE diagnosis of different RCC subtypes was statistically significant. So, multiparametric MRI had a role in differentiating the subtypes of RCC which had fair agreement with HPE. The present study results state that the renal mass lesions has different ADC values for different lesions because of the change in tissue contents and there was a statistically significant difference in ADC values between low and high-stage RCCs. Histologic and radiologic profiles of renal space occupying lesions and diverse subtypes of RCC can be used as biologic indicators of clinical behavior, response to treatment, and prognosis.

Sentiment Analysis is a Natural Language Processing (NLP) technique that determines the opinion towards an entity, identifying the opinion as positive, negative, or neutral. Extensive research has taken place for high-resourced Languages like English, whereas for Low resourced Indo-Aryan languages, it is still an area in progress. This paper attempts to perform Sentiment Analysis on Assamese Texts, which is a morphologically rich yet Low-resource Indo-Aryan Language, using the concepts of popular sentiment analyzer named “Vader”, while taking “Bengali-Vader” as its backbone. The process follows that of a traditional Vader tool, with consideration towards creating a dictionary of negative booster words and creating an Assamese Lexicon, pre-processing of data, boosting the valence of each word, valence calculation and sentiment categorization of text based on the valence. The necessity of proper dataset in this method is immense, and even though the lack of good translation tools and resources to feed the model, this model was experimented on a set of Assamese texts from a renowned Assamese novel. The comparison was done by manually translating the Assamese sentences to its Bengali and English forms and it shows significant results when compared with the Bengali and English counterparts.

In today’s world, individuals show more enthusiasm for robotics and aim to depend upon humanoid robots for multiple purposes. It has implementations in a wide range of sectors such as- in atomic plants, house management, government foundations and even astro stations. Our research-based project is elucidating robotics with a tiny humanoid robot following the human structure to make gestures like strolling, dancing, and detecting objects near it. It has been achieved using Arduino Nano (Atmega 328P), Servo motors SG90 working on the conception of servomechanism and the Ultrasonic sensor to identify obstacles and restrain the Robot from going ahead. The aim is to fabricate a bigger humanoid robot that will serve our general public and make our life simpler. The Robot has eventual utilize in marketing, entertainment and helping the visually impaired to move from one place to another. This project can also be a great apparatus for future research works and alteration.

In this paper, a deloading control technique is proposed for a photovoltaic system to regulate DC link voltage under lightly loaded conditions. A simplified deloading control technique that can regulate the DC link voltage is proposed here for the battery bank supported PV system. In addition, the proposed control technique is implemented in field-programmable gate array (FPGA)-in-loop simulation to verify the feasibility of successful hardware implementation. The proposed controller is converted into an equivalent code in VHDL, a hardware description language. In the implementation process, the code is implemented in Zedboard (FPGA hardware), which is interfaced with MATLAB/Simulink and Vivado software. The results obtained using the FPGA-in-Loop simulation are satisfactory and maintaining the DC link voltage within the limit under both MPPT and deloading conditions.

Proper detection of islanding of a microgrid and its resynchronisation is very important for the plug and play operation. A simple passive islanding detection and resynchronisation technique is proposed in this paper. The change in the voltage magnitude, frequency and phase are taken into the consideration to avoid any false triggering during the detection process. In addition, the proposed technique is designed in such a way that it can be implemented in a field-programmable gate array (FPGA) hardware using FPGA-in-Loop (FIL) platform. The complete controller is converted into VHDL, a hardware description language code. Finally, the bitstream generated from the code is implemented in FPGA hardware, Zedboard. The results obtained in terms of both islanding detection and resynchronisation using the FPGA-in-Loop simulation are found to be satisfactory.

Object detection in motion pictures is always a challenging task due to the presence of dynamic background. Deep learning architectures especially encoder-decoder type has shown promising performance in segmenting foreground objects against the background in video sequences. Thus, in this work, a VGG-16 based encoder-decoder architecture is investigated and several modifications are proposed to improve the efficiency the model. The modified models are evaluated on two different standard databases— CDNet 2014 and SBI2015 with various scenes and achieved the highest precision of 0.99 which is competitive in nature with the current schemes in the state-of-the-art.

Over the most recent couple of years, people are indicating more enthusiasm for robotics and planning to rely upon robots for many reasons. One can easily expect that in the twenty-first century, the use of robots will expand in our society. We believe that robots will achieve a wide assortment of undertakings in homes, war zones, atomic plants, government establishments, production line floors, and even space stations. In this paper, we are throwing some light on robotics with a robot that is utilizing the human structure to make movements like walking and dancing. This biped robot is also able to detect an object in front of it. This has been accomplished using Arduino Nano (ATmega328P), servomotors SG90 operating on the principle of servomechanism, and ultrasonic sensor to detect objects near it and prevent the robot from proceeding forward. The robot has ultimate use in entertainment, in marketing, and in assisting visually impaired to navigate.

TF-IDF is one of the earliest and most effective word vectorization techniques that provide the basis for many natural language processing (NLP) tasks. This paper provides a novel approach to create TF-IDF vectors for Assamese text. A considerable number of experiments are carried out throughout the process, and a significant result was found which is discussed here in detail.

Detection of anomalous cells by analyzing the microscopic blood smear plays a key role in identification of various blood diseases in medical field. It becomes easier with robust segmentation steps followed by a classifier for automatic recognition of normal as well as anomalous blood cells. Sickle cell anemia is a disease that changes shape of the red blood cell (RBC) into sickle cell which can be detected by use of automatic classifier. In this reported work, a novel robust hybrid segmentation method is introduced for segmenting normal RBCs and sickle cells. The hybrid segmentation process proposed here is a combination of two segmentation methods namely fuzzy C-means and adaptive (local) thresholding. Four different adaptive thresholding methods are experimented here in combination with fuzzy C-means. The features extracted for carrying out this analysis are major axis, minor axis, aspect ratio, area, perimeter, form factor (metric value), eccentricity and solidity of each of the cells present in the sample blood smear. These eight features are used for training and testing the classifiers. For classification, two supervised classifiers, i.e. Naive Bayes classifier and K-nearest neighbor classifier have been demonstrated on a dataset of 10 image data samples and the evaluated results are compared for each of the hybrid combinations. The comparison shows that the proposed hybrid combination of fuzzy C-means and NICK's thresholding alongwith K-nearest neighbor classifier gives the best performance in terms of the considered performance parameters. This combination gives a significant classification accuracy of 98.87% with lowest computational time.

India’s growth story has attracted worldwide attention, particularly because this growth has been fuelled by the wide-ranging economic reforms introduced since early 1990s. A distinctive feature of Indian liberalization was the gradual and calibrated manner in which the reforms were introduced in the various sectors of agriculture, manufacturing and services. This article is a modest attempt to capture the role played by trade liberalization and foreign direct investment (FDI) policies in growth and development of different sectors in India. We applied computable general equilibrium (CGE) modelling as our relevant methodology following Das and Chakraborti (doi:10.1007/s40622-013-0003-3), 2013. The article constructs a social accounting matrix (SAM) and attempts to analyze the impacts of reduced import tariff and increased FDI on export–import volumes of different sectors and on exchange rate, GDP, rural and urban income levels and government income under perfect competition market structure. The study reveals that reduction in import tariff increases imports leading to a depreciation of home currency and boosting exports. On the other hand, increase in FDI appreciates exchange rate, making import cheaper and so import increases in all sectors. Export becomes less profitable leading to a reduction in sectoral export.

In India electricity price for agriculture is cross subsidized by the industries. The Indian government has started a process through which the extent of cross subsidization is gradually being reduced. The idea is to replace the cross subsidization by 2030 and introduce a rate structure that will increase with the amount of electricity usage. This paper uses the Computable General Equilibrium framework to evaluate the ex-ante impact of these policy changes on the Indian economy. The paper finds that removal of cross subsidies will increase inflation particularly food inflation resulting in a decline in household incomes more so in rural areas. Replacing cross subsidies with a progressive rate structure will compensate for only a small part of the negative effects of the removal of cross subsidies. Four other policy options are also investigated targeting household incomes, food inflation and general inflation. Most of these options do not work as the required increase in budget deficit is unlikely to be bearable to the government. The only feasible option appears to be a direct price subsidy to agricultural sector: in this case food prices are held down, inflation is moderate and effect on household incomes is minimal.

Modern image processing techniques are playing an important role in the detection and diagnosis of several medical abnormalities nowadays. In the process of analysis of any image, thresholding is a significant stage. Various researchers have developed different tools for image thresholding depending on the type of applications. Adaptive thresholding is a powerful tool in processing medical images for diagnosis of various diseases. In this work several adaptive image thresholding techniques are being used in processing the blood images of patients suffering from Sickle Cell Disease (SCD). The Sickle cell anaemia is a disorder of inherited type where defective haemoglobin distorts the shape of the red blood cell. It is essential to accurately identify the distorted red blood cells (RBCs) for faster diagnosis of this disease. Although different adaptive thresholding techniques like Niblack, Bernsen, Sauvola and NICK have extensively been used for segmentation purpose, this paper focuses mainly on applying these tools for image thresholding for detection of Sickle cell anaemia from microscopic blood images and providing a comparative analysis.

Sickle cell anemia (SCA) is a type of haemolytic anemia which is the most commonly inherited blood disorder. Detection of red blood cell and sickle cell from sickle cell anemic person is a very challenging task. Besides traditional visual inspection of microscopic images, various methods have been developed which are based on image processing technique for faster and more accurate diagnosis of SCA. In this paper normal and abnormal red blood cells have been detected using Niblack’s thresholding technique from microscopic blood images of sickle cell anemic patient. The process involves preprocessing of microscopic blood smear and segmenting the preprocessed image using Niblack’s thresholding algorithm. Then using geometrical features of blood cells a metric (form factor) is calculated to classify normal red blood cells and abnormal cells.

The retail price of petrol, diesel, liquid petroleum gas (LPG) and other energy products in India is a complex mixture of crude prices, taxes and subsidies by the central as well as state governments. In this paper, we discuss the break-up of these components of pricing. The discussion is set in the background of international pricing mechanisms and recommendations of international agencies such as the International Institute of Sustainable Development and the International Monetary Fund. The main point that emerges from the discussion is that since these products are necessities with very low elasticity of demand, incidence of taxes and/or subsidies on these products are almost 100% making them ideal tools for the international cartels (seeking profits) and local governments (seeking revenues) to maximize their pecuniary benefits from the manipulation of such prices. The complex formula leading to the retail price is a corollary of these interests.

This study analyzes the impacts of international crude oil fluctuations and energy subsidy (on LPG, petrol and diesel) removals on Indian economy. We have applied computable general equilibrium (CGE) modelling as our relevant methodology, following Shoven and Whalley ( J Econ Lit XXII: 1007–1051, 1984) based on energy social accounting matrix (ESAM) of India for the year 2007–2008. It is seen that the international crude oil price fluctuations has a greater effect in determining gross domestic product (GDP) and exchange rate as compared to the effect of energy subsidy removal. With decrease in international crude oil price, GDP increases and exchange rate appreciates. On the other hand, with decrease in energy subsidy, GDP decreases and exchange rate appreciates. Moreover, with introduction of direct cash transfer scheme in lieu of subsidy for LPG, it is seen that the impact on demand of LPG (substitution effect) is negligible indicating that LPG is an essential commodity.

doiBackground and aims: It is known that endothelial dysfunction and low-grade inflammation are important element of diabetic complications. The soluble E-selectin is a marker of endothelial dysfunction and C-reactive protein is the most important inflammatory marker. The present study is done to investigate the effect of diabetes on these two markers. Methods: The present hospital based case control study was conducted in the department of Biochemistry with the collaboration of department of Medicine of Burdwan Medical College, Burdwan, west Bengal India. All subjects were selected by simple random sampling between March 2011 and May 2013. Blood level of these soluble E-selectin and high sensitive C-reactive protein were measured in two hundred diabetic and two hundred age, sex and other non-modifiable as well as modifiable risk factors matched non-diabetic healthy controls. Results: Compared with control value of high sensitive C-reactive protein and soluble E-selectin levels were higher in diabetic subjects (p < 0.001). Our present study shows that levels of adhesion molecules and high sensitive C-reactive protein were higher in diabetic than non-diabetic. Conclusion: These data suggested that an increased level of inflammatory markers and adhesion molecules among diabetic compared with non-diabetic subjects. This suggests that chronic uncontrolled hyperglycemia contributes to progression of atherosclerosis associated with life-threading complications in middle-aged diabetic patients. DOI: http://dx.doi.org/10.3126/jaim.v3i1.10697 Journal of Advances in Internal Medicine 2014;03(01):17-22

To estimate the rate of excretion of urinary calcium, a 24-hour sample of urine is required and this is not always easy to collect accurately in infant and children. So, random urine calcium to creatinine ratio (Ca/Cr ratio) has been developed. But as the ratio varies worldwide, reference values of the parameter in paediatric population are not developed. To determine reference value, the present study was conducted in healthy paediatric population in Burdwan district, West Bengal. This study was performed on 693 healthy paediatric subjects, aged between 3 months to 18 years and divided into five groups. Early morning non-fasting urine samples from all study groups were analyzed for Ca/Cr ratio. A negative correlation was observed between age and urinary Ca/Cr ratio, but there was no significant difference of urinary Ca/Cr ratio between two sexes. Considering 97.5 th percentile of the underlying distribution of values as the upper limit of reference range, upper reference values of urinary Ca/Cr ratio for age groups of

The emerging interest in the field of digital watermarking is due to the increase in concern over copyright protection of digital information and prevention of unauthorized access, duplication and manipulation of digital data. In this paper, an entropy based blind video watermarking scheme is proposed using combined DCT-DWT transform. Two level DWT is performed on the extracted video frames, which results in sixteen subbands. Entropy is calculated for all the sixteen subbands and the subband with the highest entropy is chosen for embedding the watermark bits. 8x8 block based DCT is performed on this subband and the middle frequency transformed coefficients of each block are selected for embedding the watermark bits. The watermarked video frames are QPSK modulated and transmitted through an AWGN channel. At the receiver end the watermarked frames are QPSK demodulated and the watermark is extracted. This scheme applied to videos shows that it is highly imperceptible and exhibits high robustness against additive white Gaussian noise.

Video compression standard H.264/AVC outperforms previous standards in terms of coding efficiency but at the cost of higher computational complexity. In H.264/AVC, the variable block size full motion estimation is the most time-consuming operation. This paper presents a method to reduce the complexity of motion estimation in two stages. The first stage exploits the similarities between frames for early SKIP mode decision for a macroblock (MB) based upon a criteria formulated on the basis of the statistics of the frame difference residues. MBs that fail to qualify for the SKIP mode in the first stage spills over to the second stage where mode decision depends upon the number of zero blocks (ZB) in the MB. The study of the full search motion estimation on different sequences show that there is a strong dependence between the number of ZBs in a MB and the likelihood of a particular mode being selected. The proposed algorithm utilizes this relationship for early mode decision for a MB. The algorithm is evaluated using a wide range of test sequences from different classes. Experimental results show that the proposed algorithm gives considerable saving in encoding time and search points in the range of 36–87%. Furthermore, despite the reduction in computational complexity, the coding efficiency (picture quality and bitrate) in the proposed method is comparable to the H.264/AVC standard software Joint Model (JM12.4).

Fast depleting reserves of fossil fuels, increasing cost of fuel for conventional power generation in recent times, along with a regulatory framework to minimize the carbon footprint worldwide, the use of Renewable sources of power generation has become an inevitable reality. The paper makes a feasibility assessment of Renewable Energy System (RES) as a possible source for electrification for an education institution namely Assam Engineering College, located at Guwahati, Assam, India (26.13 °N, 91.66 °E). A combination of one or more renewable sources like photovoltaic (PV) and wind either in standalone or grid connected mode with energy storage capacity based energy systems are investigated in this paper. The objective of the study is to optimize the size and cost of renewable energy system at the selected site to meet the electrical demand of a load of 24kWh/day having a peak load of 6.5 kW. The modeling, optimization and analysis of the hybrid system is carried out using HOMER software produced by National Renewable Energy Laboratory (NREL), Colorado USA. The hybrid systems are evaluated on the basis of Net Present Cost (NPC), Levelised Cost of Energy (COE), Initial Cost, Operating Cost, and Renewable Fraction obtained on the basis of computation results. The results are favorable for the use of renewable energy based sources at the selected site with PV-Wind-Grid connected system emerging as the most economic RES with COE equal to 0.209 $/kWh.

This paper presents wavelet transform based analysis to determine transmission line faults classification. The fault classification technique is developed on the basis of extensive simulation studies carried out on the power system model within the MATLAB Simiilink program for different operating conditions. Discrete wavelet transform has been applied on the fault current and the mean of approximate coefficients of the phase signals is calculated. Daubechies4 wavelet transform has been applied to decompose the phase signals into Scale 1 wavelet coefficient. The detection is carried out by analyzing the mean of approximate coefficients of the phase signals and the fault classification criteria are presented.

This paper presents wavelet transform based analysis to determine whether a fault has occurred at near end or far end with respect to a reference bus and thus facilitates the calculation of fault location based on the lattice diagram of the travelling wave phenomenon in transmission line. Faults in power transmission lines cause transients that travel at the speed of light and propagate along the line as travelling wave. The fault is generated in a simple two bus system within the MATLAB Simulink program. Clarke's transform has been applied to convert the fault current into their modal components. Aerial mode is only applied in the analysis. Daubechies4 wavelet transform has been applied to decompose the Aerial mode into Scale 1 and Scale 2 wavelet coefficient.

In this paper, we propose a fast mode decision algorithm for intra and inter prediction for H.264/AVC video coding. Here the subband decomposition of the input video is done for the fast mode prediction. To the LL band of the subband decomposition of the video, exhaustive intra prediction is done. To the LH, HL and HH bands selective intra prediction modes are used for the intra prediction. For the inter mode decision, the motion estimation is performed for the LL band and the motion vectors obtained are used directly for the high frequency bands. The proposed method reduces the computational complexity and time considerably while maintaining similar PSNR and bit rate. Experimental results for various test sequences at different resolutions are presented to show the effectiveness of the proposed method.

In this paper, we propose a fast intra-prediction mode selection method for JVT video coding standard H.264. The fast mode decision is based on the quantization parameter and the local edge information obtained by calculating the edge histogram parameters. The inclusion of the DC mode is based on the Residual Complexity factor. The proposed method reduces the computational complexity and time considerably while maintaining similar PSNR and bit rate. Experimental results for various test sequences at different resolutions are presented to show the effectiveness of the proposed method.