Convolution

In radionuclide therapy, absorbed dose is calculated by convolution of a three-dimensional activity matrix with a three-dimensional dose point kernel. A technique employing the fast Hartley Transform (FHT) has been developed to perform this calculation. An important part of that development was the indexing scheme for 3D data. The results of this new FHT convolution technique were compared to direct convolution. A cube was convolved with itself by these two techniques. The results differed by less than 2 percent. In an effort to show the practical applicability of 3D convolution, a three-dimensional activity matrix from a I-131-labeled 16.88 monoclonal antibody patient was convolved with beta and photon dose point kernels using direct convolution. Isodose contours were then generated from the calculated absorbed dose matrix and overlaid on a CT image of the patient

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed.

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer

The main objective of image enhancement is to improve some characteristic of an image to make it visually better one. This paper proposes a method for enhancing the color images based on nonlinear transfer function and pixel neighborhood by preserving details. In the proposed method, the image enhancement is applied only on the V (luminance value) component of the HSV color image and H and S component are kept unchanged to prevent the degradation of color balance between HSV components. The V channel is enhanced in two steps. First the V component image is divided into smaller overlapping blocks and for each pixel inside the block the luminance enhancement is carried out using nonlinear transfer function. In the second step, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel value and its neighborhood pixel values. Finally, original H and S component image and enhanced V component image are converted back to RGB image. The subjective and objective performance evaluation shows that the proposed enhancement method yields better results without changing image original color in comparison with the conventional methods.

Abstract: Hand gestures recognition provides a natural way to interact and communicate with machines of different kinds. The process is known and referred to as static hand gesture recognition in which images of a hand gesture are stored in the database and analyzed in order to determine the meaning of the hand gesture. The implementation has been done using Scale Invariant Feature Transform (SIFT), Principal Component Analysis(PCA) which present an interface used to recognize  hand gestures from the American Sign Language.
Keywords:  Distance ratio, Threshold, convolution, Gaussian Filters, Eigen Vector.
Title: Analysis and Implementation of Hand Gesture Recognition System
Author:  Mrs. Poonam Verma, Utkarsh Mor, Puneet Singh, Arpan Khandelwal
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals

Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feed-forward neural networks and adaptive filters. Two-dimensional CNNs are formed by one or more layers of two-dimensional filters, with possible non-linear activation functions and/or down-sampling. Convolutional neural networks (CNNs) impose constraints on the weights and connectivity of the network, providing a framework well suited to the processing of spatially or temporally distributed data. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). The so- called “weight-sharing” property of CNNs limits the number of free parameters. Although CNNs have been applied to face and character recognition, it is fair to say that the full potential of CNNs has not yet been realised. This chapter presents a description of the convolutional neural network architecture, and reports some of our work applying CNNs to theoretical and real-world image processing problems.

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

موضوعات مورد مطالعه :
 کدگذاری کانال با استفاده از کدهای کانولوشنال
 شبیه سازی کانال چند مسیره با نویز گوسی سفید جمع شونده
 طراحی همسان‌ساز MLSE برای حذف اثر ISI ، به کمک الگوریتم ویتربی
 آشکارسازی کدهای کانوشنال، به کمک الگوریتم ویتربی

Convolution reverb is the process used for reverberating a signal by an impulse response of an actual space. The product of this operation is a third signal, containing reverberation of the space where the impulse response is captured. This project is based on creating a convolution reverb plugin on MaxMSP and evaluating the perceptual quality of convolution reverb.
The real-time fast convolution reverb plugin was implemented using multiple frequency delay line non-uniform partitioned convolution method on MaxMSP. An experimental design methodology was introduced in order to evaluate the perceptual quality of convolution reverb. In order to realise, anechoic drum kit music samples were recorded and re-recorded in a chamber to form the control signal. Also, using same equipment and setup, an impulse response was captured in the same chamber to form the test sample. These samples were then used for subjective analysis in a pair-wise categorical preference type listening test. The samples were also analysed in their spectrogram views in order to analyse the quality objectively.
Listening tests were designed so that realism, quality and personal preference categories were present. Null hypothesis was proved, where the realism difference of the two samples resulted in 52% of the participants preferring chamber reverb (control signal). However personal preference category resulted in 61%of the subjects preferring the chamber reverb. This was justified by objective analysis, where the convolution reverb shown to be having a faster decay rate for high frequency bands, thus sounding unnatural.

An introduction to Dirac delta function$ and its salient properties are presented. The experience of having taught  subjects in physics such as quantum mechanics, electromagnetism, optics, mathematical physics for the past three decades, the presentation in this communication is distinct and different. Many situations wherein Dirac delta function plays important roles are listed and discussed. It is hoped that this pedagogical paper will help the beginners to understand and appreciate this important singular function.
$ P. A. M. Dirac, “The Principles of Quantum mechanics”, Oxford Science Publications, 1970.

All physical subjects, involving random phenomena, something depending upon chance, naturally find their own way to theory of Statistics. Hence there arise relations between the results derived for hose random phenomena in different physical subjects and the concepts of Statistics. Convolution theorem has a variety of applications in field of Fourier transforms and many other situations, but it bears beautiful applications in field of statistics also .Here in this paper authors want to discuss some notions of Electrical Engineering in terms of convolution of some probability distributions.

Face recognition is an emergent research area, spans over multiple disciplines such
as image processing, computer vision and signal processing, machine learning. Face
recognition is mainly used for identity authentication/identification, security access
control and intelligent human-computer interaction.
This work compares holistic and hybrid face recognition methods. The hybrid face
feature extraction in which local features were derived using Multi Scale Block Local
Binary Patterns (MB-LBP) and global features are derived using Principal Component
Analysis (PCA). For a facial image a spatially enhanced, concatenated representation
is obtained by deriving a histogram from each grid that an input image
is divided. These histograms were projected to lower dimensions by applying PCA
to create eigenfaces. The holistic face representation of a subject was derived by
projecting several images of the subject into lower dimensions applying PCA.
There are several parameters affect the performance of local descriptor based face
recognition system viz: image size, grid size, operator scale and available codes. Impact
of these parameters on performance of FRS are not explored much in literature.
This thesis aims to study empirically the effect of these parameters for FRS configured
in verification mode.

The distribution of the sum of independent identically distributed uniform random variables is well-known. However, it is sometimes necessary to analyze data which have been drawn from different uniform distributions. By inverting the characteristic function, we derive explicit formulae for the distribution of the sum of n non-identically distributed uniform random variables in both the continuous and the discrete case. The results, though involved, have a certain elegance. As examples, we derive from our general formulae some special cases which have appeared in the literature.

"Kubla Khan" and "This Lime-Tree Bower My Prison" are constructed on utterly different schemes, though they share some of the same underlying components. "Kubla Khan" is ontological and impersonal in character and makes extensive use of convolution in calculating meanings. It reveals the structure of being. "Lime-Tree Bower" is narrative and personal and makes little or no use of convolution. It reveals the unfolding of subjectivity in time. The two poems also differ in their versification, a differences which is related to their different strategies of meaning.

Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-aware Temporal Weighted CNN (ATW CNN) for action recognition in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Our experimental results on the UCF-101 and HMDB-51 datasets showed that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.

In image processing one of significant perspective is the image compression. It is concerned in reducing the quantity of fundamental element to represent of an image. An immense memory and high data transfer capacity are required for transmission of pictures continuously. Hereafter to build the organization capacities of winning organizations in a successful way and furthermore to make the interest for capacity to an economical level, image compression gives a quick solution for this arrangement. In this proposed work, an effective discrete wavelet transform (DWT) is designed for compression of image by utilizing fundamental cell strategy and a streamlined controller pathway laterally with a capable design architecture is suggested. The architecture design is thought by reasonably interconnecting the units and is realized in Verilog HDL. The task complier is been utilized to synthesize and confirm the design functional practically by utilizing the EDA tool. The result of the approach shows decline in power use, repetition in memory utilization, high-throughput and less latency contrasted with other existing techniques.