Accepted to IET MAP Journal Feb 2017 Design of Frequency Reconfigurable Multiband Compact Antenna using two PIN diodes for WLAN/WiMAX Applications Abstract:In this paper, we present a simple reconfigurable multiband antenna with two PIN diode switches for WiMAX/WLAN applications. The antenna permits reconfigurable switching in up to ten frequency bands between 2.2 GHz and 6 GHz, with relative impedance bandwidths of around 2.5% and 8%. The proposed antenna has been simulated using CST microwave studio software and fabricated on an FR-4 substrate. It is compact, with an area of 50 × 45 mm 2 , and has a slotted ground substrate. Both measured and simulated return loss characteristics of the optimized antenna show that it satisfies the requirement of 2.4/5.8 GHz WLAN and 3.5 GHz WiMAX antenna applications. Moreover, there is good agreement between the measured and simulated result in terms of radiation pattern and gain.

The advent of the Internet of Things has witnessed tremendous success in the application of wireless sensor networks and ubiquitous computing for diverse smart-based applications. The developed systems operate under different technologies using different methods to achieve their targeted goals. In this treatise, we carried out an inclusive survey on key indoor technologies and techniques, with to view to explore their various benefits, limitations, and areas for improvement. The mathematical formulation for simple localization problems is also presented. In addition, an empirical evaluation of the performance of these indoor technologies is carried out using a common generic metric of scalability, accuracy, complexity, robustness, energy-efficiency, cost and reliability. An empirical evaluation of performance of different RF-based technologies establishes the viability of Wi-Fi, RFID, UWB, Wi-Fi, Bluetooth, ZigBee, and Light over other indoor technologies for reliable IoT-based applications. Furthermore, the survey advocates hybridization of technologies as an effective approach to achieve reliable IoT-based indoor systems. The findings of the survey could be useful in the selection of appropriate indoor technologies for the development of reliable real-time indoor applications. The study could also be used as a reliable source for literature referencing on the subject of indoor location identification.

No abstract

No abstract

Human activity recognition from sensor data is a fundamental research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed at enabling people's capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Moreover, new technological approaches to support AAL within the home or community setting offers people the prospect of more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A twolayer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is implemented. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart homes and smart health and offers a pervasive sensing environment for the elderly, persons with disability and chronic illness.

No abstract

No abstract

No abstract