Sajjad Ali Haider

Solar energy potential in Pakistan

In view of the growing needs of energy in Pakistan, the efficient use and development of renewable energy sources has become a major issue in the country. This has brought the intention of several national and multinational companies to design and implement a major work plan for energy conservation and construction of renewable energy sources like wind mills and solar panels. Fortunately, Pakistan is among those countries in which sun warms the surface throughout the year and therefore has a strong potential for solar power generation. This study was conducted to explore those areas which are most suitable for solar energy potential using fifty eight meteorological stations covering the whole country. Angstrom equation and Hargreaves formula was used to calculate monthly solar energy potential by utilizing monthly climatical data of bright sunshine hours, mean maximum and minimum temperatures. The lowest solar radiation intensity 76.49 W/m2 observed at Cherat during December and highest 339.25 W/m2 at Gil...

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

RETRACTED: Fresnelet approach for image encryption in the algebraic frame

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Classification and Assessment of Aridity Over Pakistan Provinces (1960-2009)

10-9.

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

Considering the effectiveness of outcome-based education and its increasing implementation in higher education, we propose a set of course learning outcomes that may be related to any engineering problem, in particular the final year project in undergraduate engineering programs. We also show how these outcomes may be mapped to program learning outcomes identified by the Washington Accord. Our case study is an embedded vision system developed by our own group, which we assess against these outcomes using the proposed self-assessment report rubrics. We conclude by presenting assessment reports for our project from two expert academics showing how well it managed to attain the course learning outcomes. This work is intended to give students a direction to professionally as well as ethically approach a particular design problem, and at the same time help the instructors evaluate their students’ projects by simply adopting the proposed assessment methodology.