Manoj Nagmode

Bio: Manoj Nagmode is an academic researcher from Savitribai Phule Pune University. The author has contributed to research in topics: Signal & Acceleration. The author has an hindex of 3, co-authored 9 publications receiving 42 citations.

Malaria Disease Identification and Analysis Using Image Processing

Abstract: Malaria is an infectious disease. According to World Health Organization (WHO) it affects one million people face deaths each year. There are various methods to analyses malaria using manual microscopy is considered to be However it requires manual assessment, this diagnostic method is dispose to human error and time consuming, even in experienced hands. The this study is to improve an unsupervised, sensitive and sturdy which lessens human trust as well as reduces material cost and is, so, more method compatible in applying diagnostic criteria.

Vibration Measurement System with Accelerometer Sensor Based on ARM

Abstract: These Vibration amplitude may be measured as a displacement, a velocity, or acceleration. Vibration amplitude measurements may either be relative, or absolute. The basic scheme of vibration measurement is outlined first. Descriptions are given of transducers devices which transform physical variables into equivalent electrical signals and of vibration pickups. The increasing demands of higher productivity and economical design lead to higher operating speeds of machinery and efficient use of materials through lightweight structures. How we can measure vibration or earthquake signal of any device is describe in this system. These trends make the occurrence of resonant conditions more frequent during the operation of machinery and reduce the reliability of the system. Hence the periodic measurement of vibration characteristics of machinery and structures becomes essential to ensure adequate safety margins. Accelerometers develop a voltage from a piezoelectric crystal that has a mass mounted upon it. When the mass fixed to the crystal vibrates from the motion of the device upon which the sensor is attached, the crystal generates a voltage proportional to the force applied by the mass as it vibrates with the machinery. While no external excitation is required for the sensor to produce its voltage signal, the signal is small (self- generated) and requires a preamplifier.

Two Level Key Frame Extraction for Action Recognition Using Content Based Adaptive Threshold

Abstract: Extracting keyframes for action recognition purposes is a challenging task, as compression is to be achieved without losing the impression of the action. Insufficient or wrong keyframes can lead to confusion in the type of action. Proposed two-level key frame extraction algorithm uses an adaptive threshold technique to identify most dissimilar frames as keyframes. At the first level, global features based on intensity histogram and at second level local features computed from wavelet decomposition are used as similarity measures. A new performance parameter, Compression Ratio-Normalized Fidelity (CRNF) is introduced for evaluating the keyframe extraction algorithm. Average CRNF of 0.81 is achieved by the proposed algorithm, as compared 0.37 achieved by existing histogram-based method for action recognition dataset. CRNF value of 0.95 is achieved for Open Video project dataset, which is more than existing methods. Qualitative results further prove the effectiveness of the proposed algorithm.

Handbook Of Human Vibration

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An Introduction to the Theory of Seismology

Abstract: IN view of the number of different lines of approach, a borderline subject is apt to amass a vast literature, some of it difficult of access to investigators This is particularly true of seismology ; the phenomena of earthquakes have attracted the attention of many famous geologists, and the recording of earthquake tremors involves the methods of experimental physics, while the interpretation of the records of earthquakes has provided a great stimulus to the development of the theory of the propagation of disturbances in elastic media and has ultimately thrown considerable light on the composition of the interior of the earth Many books have been written on seismology in its various aspects, but many of the most interesting modern geophysical developments have hitherto been available only in the original papers It is therefore of considerable interest to read the elegant exposition that Prof K E Bullen has presented in the book under review An Introduction to the Theory of Seismology By Prof K E Bullen Pp xiv + 276 (Cambridge: At the University Press, 1947) 18s net

Fine-Grained Vehicle Model Recognition Using A Coarse-to-Fine Convolutional Neural Network Architecture

Abstract: Fine-grained vehicle model recognition is a challenging problem in intelligent transportation systems due to the subtle intra-category appearance variation. In this paper, we demonstrate that this problem can be addressed by locating discriminative parts, where the most significant appearance variation appears, based on the large-scale training set. We also propose a corresponding coarse-to-fine method to achieve this, in which these discriminative regions are detected automatically based on feature maps extracted by convolutional neural network. A mapping from feature maps to the input image is established to locate the regions, and these regions are repeatedly refined until there are no more qualified ones. The global and local features are then extracted from the whole vehicle images and the detected regions, respectively. Based upon the holistic cues and the subordinate-level variation within these global and local features, an one-versus-all support vector machine classifier is applied for classification. The experimental results show that our framework outperforms most of the state-of-the-art approaches, achieving 98.29% accuracy over 281 vehicle makes and models.

Object recognition and detection with deep learning for autonomous driving applications

Abstract: Autonomous driving requires reliable and accurate detection and recognition of surrounding objects in real drivable environments Although different object detection algorithms have been proposed, not all are robust enough to detect and recognize occluded or truncated objects In this paper, we propose a novel hybrid Local Multiple system LM-CNN-SVM based on Convolutional Neural Networks CNNs and Support Vector Machines SVMs due to their powerful feature extraction capability and robust classification property, respectively In the proposed system, we divide first the whole image into local regions and employ multiple CNNs to learn local object features Secondly, we select discriminative features by using Principal Component Analysis We then import into multiple SVMs applying both empirical and structural risk minimization instead of using a direct CNN to increase the generalization ability of the classifier system Finally, we fuse SVM outputs In addition, we use the pre-trained AlexNet and a new CNN architecture We carry out object recognition and pedestrian detection experiments on the Caltech-101 and Caltech Pedestrian datasets Comparisons to the best state-of-the-art methods show that the proposed system achieved better results