Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A new method for designing high efficiency multi feed multi beam reflector antennas

A novel method to design a high efficiency multi feed offset paraboloidal reflector antenna to be used in broadband multiband applications is presented. This method controls the arrangement of feed positions in order to increase the efficiency and gain of the whole structure. The structure has been designed, simulated and measured based on the proposed method in the frequency range from 6 GHz to 18 GHz and has the advantages of high efficiency and high gain. There is an excellent agreement between simulation and measurement results. The proposed antenna system is a good candidate for broadband access where wide coverage and high gain are simultaneously required. The maximum gain is 27 dBi at 18 GHz and the overall coverage is 30° in the elevation plane and 6° in the azimuth plane by producing 4 distinct beams and with an aperture efficiency of more than 51%. This method provides an alternative to other optimization methods because of its fast optimization time

A Compact Ultra-Wideband Multibeam Antenna System

A compact UWB (6-18 GHz) multibeam antenna system is proposed. Design procedures comprising of ridged coaxial waveguide, radome with lens properties, and biconical antenna are presented. A novel UWB feed network consisting of a ridged coaxial waveguide with eight inputs has been designed and optimized to achieve minimum reflections as well as desired radiation pattern over the frequency range of operation. The radiating element is a biconical antenna, redesigned and optimized to meet the requirements for radiation characteristics. Another notable improvement made by our design is to employ a radome, which not only enhances the mechanical stability of the biconical antenna and protects the structure, but also it acts as a lens that improves the directivity of the radiating element. Extensive optimization procedures have been applied to all parts of the antenna system to achieve the desired performance. The whole system has been simulated using HFSS full-wave simulator. The measurement results of the fabricated system are in good agreement with simulations

A Parallel Plate Ultra-Wideband Multibeam Microwave Lens Antenna

An ultra-wideband multibeam microwave lens antenna operating from 8 GHz to 18 GHz is proposed. The antenna consists of four excitation ports connected to a parallel plate waveguide filled with a cylindrical dielectric slab, which serves as a lens in order to modify the cylindrical wavefront launched by the excitation ports. The output of the lens is a plane wave guided to a radiation aperture with a linear tapered flare. Four distinct fan-beams covering 40◦ in the azimuth plane with an elevation beamwidth of 30◦ and a minimum gain of 15 dBi have been achieved. The main advantages of our design include its relative simplicity, ease of fabrication, having a low profile, not requiring an antenna feed, and high-power handling capability. The design procedure is presented together with the optimization procedures that have been applied to all parts of the antenna system to achieve the desired performance. The proposed structure has been simulated with CST Microwave Studio software. There is an excellent agreement between the simulation and measurement results

Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait

Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in biomechanical changes in human gait. In literature, for the last 50 years, researchers are trying to observe the biomechanical changes due to DSPN by studying muscle electromyography (EMG), and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we are proposing to use Machine learning techniques to identify DSPN patients by using EMG, and GRF data. We have collected a dataset consists of three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and 3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and a newly proposed feature extraction technique scheme from literature was applied to extract the best features from the signals. The extracted feature list was ranked using Relief feature ranking techniques, and highly correlated features were removed. We have trained different ML models to find out the best-performing model and optimized that model. We trained the optimized ML models for different combinations of muscles and GRF components features, and the performance matrix was evaluated. This study has found ensemble classifier model was performing in identifying DSPN Severity, and we optimized it before training. For EMG analysis, we have found the best accuracy of 92.89% using the Top 14 features for features from GL, VL and TA muscles combined. In the GRF analysis, the model showed 94.78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals. The performance of ML-based DSPN severity classification models, improved significantly, indicating their reliability in DSPN severity classification, for biomechanical data.Comment: 17 pages, 15 figures, 8 table