Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination

Due to the very large number of bands in hyperspectral imagery, two major problems which arise during classification are the ‘curse of dimensionality’ and computational complexity. To overcome these, dimensionality reduction is an important task for hyperspectral image analysis. An unsupervised band elimination method is proposed which iteratively eliminates one band from the pair of most correlated neighbouring bands depending on the discriminating capability of the bands. Correlation between neighbouring bands is calculated over partitioned band images. Capacitory discrimination is used to measure the discrimination capability of a band image. Finally, four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the selected bands to measure the efficiency of the proposed method. The proposed unsupervised band elimination technique is compared to three popular state-of-the-art approaches, both qualitatively and quantitatively, and shows promising results compared to them.     

Histogram thresholding for unsupervised change detection of remote sensing images

The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.     

Quasi-static Torsional Deformation Behavior of Porous Ti6Al4V alloy

Laser processed Ti6Al4V alloy samples with total porosities of 0%, 10% and 20% have been subjected to torsional loading to determine mechanical properties and to understand the deformation behavior. The torsional yield strength and modulus of porous Ti alloy samples was found to be in the range of 185-332 MPa and 5.7-11 GPa, respectively. With an increase in the porosity both the strength and the modulus decreased, and at 20% porosity the torsional modulus of Ti6Al4V alloy was found to be very close to that of human cortical bone. Further, the experiments revealed clear strain hardening and ductile deformation in all the samples, which suggests that the inherent brittleness associated solid-state sintered porous materials can be completely eliminated via laser processing for load bearing metal implant applications.     

A novel concept of embedding orthogonal basis function expansion block in a neural equalizer structure for digital communication channel

The proposed neural equalizer structure is based on a novel orthogonal basis function (OBF) expansion technique, motivated by genetic evolutionary concept, which utilizes a self-breeding approach to evolve new information to consolidate the final output. Here, the decision at a feedforward neural network (FNN) node termed as expert opinion of a generation undergoes an orthogonal expansion in two dimensions, where one of the outputs possessing the knowledge base for that generation participates in taking the final decision. Hence, a collective judgment based on the expert opinions evolved from decisions of individual generations gives a more rational and heuristic solution compared to a conventional feedforward neural network (CFNN) structure. Propagation of output error backwards and calculation of local gradients at each node become a difficult task as the OBF block is positioned in between the neurons of different layers. In order to circumvent such situation, a new technique has been evolved. The developed equalizer structure using this concept has outperformed the CFNN equalizer with wide margins. Further their bit-error-rate performances are close to that of Bayesian equalizer, which is optimal in the theoretic sense. Application of this proposed technique also reduces the structural and computational complexity of conventional neural equalizers. Hence, this efficient equalizer structures suitable for digital communication channels have the potential for real-time implementation in DSP, FPGA processors also.     

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms.     

Spectrum occupancy prediction based on functional link artificial neural network (FLANN) in ISM band

Recent advancements in artificial neural networks (ANNs) motivated us to design a simple and faster spectrum prediction model termed the functional link artificial neural network (FLANN). The main objective of this paper is to gather realistic data to obtain utilization statistics for the industrial, scientific and medical band of 2.4–2.5 GHz. To present the occupancy statistics, we conducted measurement in indoors at the Swearingen Engineering Center, University of South Carolina. Further, we introduce different threshold-based spectrum prediction schemes to show the impact of threshold on occupancy, and propose a spectrum prediction algorithm based on FLANN to forecast a future spectrum usage profile from historical occupancy statistics. Spectrum occupancy is estimated and predicted by employing different ANN models including the Feed-forward multilayer perceptron (MLP), Recurrent MLP, Chebyshev FLANN and Trigonometric FLANN. It is observed that the absence of a hidden layer in FLANN makes it more efficient than the MLP model in predicting the occupancy faster and with less complexity. A set of illustrative results are presented to validate the performance of our proposed learning scheme.

     

Reliable communication in UWB body area networks using multiple hybrid relays

Body area networks (BANs) are wireless networks around the human body. Ultra-wideband (UWB) is one of the most preferred candidates for the physical layer of BAN. However, the attenuation due to human body tissues and the movement of body parts with respect to each other causes severe attenuation and limits the range of UWB. Hence, new and novel methods are required to improve communications in UWB BAN. The paper proposes, hybrid amplify forward and decode forward relays for communication in body area networks. Scenarios in the presence of multiple relays for both body surface and body surface to external models have been taken into consideration. The performance has been tested using IEEE 802.15 task group 6 designated channel models developed from real-time measurements. The findings from the analytical as well as simulation-based study indicate that hybrid relaying provides a versatile performance, and improves the reliability of the communication link by decreasing the bit error rate. The performance has been further validated by the transmission of medical magnetic resonance images. The results indicate that hybrid relaying has the best performance, and it can undoubtedly improve the reliability of the body area networks.     

Processing and characterization of porous alumina scaffolds

Bioceramic materials are used for the reconstruction or replacement of the damaged parts of the human body. In this study an improved procedure is described for producing ceramic scaffolds with controlled porosity. Bioinert alumina ceramic was used to make porous scaffolds by using indirect fused deposition modeling (FDM), a commercially available rapid prototyping (RP) technique. Porous alumina samples were coated with hydroxyapatite (HAp) to increase the biocompatibility of the scaffolds. Initial biological responses of the porous alumina scaffolds were assessed in vitro using rat pituitary tumor cells (PR1). Both porous alumina and HAp coated alumina ceramics provided favorable sites for cell attachments in a physiological solution at 37 °C, which suggests that these materials would promote good bonding while used as bone implants in vivo. Based on these preliminary studies, similar tests were performed with human osteosarcoma cells. Cell proliferation studies show that both the ceramic materials can potentially provide a non-toxic surface for bone bonding when implanted in vivo.