Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect

The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained

Estimating the Quality of Digitally Transmitted Speech over Satellite Communication Channels

Analogue speech signal is one of the most natural means used by humans for communication purposes. The emergence of digital modulation and coding techniques has made the transmission of analogue speech (as digital content) over various conduits possible, albeit with inevitable signal degradation as a result of errors inherent in the conversion process. A need naturally arises for determining the quality of speech received at the information sink, with a view to enhancing its robustness to degradation suffered in transit over the communication channel. We present in this paper analytic methods of qualitative assessment of the quality of recovered digitally transmitted speech. A methodology for determining the intelligibility of speech by using segmental SNR gotten by dividing the speech signal into M integer segments is proposed. This methodology has the following advantages: a) it allows for assessing the dynamics of change of speech quality in real-time through statistical modeling, b) it obviates the need for expensive, yet subjective experimental approaches like MOS, and c) it takes into consideration not only the signal power, but also its spectral characteristics which is a step above the use of Modulated Noise Reference Units (MNRUs). Using the obtained results, a procedure for analysis of speech intelligibility by means of statistical modeling is developed. Keywords: Speech processing, Mean opinion score, MOS, SNR, PCM, Quantization nois

Negative resistance amplifier circuit using GaAsFET modelled single MESFET

Negative resistance devices have attracted much attention in the wireless communication industry because of their low cost, better performance, high speed, and reduced power requirements. Although negative resistance circuits are non-linear circuits, they are associated with distortion, which may either be amplitude-to-amplitude distortion or amplitude-to-phase distortion. In this paper, a unique way of realizing a negative resistance amplifier is proposed using a single metal-semiconductor field-effect transistor (MESFET). Intermodulation distortion test (IMD) is performed to evaluate the characteristic response of the negative resistance circuit amplifier to different bias voltages using the harmonic balance (HB) of the advanced designed software (ADS 2016). The results obtained are compared to those of a conventional distributed amplifier. The findings of this study showed that the negative resistance amplifier spreads over a wider frequency output with reduced power requirements while the conventional distributed amplifier has a direct current (DC) offset with output voltage of 32.34 dBm

Cost-Effective Medical Robotic Telepresence Solution using Plastic Mannequin

Robotic telepresence is an Information and Communication Technology (ICT) solution that has a huge potential to address the problem of access to quality healthcare delivery in rural areas. However, the capital and operating costs of available systems are considered to be unffordable for rural dwellers in emerging economies. In addition, most of these communities are not even connected to the power grid. In this paper, the authors reduced the cost of engaging a robotic telepresence solution for rural medicare by using plastic mannequin and solar photovoltaic technology. An IP camera was fixed in each of the eye sockets of the plastic mannequin. These cameras are connected to a mini-computer embedded in the plastic mannequin. A Wi-Fi module establishes an Internet connection between remote physicians and rural heathcare facilities. The system is powered by a solar photovoltaic energy source to guarantee power availability. Another unique feature of this solution is that it gives the patient a better impression of the physical presence of a physician. Comparative cost analysis with robotic telepresence available in the market showed that our system is more affordable. This development will increase the adoption of robotic telepresense in rural telemedicine

A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system

In Content-Based Image Retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on Principal Component Analysis (PCA) is implemented. Each image in a database is indexed using 174 dimensional feature vector comprising of 54-dimensional Colour Moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional Gabor Wavelet (GW48) and 40-dimensional Wavelet Moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest Eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100

RESEARCH TRENDS IN NIGERIAN UNIVERSITIES : ANALYSIS OF NUMBER OF PUBLICATIONS IN SCOPUS (2008 - 2017

Among other things, the performance of a university can be measured based on the volume and the impact of their scholarly research publications . However, the empirical evidence that are needed for objective analysis, evaluation, and ranking of universitie s based on this factor are often not readily and freely accessible to the public. In this paper , the trends of research publications in Nigerian Universities are analyzed . The total number of scholarly articles published by academic researchers in 67 Niger ian universities over a period of ten years (2008 - 2017) were sourced from Scopus abstracting/indexing database. Nigerian universities covered include 32 federal universities, 26 state universities, and nine private universities. The publication trends are presented using tables and graphs. Also, yearly percentage growth in scholarly research outputs are computed for each university. In practice, the insights provided will propel a more informed policy formulation and implementation towards improving institu tional academic research productivity

Data-driven optimal planning for hybrid renewable energy system management in smart campus: a case study

Academic and research institutions need to be at the forefront of research and development efforts on sustainable energy transition towards achieving the 2030 Sustainable Development Goal 7. Thus, the most economically feasible hybrid renewable energy system (HRES) option for meeting the energy demands of Covenant University was investigated in this study. Several optimal combinations of energy resource components and storage which have significant potentials within the university campus were modeled on HOMER software in grid-connected mode. The daily energy consumption data of Covenant University were measured using EDMI Mk10E digital energy meter for a whole year. Data for analyzing renewable energy potentials for several years were sourced from the NASA database through the HOMER platform. Significantly, due to the fluctuating price of diesel fuel in Nigeria, sensitivity analysis was carried out for each combination using diesel fuel prices ranging from 0.3 $/litre to 1$/litre. The results of each projected combination which gave 32 simulation scenarios, were analyzed comparatively using eight important system performance indices which cover economic, technical, and environmental impact assessment with and without battery energy systems. The results of the comparative analysis showed that the PV-Diesel-Grid-BESS HRES is the best configuration for meeting the Covenant university load demands in terms of credible reduction in the net present cost and cost of electricity. However, deployment of the wind energy system is economically infeasible at the study site, while the diesel generator should be strictly a backup

Multi-instance contingent fusion for the verification of infant fingerprints

It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints