An efficient singular value decomposition algorithm for digital audio watermarking

The singular value decomposition (SVD) mathematical technique is utilized, in this paper, for audio watermarking in time and transform domains. Firstly, the audio signal in time or an appropriate transform domain is transformed to a 2-D format. The SVD algorithm is applied on this 2-D matrix, and an image watermark is added to the matrix of singular values (SVs) with a small weight, to guarantee the possible extraction of the watermark without introducing harmful distortions to the audio signal. The transformation of the audio signal between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. A comparison study is presented in the paper between the time and transform domains as possible hosting media for watermark embedding. Experimental results are in favor of watermark embedding in the time domain if the distortion level in the audio signal is to be kept as low as possible with a high detection probability. The proposed algorithm is utilized also for embedding chaotic encrypted watermarks to increase the level of security. Experimental results show that watermarks embedded with the proposed algorithm can survive several attacks. A segment-by-segment implementation of the proposed SVD audio watermarking algorithm is also presented to enhance the detectability of the watermark in the presence of severe attacks.     

Low-power high-speed interconnection networks in NOC using multi-valued current-mode techniques

With the fast developments and growth of electronic applications and a great demand for increasing the speed of processors, VLSI circuits and on-chip interconnections the ＂Networks-On-Chip＂ （NOC） is the best solution for ＂System-On-Chip＂ （SOC）. The most fundamental part of networks On-Chip is the On-Chip interconnection. The model presented in this article describes the method of sending data on On-Chip interconnection by using a current-mode multi-valued logic instead of voltage-mode in order to increase speed and reduce the number of interconnection wires.     

Comparative effects of gamma and microwave irradiation on the quality of black pepper

Powdered black pepper from Egyptian markets, was irradiated with different recommended doses of gamma rays (5.0 and 10.0 kGy) and with microwaves for different periods (20, 40 and 75 s) to improve its hygienic quality. The most common bacterial isolates were of three generaBacillus, Clostridium andMicrococcus (7.5 × 10⁶), whereas the predominant fungi (7.8 × 10⁴) wereAspergillus species,A. glaucus, A. flavus, A. niger andA. ochraceus. Doses of gamma irradiation used (5.0 and 10 kGy) were sufficient to decrease spore-forming bacteria (SFB) and to inhibit the fungal flora and coliforms which contaminated the black pepper powder. Microwave treatments for 40 s and 75 s were of the same effectiveness whereas treatment for 20 s was less so. GLC analysis proved the presence of 31 peaks, only 19 compounds were identified as monoterpene hydrocarbons (56.21%), the major one being -phellandrene and limonene. Sesquiterpenes were also present, mainly -caryollphyllene (3.69%) as well as oxygenated compounds such as terpenol, geraniol, Me-chavicol, eugenol and anisol. Gamma irradiation at 5 kGy and 10 kGy respectively decreased the numbers of identified compounds from 21 (86.58% concentration) in untreated pepper to 16 (59.22% concentration), 15 (54.06% concentration). In comparison, microwave treatments, particularly for 40 s and 75 s, increased the concentration of the same compounds. The results obtained indicate that microwave treatment, under these conditions, is a safe and suitable technique for decontamination of black pepper which does not result in a great loss of flavour compounds, as compared with recommended doses of gamma irradiation.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Thermal blooming and photoluminescence characterizations of sol–gel CdO–SiO<Subscript>2</Subscript> with different nanocomposite

The CdO NPs was synthesized using the sol–gel method and the nanoparticles were characterized using an UV–Vis spectrophotometer, with shape and size were examined by SEM and XRD. The XRD analysis respects the Bragg’s law and confirmed the crystalline nature of CdO nanoparticles. From the XRD, the average size of CdO NPs was found to be around 41 nm. The photoluminescence spectra of the CdO NPs, as recorded at room temperature, were excited at 300 nm wavelength. The broad emission peaks were between 600 and 650 nm (orange emission). The optical limiting performance of the nanocomposite was described in the sol–gel state. Also, this study has observed and studied the diffraction rings generated in CdO NPs using the same CW laser. The number of rings increases almost exponentially with an increasing volume fraction of SiO₂ in the nanocomposites. The refractive index change, Δn, and effective nonlinear refractive index, n ₂, were found to be 10^?4 and 10^?8 cm²/W, respectively. The effective nonlinear refractive index, n ₂, was determined based on the observed number of rings. The threshold values of the CdO, CdO–2SiO₂ and CdO–5SiO₂ nanocomposites are 7.1, 6.55 and 6.34 mW, respectively. This large nonlinearity is attributed to the thermal effect. The present studies suggest that the nanocomposite is a potential candidate for optical device applications such as the optical limiters. The thermal blooming technique was applied to evaluate the thermo-optic coefficient and thermal diffusivity of the CdO NPs. In the thermal blooming experimental setup a transistor–transistor logic modulated CW laser of wavelength 532 nm was used as the excitation source.     

Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Formulation and Biodistribution of <Superscript>99<Emphasis Type="Italic">m</Emphasis></Superscript>Tc-Dacarbazine,a Radioligand for Neoplasm Imaging

A method for preparing ^99mTc-Dacarbazine, a potential cancer diagnostic agent, was developed. The method is based on direct labeling of Dacarbazine with ^99mTc in the presence of stannous chloride as reducing agent. The reaction conditions were optimized to obtain the radiochemical yield of ^99mTc-Dacarbazine higher than 90%. The biodistribution of ^99mTc-Dacarbazine in normal and tumor-bearing Albino Swiss mice was studied. ^99mTc-Dacarbazine has a high tumor affinity and shows promise for cancer imaging. Quantitative relationship between the optimized structures and computed molecular properties related to Dacarbazine binding sites was analyzed using molecular modeling.     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...