An audio watermarking scheme using singular value decomposition and dither-modulation quantization

Quantization index modulation is one of the best methods for performing blind watermarking, due to its simplicity and good rate-distortion-robustness trade-offs. In this paper, a new audio watermarking algorithm based on singular value decomposition and dither-modulation quantization is presented. The watermark is embedded using dither-modulation quantization of the singular values of the blocks of the host audio signal. The watermark can be blindly extracted without the knowledge of the original audio signal. Subjective and objective tests confirm high imperceptibility achieved by the proposed scheme. Moreover, the scheme is quite robust against attacks including additive white Gaussian noise, MP3 compression, resampling, low-pass filtering, requantization, cropping, echo addition and denoising. The watermark data payload of the algorithm is 196 bps. Performance analysis of the proposed scheme shows low error probability rates.     

Hidden Markov model a tool for recognition of human contexts using sensors of smart mobile phone

A fast and accurate computational model of HMM (Hidden Markov Model) is proposed for Activity Recognition System using inbuilt sensors of Smart Mobile Phone. Twelve features are calculated from the captured data and the feature vectors are divided into two vectors which are used as inputs to HMM. All computational methods follow probability theories and for measuring differences of two probability based events we used K–L divergence of Kullback and Leibler (Ann Math Stat 22(1):79–86, 1951) known as KLD (Kullback & Leibler Divergence). For comparing of feature values of ground truth and that of experimental values, we have developed an algorithm D-HMM (Divisional-HMM, proposed algorithm). Results show better recognition than existing HF-SVM (Hardware Friendly Support Vector Machine) and also better than our previous work of CFT (Conditional Features using Threshold, a method developed for using different schemes of threshold values for selection and matching purposes of feature values).

     

‘MaaS’: fast retrieval of E-file in cloud using metadata as a service

In cloud era as the data stored is enormous, efficient retrieval of data with reduced latency plays a major role. In cloud, owing to the size of the stored data and lack of locality information among the stored files, metadata is a suitable method of keeping track of the storage. This paper describes a novel framework for efficient retrieval of data from the cloud data servers using metadata with less amount of time. Performance of queries due to availability of files for query processing can be greatly improved by the efficient use of metadata and its analysis thereof. Hence this paper proposes a generic approach of using metadata in cloud, named ‘MaaS—Metadata as a Service’. The proposed approach has exploited various methodologies in reducing the latency during data retrieval. This paper investigates the issues on creation of metadata, metadata management and analysis of metadata in a cloud environment for fast retrieval of data. Cloud bloom filter, a probabilistic data structure used for efficient retrieval of metadata is stored across various metadata servers dispersed geographically. We have implemented the model in a cloud environment and the experimental results show that methodology used is efficient on increasing the throughput and also by handling large number of queries efficiently with reduced latency. The efficacy of the approach is tested through experimental studies using KDD Cup 2003 dataset. In the experimental results, proposed ‘MaaS’ has outperformed other existing methods.     

Linear time invariant system order reduction using multipoint step response matching

The proposed method of linear time invariant system order reduction is based on multipoint step response matching for both pole and zero evaluation of the reduced order system (ROS). Depending on the number of zeros and poles of the ROS, the number of points is selected on the time axis of the unit step response such that the unknown poles and zeros can be determined by solving a set of non-linear equations using Newton's method. Numerous examples have been solved using the proposed technique and the results are encouraging.     

Parameter convergence and learning curves for neural networks

We revisit the oft-studied asymptotic (in sample size) behavior of the parameter or weight estimate returned by any member of a large family of neural network training algorithms. By properly accounting for the characteristic property of neural networks that their empirical and generalization errors possess multiple minima, we rigorously establish conditions under which the parameter estimate converges strongly into the set of minima of the generalization error. Convergence of the parameter estimate to a particular value cannot be guaranteed under our assumptions. We then evaluate the asymptotic distribution of the distance between the parameter estimate and its nearest neighbor among the set of minima of the generalization error. Results on this question have appeared numerous times and generally assert asymptotic normality, the conclusion expected from familiar statistical arguments concerned with maximum likelihood estimators. These conclusions are usually reached on the basis of somewhat informal calculations, although we shall see that the situation is somewhat delicate. The preceding results then provide a derivation of learning curves for generalization and empirical errors that leads to bounds on rates of convergence.     

Development and use of augmented reality and 3D printing in consulting patient with complex skull base cholesteatoma

Temporal bone and skull base anatomy is complex and can pose difficulties in patient’s perception of disease and anatomy, perceived risks, and complications of surgery. We wish to demonstrate the development and use of augmented reality (AR) technology and three-dimensional (3D) printing to assist in preoperative patient consultation in the case of a complex skull base cholesteatoma. A series of 3D models were constructed from a patient’s petrous temporal bone computed tomography (CT) scans using CAD (computer-aided design) software to display the cholesteatoma affected temporal bone and related structures. Supplementary labels and titles were also created. A two-dimensional (2D) image was created as an AR recognition marker. Both 3D and 2D assets were uploaded, linked in an AR development platform called Hyperspaces which was then published to the Hyperspaces cloud server to build an AR application for free access using a predefined keyword on an iOS mobile device. Patient’s temporal bone was also fabricated through a fused deposition modelling 3D printer using polylactic acid filament for patient consultation. AR and 3D printing allow patient-specific clinical information and complexities to be made readily available to the patient and doctor at low cost, and aid in understanding complex skull base anatomy and progressive disease such as cholesteatoma. The advancement of AR and 3D printing technologies are making complex and patient-specific 3D medical data visualisation feasible and tangible on a mobile device and in hand. Thus, these technologies can be used as an invaluable patient education and counselling tool offering a powerful medium in specialties where difficult anatomical challenges are encountered.Abbreviations: ABS: acrylonitrile–butadiene–styrene; AM: additive manufacturing; AR: augmented reality; CAD: computer-aided design; CT: computed tomography; DICOM: Digital Imaging and Communication in Medicine; FBX: Filmbox; FDM: fused deposition modelling; JPEG: joint photographic experts group; MRI: magnetic resonance imaging; PLA: polylactic acid; ROI: region of interest; STL: Stereolithography     

Hot wire chemical vapor deposited multiphase silicon carbide (SiC) thin films at various filament temperatures

Influence of filament temperature (T_Fil) on the structural, morphology, optical and electrical properties of silicon carbide (SiC) films deposited by using hot wire chemical vapor deposition technique has been investigated. Characterization of these films by low angle XRD, Raman scattering, XPS and TEM revealed the multiphase structure SiC films consisting of 3C–SiC and graphide oxide embedded in amorphous matrix. FTIR spectroscopy analysis show an increase in Si–C, Si–H, and C–H bond densities and decrease in hydrogen content with increase in T_Fil. The C–H bond density was found higher than the of Si–H and Si–C bond densities suggesting that H preferably get attached to C than Si. AFM investigations show decrease in rms surface roughness and grain size with increase in T_Fil. SEM studies show that films deposited at low T_Fil has spherulites-like morphology while at high T_Fil has cauliflower-like structure. Band gap values E_Tauc and E₀₄ increases from 1.76 to 2.10 eV and from 1.80 to 2.21 eV respectively, when T_Fil was increased from 1500 to 2000 °C. These result show increase in band tail width (E₀₄–E_Tauc) of multiphase SiC films. Electrical properties revealed that σ_Dark increases from ~7.87 × 10^?10 to 1.54 × 10^?5 S/cm and E_act decreases from 0.67 to 0.41 eV, which implies possible increase in unintentional doping of oxygen or nitrogen due to improved crystallinity and Si–C bond density with increase in T_Fil. The deposition rate for the films was found moderately high (21 < r_dep < 30 Å/s) over the entire range of T_Fil studied.     

Pharmacophore mapping of arylbenzothiophene derivatives for MCF cell inhibition using classical and 3D space modeling approaches

Considering the worth of developing non-steroidal estrogen analogs, the present study explores the pharmacophore features of arylbenzothiophene derivatives for inhibitory activity to MCF-7 cells using classical QSAR and 3D space modeling approaches. The analysis shows that presence of phenolic hydroxyl group and ketonic linkage in the basic side chain of 2-arylbenzothiophene core of raloxifene derivatives are crucial. Additionally piperidine ring connected through ether linkage is favorable for inhibition of breast cancer cell line. These features for inhibitory activity are also highlighted through 3D space modeling approach that explored importance of critical inter features distance among HB-acceptor lipid, hydrophobic and HB-donor features in the arylbenzothiophene scaffold for activity.     

Automated Semantic Analysis of Schematic Data

Content in numerous Web data sources, designed primarily for human consumption, are not directly amenable to machine processing. Automated semantic analysis of such content facilitates their transformation into machine-processable and richly structured semantically annotated data. This paper describes a learning-based technique for semantic analysis of schematic data which are characterized by being template-generated from backend databases. Starting with a seed set of hand-labeled instances of semantic concepts in a set of Web pages, the technique learns statistical models of these concepts using light-weight content features. These models direct the annotation of diverse Web pages possessing similar content semantics. The principles behind the technique find application in information retrieval and extraction problems. Focused Web browsing activities require only selective fragments of particular Web pages but are often performed using bookmarks which fetch the contents of the entire page. This results in information overload for users of constrained interaction modality devices such as small-screen handheld devices. Fine-grained information extraction from Web pages, which are typically performed using page specific and syntactic expressions known as wrappers, suffer from lack of scalability and robustness. We report on the application of our technique in developing semantic bookmarks for retrieving targeted browsing content and semantic wrappers for robust and scalable information extraction from Web pages sharing a semantic domain. This work has been conducted while the author was at Stony Brook University.     

Effect of cooling rate in overageing and other thermomechanical process parameters on grain refinement in an AA7475 aluminium alloy

Fine-grained AA7475 aluminium alloy sheets were produced in this study by a thermomechanical treatment involving solution anneal, overageing, rolling and recrystallization steps. It has been found that the cooling rate after the intermediate overageing treatment should be fast to obtain the finest grain size. The fast cooling rate ensured the presence of relatively large particles of MgZn₂ and some supersaturation prior to cold rolling. Generally, the final grain structure was heterogeneous, with bands of fine grains lying parallel to the rolling direction. In material rapidly cooled after overageing, bands of fine grains were also observed in the transverse direction and these bands were associated with shear bands formed during rolling. The fine-grained AA7475 alloy sheets with an average grain size of about 9 m showed large tensile elongations of about 800% when deformed at 516 °C and with an initial strain rate of 5×10^–4s^–1.