UP-DOWN ROUTING BASED DEADLOCK FREE DYNAMIC RECONFIGURATION IN HIGH SPEED LOCAL AREA NETWORKS

Dynamic reconfiguration of high speed switched network is the process of changing from one routing function to another while the network remains in running mode Current distributed switch-based interconnected systems require high performance reliability and availability These systems changes their topologies due to hot expansion of components link or node activation and deactivation Therefore in order to support hard real-time and distributed multimedia applications over a high speed network we need to avoid discarding packets when the topology changes Thus a dynamic reconfiguration algorithm updates the routing tables of these interconnected switches according to new changed topology without stopping the traffic Here we propose an improved deadlock-free partial progressive reconfiguration PPR technique based on UP DOWN routing algorithm that assigns the directions to various links of high-speed switched networks based on pre-order traversal of computed spanning tree This improved technique gives better performance as compared to traditional PPR by minimizing the path length of packets to be transmitted Moreover the proposed reconfiguration strategy makes the optimize use of all operational links and reduces the traffic congestion in the network The simulated results are compared with traditional PP

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.Comment: 13 pages, 6 figure

Wavelet based vector quantization with treecode vectors for EMG Signal compression

1064-1071This paper presents a wavelet-based vector quantization technique using DCCR (Distortion constrained codebook replenishment) mechanism for compression of Electromyogram (EMG) signals. Wavelet coefficients, obtained from EMG signal samples, are arranged to form tree vectors (TVs), where each vector has a hierarchical tree structure. Vector quantization is then applied for encoding to TVs, which uses a pre-calculated codebook. Codebook is created using codebook training algorithm and is updated dynamically using SPIHT coding strategy. Signal is decoded using a copy of the same codebook available with encoder. Tests were performed on EMG records obtained from PGI, Chandigarh. A good quality of reconstructed signal and sufficient compression is achieved. An average Compression Ratio (CR) of 20.64:1 at percentage root mean square difference (PRD) of 6.12% is obtained by this technique

Simulation Analysis of Prototype Filter Bank Multicarrier Cognitive Radio Under Different Performance Parameters

Cognitive Radio has proven as a optimum technique for getting improved spectrum utilization by sharing the radio spectrum with licensed primary users opportunistically. The cognitive radio is a new paradigm to overcome the persisting problem of spectrum underutilization.Seeing the everincreasing demand of wireless applications,the radio sp ectrum is a valuable resource and in cognitive radio systems,trustworthy spectrum sensing techniques are required to avoid any harmful interference to the primary users.As cognitive radio possess the capability to utilise the unused spectrum holes or white spaces so,there is a tremendous need to scan the large range of spectrum either for interference management or for primary receiver detection.Dynamic Spectrum Access techniques need to be implemented for the sake of better radio resource management and computational complexity analysis of multirate filter bank cognitive radio,where BER and Eb/No are the performance metrics or governing parameters to affect the system performance using polyphase filter bank.The present paper deals with the study of effect of variation of number of subchannels M at fix overlapping factor K of polyphase component of Filter Bank Multicarrier cognitive radio in terms of prototype filter length at Lp=K*M

K Coverage Probability of 5G Wireless Cognitive Radio Network under Shadow Fading Effects

Land mobile communication is burdened with typical propagation constraints due to the channel characteristics in radio systems.Also,the propagation characteristics vary form place to place and also as the mobile unit moves,from time to time.Hence,the tramsmission path between transmitter and receiver varies from simple direct LOS to the one which is severely obstructed by buildings,foliage and terrain.Multipath propagation and shadow fading effects affect the signal strength of an arbitrary Transmitter-Receiver due to the rapid fluctuations in the phase and amplitude of signal which also determines the average power over an area of tens or hundreds of meters.Shadowing introduces additional fluctuations,so the received local mean power varies around the area –mean.The present section deals with the performance analysis of fifth generation wireless cognitive radio network on the basis of signal and interference level based k coverage probability under the shadow fading effects

Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases

This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis