In the current digitalized world, cloud computing becomes a feasible solution for the virtualization of cloud computing resources. Though cloud computing has many advantages to outsourcing an organization’s information, but the strong security is the main aspect of cloud computing. Identity authentication theft becomes a vital part of the protection of cloud computing data. In this process, the intruders violate the security protocols and perform attacks on the organizations or user’s data. The situation of cloud data disclosure leads to the cloud user feeling insecure while using the cloud platform. The different traditional cryptographic techniques are not able to stop such kinds of attacks. BB84 protocol is the first quantum cryptography protocol developed by Bennett and Brassard in the year 1984. In the present work, three ways BB84GA security systems have been demonstrated using trusted cryptographic techniques like an attribute-based authentication system, BB84 protocol, and genetic algorithm. Firstly, attribute-based authentication is used for identity-based access control and thereafter BB84 protocol is used for quantum key distribution between both parties and later the concept of genetic algorithm is applied for encryption/decryption of sensitive information across the private/public clouds. The proposed concept of involvement of hybrid algorithms is highly secure and technologically feasible. It is a unique algorithm which may be used to minimize the security threats over the clouds. The computed results are presented in the form of tables and graphs.
It is often needed to have circuits that can display the decimal representation of a binary number and specifically in this paper on a 7-segment display. In this paper a circuit that can display the decimal equivalent of an n-bit binary number is designed and it’s behavior is described using Verilog Hardware Descriptive Language (HDL).
This HDL program is then used to configure an FPGA to implement the designed circuit.
Q-switch Nd: YAG laser of wavelengths 235nm and 1,460nm with energy in the range 0.2 J to 1J and 1Hz repetition rate was employed to synthesis Ag/Au (core/shell) nanoparticles (NPs) using pulse laser ablation in water. In this synthesis, initially the silver nano-colloid prepared via ablation target, this ablation related to Au target at various energies to creat Ag/Au NPs. Surface Plasmon Resonance (SPR), surface morphology and average particle size identified employing: UV-visible spectrophotometer, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The absorbance spectra of Ag NPs and Ag/Au NPs showed sharp and single peaks around 400nm and 410nm, respec
The current study was designed to evaluate the anti-inflammatory effect of GKB in the rat model of granulomatous inflammation. Thirty rats were distributed into five groups: The first group served as negative control group that received distilled water (DW) only without inducting inflammation, positive control group; treated with DW with the induction of inflammation and they were assigned to cotton pellet-induced granuloma, ginkgo biloba (GKB) treated group (200mg/kg/day), dexamethasone-treated group (1mg/kg), and Prednisolone treated group (5mg/kg). All the treatments were given orally for seven consecutive days. On day eight, the rats were anesthetized and the pellets together with granulation tissue were carefully removed
... Show MoreThe Na-alginate bead is commonly used in biotechnology fields such as adsorption due to ion exchange between Ca and Na with elements. Scanning electron microscopy (SEM-EDX) has proven to be a comparative method in the detections of these adsorbed elements, but the un-flat forming area of beads that can introduce impossible of the detection of element adsorbed. In contrast, X-ray fluorescence (XRF) documents analysis of elements, direct examination, which may analysis the adsorbents of elements. Here, this Study evaluated the possibility by using XRF for the direct analysis for examples of Cd and Ag in a bench stand. This Study compared this to commonly use
... Show MoreChannel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T
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