Syed Rameez Naqvi

Reversible integer wavelet transform for blind image hiding method

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image.

Image de-noising with subband replacement and fusion process using bayes estimators, ☆ ☆☆

Abstract A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to handle substantially high noise with an unknown variance. The impetus of the framework is to preserve the latent detail information of the noisy image while removing the noise with an appropriate smoothing and feasible sharpening. The proposed method is executed in two steps. First, the sub-band replacement and fusion process based on accelerated version of the Bayesian non local means method are implemented to enhance the weak edges that often result in low gradient magnitude and fade out during the de-noising process. Then, a truncated beta-Bernoulli process is employed to infer an appropriate dictionary of the edge enhanced data to obtain de-noising results precisely. Numerical simulations are performed to substantiate the restoration of the weak edges through sub-band replacement and fusion process. The proposed de-noising scheme is validated through visual and quantitative results using well established metrics.

A dynamically reconfigurable logic cell: from artificial neural networks to quantum-dot cellular automata

Considering the lack of optimization support for Quantum-dot Cellular Automata, we propose a dynamically reconfigurable logic cell capable of implementing various logic operations by means of artificial neural networks. The cell can be reconfigured to any 2-input combinational logic gate by altering the strength of connections, called weights and biases. We demonstrate how these cells may appositely be organized to perform multi-bit arithmetic and logic operations. The proposed work is important in that it gives a standard implementation of an 8-bit arithmetic and logic unit for quantum-dot cellular automata with minimal area and latency overhead. We also compare the proposed design with a few existing arithmetic and logic units, and show that it is more area efficient than any equivalent available in literature. Furthermore, the design is adaptable to 16, 32, and 64 bit architectures.

Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision☆

Abstract Considering the incessantly increasing economic losses due to plant diseases in the agricultural sector, we have designed a real-time system capable of classifying plant diseases. In this context, we have proposed an image processing algorithm that transforms the image into three colorspaces, which are processed simultaneously. The algorithm executes in a series of intermediate steps, including contrast stretching, feature vector construction, and identification of salient regions. To enable effective execution, we have also proposed the underlying On-Chip communication architecture that allows efficient interconnection between the three digital signal processing cores, each processing its own colorspace. The architecture has been synthesized for 90 nm process, as well as on an FPGA, achieving a post-layout operational frequency of 644 MHz, and an area of 1208.9 µm 2 on the die. We demonstrate that our system outperforms few existing works in literature in terms of accuracy and computation time.

Artificial neural networks based dynamic priority arbitration for asynchronous flow control

Accesses to physical links in Networks-on-Chip need to be appropriately arbitrated to avoid collisions. In the case of asynchronous routers, this arbitration between various clients, carrying messages with different service levels, is managed by dedicated circuits called arbiters. The latter are accustomed to allocate the shared resource to each client in a round-robin fashion; however, they may be tuned to favor certain messages more frequently by means of various digital design techniques. In this work, we make use of artificial neural networks to propose a mechanism to dynamically compute priority for each message by defining a few constraints. Based on these constraints, we first build a mathematical model for the objective function, and propose two algorithms for vector selection and resource allocation to train the artificial neural networks. We carry out a detailed comparison between seven different learning algorithms, and observe their effectiveness in terms of prediction efficiency for the application of dynamic priority arbitration. The decision is based on input parameters: available tokens, service levels, and an active request from each client. The performance of the learning algorithms has been analyzed in terms of mean squared error, true acceptance rate, number of epochs and execution time, so as to ensure mutual exclusion.

Modeling electrical properties for various geometries of antidots on a superconducting film

Electrical properties, specifically critical current density, of a superconducting film carry a substantial importance in superconductivity. In this work, we measure and study the current–voltage curves for a superconducting Nb film with various geometries of antidots to tune the critical current. We carry out the measurements on a commercially available physical property measurement system to obtain these so-called transport measurements. We show that each of the used geometries exhibits a vastly different critical current, due to which repeatedly performing the measurements independently for each geometry becomes indispensable. To circumvent this monotonous measurement procedure, we also propose a framework based on artificial neural networks to predict the curves for different geometries using a small subset of measurements, and facilitate extrapolation of these curves over a wide range of parameters including temperature and magnetic field. The predicted curves are then cross-checked using the physical measurements; our results suggest a negligible mean-squared error—in the order of

Learning outcomes and assessment methodology: Case study of an undergraduate engineering project

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A low error add and shift-based efficient implementation of base-2 logarithm

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