Engineering

Mine managerial officials have to take a variety of
decisions on a routine basis for the smooth running of the mine.
For arriving at any form of decision which is responsible for
production, productivity or ensuring the safety of mine and
miners, availability and easy access of information along with
decision support will certainly not only to expedite but also to
arrive at the right decision at the proper time. Making decisions
concerning complex systems like underground mine safety often
strains our cognitive capabilities. In many situations related to
safety or disaster management or emergencies, the quality of
decisions is very important which should be rationally
supported by the available data and information. More recently
the decisions are supported or enhanced by a variety of
techniques originating from information science, cognitive
psychology, and artificial intelligence, have been implemented in
the form of computer programs, either as stand-alone tools or as
integrated computing environments for complex decision
making. Such environments are often given the common name
of decision support systems (DSSs). A safety information and
decision support system (SIDSS) for an underground coal mine
is proposed here to realize coalmine safety under the uniform
data management platform based on the technology of
integration of coalmine information and decision support
models as a modern safety information management system. A
prototype SIDSS is discussed in detail here.

The paper discusses the results of noise studies in opencast mines and analyses the noise levels produced by different machineries and its impact. Further, it focusses on the adverse effects of noise and lists the control measures for protecting the workers against noise pollution

Mine managerial officials have to take a variety of decisions on a routine basis for the smooth running of the mine. For arriving at any form of decision which is responsible for production, productivity or ensuring the safety of mine and miners, availability and easy access of information along with decision support will certainly not only to expedite but also to arrive at the right decision at the proper time. Making decisions concerning complex systems like underground mine safety often strains our cognitive capabilities. In many situations related to safety or disaster management or emergencies, the quality of decisions is very important which should be rationally supported by the available data and information. More recently the decisions are supported or enhanced by a variety of techniques originating from information science, cognitive psychology, and artificial intelligence, have been implemented in the form of computer programs, either as stand-alone tools or as integrated ...

Indirect materials procurement, mostly consist of low volume and high variety still technically they are not a part of the end sale product, even they are having same important as direct material. For example, in an automated production line power grind to a halt without a grinding wheel indirect material, hence procurement and presence of indirect materials at right time is of major importance.The reduction of non-value added activities in this process deduction of non-value added actionswould result in the reduction of lead time and buyers administrative efforts. Hence, this study has been interpretedto improving effectiveness and efficiency of the current procurement process.The objective of this study is to analyze and determine different aspects of the process of Purchase Requisition (PR) to Purchase Order (PO) and their analysis. We use power BI for interactive analysis and visualization. We applied our approach on steel coated products. Proposed approach benefits the procurement process and helps to retainconversion time and reduce the breakdown period.

In this paper design of low power asynchronous Viterbi decoder for wireless applications is exposed. Algorithm based designed which uses bundle data (BD) protocol is implemented here for code rate 1/2 and constraint length 3. Four phase bundled data handshake protocol is used as a communication protocol to synchronize the data between different blocks of Viterbi decoder. From the obtained results it is observed that hybrid register exchange method is being outperforms than register exchange method as this helps to reduce the switching activity and hence the dynamic power. Proposed asynchronous Viterbi decoder using HRE method helps to reduce average dynamic power by 61.65% than its synchronous counterpart. Complete design is implemented in Xilinx 13.1 and its XPower analyzer tool is used to calculate the dynamic power. KeywordsAsynchronous Viterbi Decoder, four-phase, two-phase, Hybrid register exchange

Viterbi decoders are widely used in digital communication which dissipates huge quantity of power. High speed and low power of system can be achieved by applying asynchronous technique to the digital system. In this work we have designed VD with 4-state, 1/2-code rate synchronous and asynchronous Viterbi decoder. Dynamic power can be lower by reducing switching activity and this is occurring due to designing VD from Hybrid Register Exchange Method. Soft decision decoding technique is being used since it can correct more number of errors than hard decision. Results shows that the implemented design using GALS gives 13.04% reduction in dynamic power consumption compared with its synchronous counterpart with improvement in maximum frequency by 52.71%. KeywordsViterbi Algorithm (VA), Viterbi decoder (VD), Branch Metric Unit (BMU), Add-CompareSelect (ACS), State Metric Unit (SMU), Hybrid Register Exchange Method (HREM). --------------------------------------------------------------------...

Image compression plays vital role in the field of the corporate world, entertainment industry, multimedia, education communications and even at home. The main objective of Image compression is to minimize the size of image in bytes by reducing the redundancy of the image data without degrading the quality of image that results in the reduction of file size so that more images can be stored in a given amount of disk or memory space and also reduces the time required to send the images over the network. Inspired by the recent advancements in the image compression techniques, we proposed an image compression framework based on the pixel-wise fidelity. The proposed method is used to reduce the bit rate of the pixels of the image for the compression by using angular transformation. In the proposed algorithm we used the normalization technique and the inverse sine transformation to get the optimized and compressed image as an output. Finally the results are compared with different methods for MSE, PSNR and the visual appearance after decoding of image.

Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.

Content-based image retrieval (CBIR) is broadly applicable for searching digital images from a gigantic database. Images are retrieved by their primitive visual contents such as color, texture, shape, and spatial layout. The approach presented in this paper utilizes structural connections within an image by integrating textured color descriptors and structure descriptors to retrieve semantically significant images. The retrieval results were obtained by applying the HSV histogram, color coherence vector, and local binary pattern histogram to the standard database of Wang et al., which has 1000 images of 10 different semantic categories. Euclidean distance was used to find the similarity between the query image and database images. This method was evaluated against different methods based on edge histogram descriptors, color structure descriptors, color moments, the color histogram, the HSV histogram, Tamura features, edge descriptors, geometrical shape attributes, and statistical properties such as mean, variance, skewness, and kurtosis. Retrieval results obtained using the proposed methods demonstrated a significant improvement in the average precision (73.8% and 73.1%) compared with those obtained using other existing retrieval methods.

In recent times the integration of video, audio and data in telecommunication devices has revolutionized the world communication. It has proven to be useful to almost every industry: the corporate world, entertainment industry, multimedia, education and even many household domestic appliances. The major problems encountered with these applications are the high data rates, high bandwidth and large memory required for storage and computing resources. Even with faster internet, throughput rates and improved network infrastructure, there are major bottlenecks in transferring such high volume data through the network due to bandwidth limitations. This justifies the need to develop compression techniques in order to make the best use of available bandwidth [1]. This paper presents how the digital image is compressed using discrete cosine transform and the comparative study with other methods.

With an exponential growth of multimedia and high speed internet, many relevant services and software are available for common person. Out of which, one of the applications is that common man wants to send multimedia data on internet. H.264 codec is used to compress this data by the systems. But data on the internet can be hacked, pirated or tampered with. Hence some security is required for multimedia data. In this paper, two schemes are provided for securing the video streaming after compression. One is with the proposed algorithm and the other with the standard algorithm(AES). In the second scheme, FMO is applied to bitstream which adds error resilience to the data. Both the schemes are applied selectively to I frame only of AVC/SVC. Performance is evaluated by finding PSNR for quality measurement.Alsooverheadbitandencodingtimearecompared. Security is discussed for some attacks. After considering the results for PSNR and discussion for other parameters, it is concluded that the s...

algorithm performs more efficiently than diamond search and hexagonal search algorithm with lower computational complexity and similar quality is maintained. The algorithm uses two hexagonal search patterns in conjunction with diamond search pattern. The profiling results of the all these algorithms are also checked.

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain–computer interface (BCI) according to different mental task conditions. EEG data was obtained either from BCI data base or from EEG experimental recording. There were different methods for feature Extraction like temporal methods, frequential methods, and Time-frequency representations. Among these methods wavelet which was type of Time frequency representation method most popularly used for feature extraction.

Content based image retrieval (CBIR) systems retrieve images based on their primitive features such as color, texture, shape etc. The semantic gap is defined as the inconsistency between the image retrieval based on these low level image features and high level human semantics. In this paper, the comparative analysis of various color model transformations is presented with the help of our proposed methods based on three color descriptors i.e. color histogram, color moments and color coherence vectors to determine the applicability of these models and descriptors for the reduction of semantic gap. Support vector machines are used to classify images into different semantic classes. The results are inferred with the help of performance parameters like precision, recall, and mean average precision. Experimental results suggest that the proposed approach gives a good evaluation of the applicability of color models as well as color descriptors for optimization of semantic gap in CBIR.