Flexible ku/k band frequency reconfigurable bandpass filter

<abstract> <p>The proposed reconfigurable BPF satisfies the International Telecommunication Unionos (ITU) region 3 spectrum requirement. In transmit mode, the frequency range 11.41-12.92 GHz is used by the direct broadcast service (DBS) and the fixed satellite service (FSS). Direct broadcast service (DBS) in reception mode employs 11.7-12.2 GHz and 17.3-17.8 GHz frequency ranges. Frequency reconfigurable filters are popular because they can cover wide range of frequencies, reducing system cost and space. Another emerging trend is electronic component flexibility or conformability, which allows them to be mounted on non-planar objects and are used in wearable applications. This project contains a frequency-reconfigurable BPF that has been entirely printed on a flexible polimide substrate. Frequency reconfigurability is obtained by using a pin diode HSCH 5318 and it is used to switch between 12 GHz and 18 GHz. The prototype reconfigurable BPF is highly compact and low-cost due to the flexible polimide substrate and the measured results are promising and match the simulated results well.</p> </abstract>

Millimetre wave 3-D channel modelling for next generation 5G networks

<abstract><p>Millimetre wave (mm-wave) spectrum (30-300GHz) is a key enabling technology in the advent of 5G. However, an accurate model for the mm-wave channel is yet to be developed as the existing 4G-LTE channel models (frequency below 6 GHz) exhibit different propagation attributes. In this paper, a spatial statistical channel model (SSCM) is considered that estimates the characteristics of the channel in the 28, 60, and 73 GHz bands. The SSCM is used to mathematically approximate the propagation path loss in different environments, namely, Urban-Macro, Urban-Micro, and Rural-Macro, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. The New York University (NYU) channel simulator is utilised to evaluate the channel model under various conditions including atmospheric effects, distance, and frequency. Moreover, a MIMO system has been evaluated under mm-wave propagation. The main results show that the 60 GHz band has the highest attenuation compared to the 28 and 73 GHz bands. The results also show that increasing the number of antennas is proportional to the condition number and the rank of the MIMO channel matrix.</p></abstract>

Genetically engineered tri-band microstrip antenna with improved directivity for mm-wave wireless application

<abstract><p>Multi-band microstrip patch antennas are convenient for mm-wave wireless applications due to their low profile, less weight, and planar structure. This paper investigates patch geometry optimization of a single microstrip antenna by employing a binary coded genetic algorithm to attain triple band frequency operation for wireless network application. The algorithm iteratively creates new models of patch surface, evaluates the fitness function of each individual ranking them and generates the next set of offsprings. Finally, the fittest individual antenna model is returned. Genetically engineered antenna was simulated in ANSYS HFSS software and compared with the non-optimized reference antenna with the same dimensions. The optimized antenna operates at three frequency bands centered at 28 GHz, 40 GHz, and 47 GHz whereas the reference antenna operates only at 28 GHz with a directivity of 6.8 dB. Further, the test result exhibits broadside radiation patterns with peak directivities of 7.7 dB, 12.1 dB, and 8.2 dB respectively. The covered impedance bandwidths when S₁₁$ \leq $-10 dB are 1.8 %, 5.5 % and 0.85 % respectively.</p></abstract>

A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

<abstract> <p>The evolution of smart meters has led to the generation of high-resolution time-series data - a stream of data capable of unveiling valuable knowledge from consumption behaviours for different applications. The ability to extract hidden knowledge from such massive amounts of data requires that it be analysed intelligently. Hence, for a clear representation of the various consumption behaviours of consumers, a good number of data mining technologies are usually employed. This paper presents a systematic review of the various data mining techniques and methodologies employed while profiling energy data streams. The review identifies the strengths and shortcomings of existing data mining methods as applied in research, focusing more on data processing techniques and load clustering. Also discussed are data mining methods used to profile consumption data, their pros and cons. It was inferred during the research that the choice of data mining technique employed is highly dependent on the application it is intended for and the intrinsic nature of the dataset.</p> </abstract>

Application of Genetic Algorithm for Binary Optimization of Microstrip Antennas: A Review

<abstract> <p>Researchers have proposed applying optimization techniques to improve performance of microstrip antennas (MSAs) in terms of bandwidth, radiation characteristics, polarization, directivity and size. The drawbacks of the conventional MSAs can be overcome by optimizing the antenna parameters while keeping a compact configuration. Applying a global optimizer is a better technique than using a local optimizer or a trial and error method for performance enhancement. This paper discusses genetic algorithm (GA) optimization of microstrip antennas presented by the antenna research community. The GA optimization procedure, antenna parameters optimized by using GA and the optimization objectives are presented by reviewing the literature. Further, evolution of GA in the field of MSAs and its significance are explored. Application of GA optimization to design broadband, multiband, high-directivity and miniature antennas is demonstrated with the support of several case studies giving an insight for further developments in the field.</p> </abstract>

Bi-LSTM based deep learning method for 5G signal detection and channel estimation

<abstract> <p>The advanced neural network methods solve significant signal estimation and channel characterization difficulties in the next-generation 5G wireless communication systems. The number of transmitted signal copies received through multiple paths at the receiver leads to delay spread, which intern causes interference in communication. These adverse effects of the interference can be mitigated with the orthogonal frequency division modulation (OFDM) technique. Furthermore, the proper signal detection methods optimal channel estimation enhances the performance of the multicarrier wireless communication system. In this paper, bi-directional long short-term memory (Bi-LSTM) based deep learning method is implemented to estimate the channel in different multipath scenarios. The impact of the pilots and cyclic prefix on the performance of Bi LSTM algorithm is analyzed. It is evident from the symbol-error rate (SER) results that the Bi-LSTM algorithm performs better than the state of art channel estimation methods known as the Minimum Mean Square and Error (MMSE) estimation method.</p> </abstract>

Mitigation strategies for communication networks induced impairments in autonomous microgrids control: A review

<abstract> <p>The advancement in communication technology and the availability of intelligent electronic devices (IEDs) have impacted positively on the penetration of renewable energy sources (RES) into the main electricity grid. High penetration of RES also come along with greater demand for more effective control approaches, congestion management techniques, and microgrids optimal dispatch. Most of the secondary control methods of microgrid systems in the autonomous mode require communication links between the distributed generators (DGs) for sharing power information and data for control purposes. This article gives ample review on the communication induced impairments in islanded microgrids. In the review, attention is given to communication induced delay, data packet loss, and cyber-attack that degrades optimal operations of islanded microgrids. The review also considered impairments modelling, the impact of impairments on microgrids operation and management, and the control methods employed in mitigating some of their negative impacts. The paper revealed that innovative control solutions for impairment mitigation rather than the development of new high-speed communication infrastructure should be implemented for microgrid control. It was also pointed out that a sparse communication graph is the basis for communication topology design for distributed secondary control in the microgrid.</p> </abstract>

Iris-Fingerprint multimodal biometric system based on optimal feature level fusion model

<abstract><p>For reliable and accurate multimodal biometric based person verification, demands an effective discriminant feature representation and fusion of the extracted relevant information across multiple biometric modalities. In this paper, we propose feature level fusion by adopting the concept of canonical correlation analysis (CCA) to fuse Iris and Fingerprint feature sets of the same person. The uniqueness of this approach is that it extracts maximized correlated features from feature sets of both modalities as effective discriminant information within the features sets. CCA is, therefore, suitable to analyze the underlying relationship between two feature spaces and generates more powerful feature vectors by removing redundant information. We demonstrate that an efficient multimodal recognition can be achieved with a significant reduction in feature dimensions with less computational complexity and recognition time less than one second by exploiting CCA based joint feature fusion and optimization. To evaluate the performance of the proposed system, Left and Right Iris, and thumb Fingerprints from both hands of the SDUMLA-HMT multimodal dataset are considered in this experiment. We show that our proposed approach significantly outperforms in terms of equal error rate (EER) than unimodal system recognition performance. We also demonstrate that CCA based feature fusion excels than the match score level fusion. Further, an exploration of the correlation between Right Iris and Left Fingerprint images (EER of 0.1050%), and Left Iris and Right Fingerprint images (EER of 1.4286%) are also presented to consider the effect of feature dominance and laterality of the selected modalities for the robust multimodal biometric system.</p></abstract>