Nazish Irfan

Neural-based approach for localization of sensors in indoor environment

Location of wireless sensor nodes is an important piece of information for many applications. There are many algorithms present in literature based on Received Signal Strength (RSSI) to estimate the location. However the radio signal propagation is easily influenced by diffraction, reflection and scattering. Therefore algorithms purely based on RSSI may not accurately predict the position of the node.In the present work, an algorithm for estimating the position of mobile nodes is proposed which is based on a combination of Received Signal Strength (RSSI) and Link Quality Indicator (LQI). Artificial Neural Networks are used to establish the relationship between the location of the mobile node and the experimentally obtained values of RSSI and LQI. Two different algorithms namely, Bayesian Regularization and Gradient Descent are used to develop the neural network model. Proposed algorithms improve the localization accuracy and perform better than other state-of-the-art algorithms.

Redundant reader elimination approaches for RFID networks

Radio Frequency Identification (RFID) systems, due to recent technological advances, have been used for various advantages in industries like production facilities, supply chain management etc. However, sometimes this implies a dense deployment of readers to cover the working area. Without optimizing readers location and number, many of them will be redundant, reducing the efficiency of the whole RFID system. There are many algorithms proposed by researchers to solve redundant reader problem, but all the algorithms are based on omnidirectional reader antenna pattern, which is not practical. Therefore, the approach proposed in this paper was first applied to RFID readers using omni-directional antenna and thus, extended to directional reader antennas. Simulation results demonstrate that our approach for omnidirectional reader antenna outperforms other well-known techniques like Redundant Reader Elimination (RRE) algorithm, Layered Elimination Optimization (LEO) and LEO+RRE while preserving the coverage ratio quite close to those obtained by RRE, LEO and LEO+RRE. Then, we extended the concept to real directional reader antenna pattern using genetic algorithm to optimize the antenna beam to eliminate redundant readers.

Genetic-Based Approach for Efficient RFID Reader Antenna Positioning

—Radio Frequency Identification (RFID) systems, due to recent technological advances, have been used for various advantages in industries like production facilities, supply chain management etc. Read accuracy and coverage achieved by readers are some of the critical factors to be considered in the adoption of any RFID system. For passive tags, these parameters depend not only on the volume of region that receives sufficient power from the reader but also on the relative orientation of reader and tag antennas as well as their polarization. In this work, we used Genetic Algorithm to optimize the directional antenna beam direction including down tilt to maximize the reader coverage and accuracy. This work can be used as an effective tool in warehouse management and logistics.

Genetic algorithm based efficient tag detection in RFID reader networks

Radio Frequency Identification (RFID) systems, due to recent technological advances, have been used for various advantages in industry like production facilities, supply chain management etc. However, sometimes this requires a dense deployment of readers to cover the working area. Without optimizing readers location and number, many of them will be redundant, reducing the efficiency of the whole RFID system. There are many algorithms proposed to solve this redundant reader problem, but all existing algorithms are based on omnidirectional reader antenna pattern, which is not practical. In this paper, a genetic algorithm is used to optimize the antenna beam to eliminate redundant reader based on real directional reader antenna pattern.

Efficient Approach for Redundant Reader Elimination in Large-Scale RFID Networks

Radio Frequency Identification (RFID) systems, due to recent technological advances, are being deployed in large scale for different applications. However, this requires a dense deployment of readers to cover the working area. Without optimizing reader’s distribution and number, many of them will be redundant, reducing the efficiency of the whole RFID system. The problem of eliminating redundant readers has motivated researchers to propose different algorithms and optimization techniques. In this paper, the authors present an efficient redundant reader elimination technique based on weights associated with reader’sneighbor and coverage. Simulation results demonstrate that the proposed algorithm eliminates more redundant readers than those of other well-known techniques like Redundant Reader Elimination (RRE) algorithm, Layered Elimination Optimization (LEO) and LEO+RRE while preserving the coverage ratio quite close to those obtained by RRE, LEO and LEO+RRE.

Miniature single-sideband subharmonically-pumped 60 GHz direct upconverter in a uniplanar GaAs pHEMT technology using inductive and capacitive loading techniques

This paper presents a novel, compact, single-sideband (SSB) subharmonically-pumped (SHP) direct upconverter developed in a uniplanar 0.18 mu m GaAs technology. A total of 100 MHz in-phase and quadrature signals directly modulate the second harmonic of a 30 GHz carrier signal, producing a 60.1 GHz output. Two pairs of antiparallel diodes reduce feed-through of the 30 GHz local oscillator (LO) signal to the mixers RF output. Novel structures patterned in the center conductor of a coplanar waveguide (CPW) provide matching and size-reduction simultaneously. The 2.1mm(2) circuit also uses a miniaturized Wilkinson divider based on asymmetric coplanar stripline and a standard CPW 90 degrees coupler. The SSB SHP direct upconverter exhibits a conversion loss of 10 dB, a lower-sideband rejection of 15 dB and 2f(LO) suppression of approximately 25 dB over a wide frequency range from 52-61 GHz.