Pseudo elliptic dual-band filters based on ring structures / Zuhani Ismail Khan

In recent years, wireless transmission via high frequency electromagnetic wave has been given a lot of attention from various industries due to the emergence of new applications and new technologies. One of the most recent and interesting evolution in the wireless communications area is the trend toward the integration of multiple functions into a wireless device that can be used anywhere. One of the main components in the communication system is the microwave filter. The performance of the device is defined in terms of its transmission loss, circuit size and response selectivity. Dual-band filters are seen as the solution for circuit size reduction in a dual-channel communication system

Spectrum averaging in a MIMO FMCW maritime radar for a small fluctuating target range estimation

Detection of a small maritime target has been challenging in radar signal processing due to the object size near the water surface. This paper provides an alternative detection method for a small fluctuating target by deploying a frequency modulated continuous waveform (FMCW) in a multiple-input multiple-output (MIMO) configuration. The work proposed a MIMO FMCW radar with a frequency offset between transmitted sub-bands, and the spectrum averaging (SA) scheme to combine the multiple received signals. A MIMO with an equal number of transmit and receive nodes were employed, and transceivers were co-located. The frequency-offset introduced an interval band between MIMO sub-signals to avoid interference and overlapping. The work observed range error parameters of a small fluctuating target. The result reveals that applying the SA with and without an interval band produced a better performance against signal-to-noise ratio (SNR) in terms of probability of range error and range error mean, through numerical simulations and experiments. However, MIMO caused an incremental computational complexity with the number of nodes based on Fast Fourier Transform (FFT) algorith

Multilayer hairpin bandpass filter for digital broadcasting / Robi'atun Adayiah Awang … [et al.]

A design of multilayer hairpin bandpass filter at digital broadcasting frequency has been presented. This filter has been presented based on the design specification together with the analyses of the response on the parameter sweeps of coupling gap, width and length of the resonators, metal thickness, substrate thickness and the measurement result of the fabricated circuit. This research has proposed 2.45-2.53 GHz bandpass filter using hairpin resonator in multilayer configuration for digital broadcasting application. The four-pole hairpin resonators centered at 2.5 GHz with bandwidth less than 100 MHz were designed. The best return and insertion losses in the passband are -42.96 dB and -2.55 dB, respectively. Combination of hairpin resonator operating at desired frequency has been optimized and simulated on Flame Retardant 4 (FR-4) with dielectric constant 4.6 together with the analysis using Computer Simulation Technology (CST). Design filter has been fabricated and measured using Network Analyzer and have a good agreement with simulated response. The measurement results of Sn and S21 obtained from the fabricated circuit are -19.56 dB and -7.64 dB, respectively. The analyses have proven that the design work according to the microwave theory. In addition it was observed that a wider bandwidth was achieved by increasing the number of resonators

Reconfigurable Ring Filter with Controllable Frequency Response

Reconfigurable ring filter based on single-side-access ring topology is presented. Using capacitive tuning elements, the electrical length of the ring can be manipulated to shift the nominal center frequency to a desired position. A synthesis is developed to determine the values of the capacitive elements. To show the advantage of the synthesis, it is applied to the reconfigurable filter design using RF lumped capacitors. The concept is further explored by introducing varactor-diodes to continuously tune the center frequency of the ring filter. For demonstration, two prototypes of reconfigurable ring filters are realized using microstrip technology, simulated, and measured to validate the proposed concept. The reconfigured filter using lumped elements is successfully reconfigured from 2 GHz to 984.4 MHz and miniaturized by 71% compared to the filter directly designed at the same reconfigured frequency, while, for the filter using varactor-diodes, the frequency is chosen from 1.10 GHz to 1.38 GHz spreading over 280 MHz frequency range. Both designs are found to be compact with acceptable insertion loss and high selectivity

The internet of things vision: a comprehensive review of architecture, enabling technologies, adoption challenges, research open issues and contemporary applications

With the improvements in machine-to-machine (M2M) communication, ubiquitous computing, and wireless sensor networks, the Internet of Things (IoT) has become a notion that is constantly rising in importance. Using uniquely addressable IDs, the Internet of Things links diverse physical items and allows them to communicate with one another through the Internet. A general overview of the IoT in the context of the architecture and associated technologies is provided in this article. On the other hand, the Internet of Things does not follow a standardised architecture model. This is accomplished by describing widely recognised architectural concepts that are subsequently refined with the associated technology in various tiers. Also included are some solutions that have been developed and future directions for addressing the obstacles faced by the IoT paradigm. Finally, the article discusses several Internet of Things applications to demonstrate the viability of the IoT idea in real-world setting

Wet road detection using CNN with transfer learning

here is an increasing need to detect wet road surfaces automatically considering many accidents and traffic problems that occur in wet weather. Road condition detection based on acoustic signals has gained more attention in recent years due to its low implementation cost. However, current deep learning methods for wet surface detection rely on supervised audio measurements. Furthermore, they require a large amount of training data. Recent advancements in convolutional neural networks (CNNs) have made it possible for transferring trained CNN from one dataset to another. In this study, we aim to evaluate the capabilities of pre-trained CNN models to detect wet road surfaces. Results show that transfer learning was able to discriminate between dry and wet road surfaces with an accuracy of more than 80%. Additionally, we also provide performance comparisons for the three trained models