Forward-link throughput optimization for wireless cellular packet data networks

An attempt has been made in this paper to access the control layer of the General Packet Radio Service (GPRS) network base station (BS), using throughput optimization. We focus on the effect of packet transmissions on the throughput of forwardlink (downlink) power balancing, with specific interest on the relative throughput of the four GPRS coding schemes for a realistic GPRS network operating in Nigeria. We derive a model capable of improving the channel quality and simulate the throughput and choice of optimum coding scheme over diverse packet sizes using the MATrix LABoratory toolkit. Simulation results show that the existing system requires effective network optimization to improve the system’s throughput performance.Facultad de Informátic

Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin

Relative signal strength coverage optimization in indoor and outdoor wireless LAN environments

Fading and obstacles constitute major threats to effective quality of service (QoS) delivery in wireless local area network (WLAN) environments. In this contribution, we investigate the signal quality of indoor and outdoor WLANs over a defined coverage area. We present experimental analysis of case studies that will be useful for further research and validate the system’s performance in practice. Using an optimized form of the pathloss models, a simulation of the system is carried out over short and extended coverage. Simulation results show that signal quality could be effectively managed to improve the system’s performance for both indoor and outdoor environments in the presence of fading and other environmental factors.Facultad de Informátic

A simulation agent for efficient network evaluation in 3G cellular mobile radio planning

An accurate procedure for evaluating the strong correlation and conflicting goals of coverage, capacity and Quality of Service (QoS) is required for efficient planning of 3G radio networks. In this paper, we explore and implement an agent-based simulation methodology which allows for the fine-tuning of the input model parameters to study and evaluate the performance of Code Division Multiple Access (CDMA) systems. The results obtained are represented graphically to show scenarios for both uplink and downlink limited CDMA. The analysis is extended to demonstrate how Tower Mounted Amplifiers (TMA) can be used to benefit the uplink performance of a 1× EV-D0 data system.Facultad de Informátic

Macrocellular Propagation Prediction for Wireless Communications in Urban Environments

In this paper, the signal propagation characteristics of urban environments are predicted by modeling important propagation parameters of the Code Division Multiple Access (CDMA) network for macrocells. The MOPEM propagation model has been selected as a model of choice due to its robustness in handling urban parameters. The model is simulated with some modifications using empirical data from the Visafone CDMA gathered from the Nigerian Telecommunications Limited (NITEL), Uyo, Nigeria and data from the field. From the simulation, we observe that propagation model parameters such as orientation angel, street width, building height, among others, has great influence on the system performance of CDMA wireless networks. We hope that with this research, systems designers could approach the installation of radio frequency equipment with some degree of confidence that the transmission link will effectively work, especially in urban areas.Facultad de Informátic

Relative signal strength coverage optimization in indoor and outdoor wireless LAN environments

Fading and obstacles constitute major threats to effective quality of service (QoS) delivery in wireless local area network (WLAN) environments. In this contribution, we investigate the signal quality of indoor and outdoor WLANs over a defined coverage area. We present experimental analysis of case studies that will be useful for further research and validate the system’s performance in practice. Using an optimized form of the pathloss models, a simulation of the system is carried out over short and extended coverage. Simulation results show that signal quality could be effectively managed to improve the system’s performance for both indoor and outdoor environments in the presence of fading and other environmental factors.Facultad de Informátic

A transfer learning approach to drug resistance classification in mixed HIV dataset

Funding: This research is funded by the Tertiary Education Trust Fund (TETFund), Nigeria.As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.Publisher PDFPeer reviewe

Towards an unrestricted domain TTS system for African tone languages

Ekpenyong ME, Urua E-A, Gibbon D. Towards an unrestricted domain TTS system for African tone languages. International Journal of Speech Technology. 2008;11(2):87-96.In this paper we discuss the procedural problems, issues and challenges involved in developing a generic speech synthesizer for African tone languages. We base our development methodology on the “MultiSyn” unit-selection approach, supported by Festival Text-To-Speech (TTS) Toolkit for Ibibio, a Lower Cross subgroup of the (New) Benue-Congo language family widely spoken in the southeastern region of Nigeria. We present in a chronological order, the several levels of infrastructural and linguistic problems as well as challenges identified in the Local Language Speech Technology Initiative (LLSTI) during the development process (from the corpus preparation and refinement stage to the integration and synthesis stage). We provide solutions to most of these challenges and point to possible outlook for further refinement. The evaluation of the initial prototype shows that the synthesis system will be useful to non-literate communities and a wide spectrum of applications