Faculty of Engineering, Technology, Applied Design & FineArt (FETADFA)

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    Clean Eco-Friendly Cooking Energy as Sustainable Approach and Mitigation to Climate Change: A Case Study of Ankole, Western Uganda.
    (Kabale University, 2023) Kayamba, William Kariiti
    The study investigates how communities in the Ankole region, western Uganda are coping with a shortage of cooking fuels, climate change and what strategies they have set up to counteract its effects using innovative, sustainable, renewable and affordable technological methods. The objectives of the study are: 1) to investigate the type of cookstoves used in cooking that is being used in the area under study. 2) To suggest eco-friendly cook stoves that can be used for cooking to save the environment and reduce health hazards that are related to inhalation of smoke. It was carried out in the districts of Mbarara and Bushenyi which are diverse in their setting. The main objective was to investigate how traditional cooking fuels have become a health hazard to many mothers and children in Ankole, human activities and rural-urban migration, have caused hiking of prices for fuel for cooking; wetland drainage, bush clearing for farming, charcoal burning, brick making associated with social and economic developments have affected the environment. Traditional methods of cooking still dominate in Ankole, where three stones are still used. Charcoal has become a major fuel for cooking in many homes as an alternative to firewood, in addition to briquettes, biogas, LPG and volcanic rocks. A sustainable eco-friendly stove is proposed to mitigate environmental degradation.
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    Concrete Production and Curing with Recycled Wastewater: A Review on the Current State of Knowledge and Practice
    (Hindawi, 2022-12-10) Tobby Michael, Agwe; Tibenderana, Philip; Twesigye-Omwe, Moses N; Abdulkadir, Sholagberu Taofeeq
    properly cited. A number of factors have combined to put excessive pressure on the finite available freshwater resources. These include increasing population, rapid urbanization, industrialization, changed land pattern usage and land cover, change in the overall ecological system, and increased temperature and unscientific compromises in the extraction of water are at alarming threshold putting pressure on the finite available freshwater resources. As a result, many countries have been stressed or are at the verge of being stressed. The problem is worsened day by day by prolonged drought, unchecked discharge of untreated or partially treated wastewater to the freshwater reservoirs and lack of proper water quality control measures and management. Many initiatives such as Zero Liquid Discharge of industrial wastewater into freshwater bodies such as reservoirs, lakes, and ponds, and the use of recycled wastewater for irrigation and domestic purposes have started to be embraced as measures to put a check on the fast-depleting freshwater resources for sustainable socio-economic development. The construction industry is the second largest consumer of freshwater just after agriculture. Concreting alone consumes, annually, over one trillion m3 of freshwater globally while the concept of the use of wastewater and/or recycled water in the concrete-making processes is yet to be adopted. Hence, this paper presents a general review of the current state of knowledge and practice on concrete production and curing using recycled wastewater from industrial, commercial, and domestic activities. An extensive review of the existing literature revealed that recycled water is ft for concrete production and curing purposes. The observations made are based on the assessment of wastewater quality parameters and their impacts on some selected concrete properties such as initial setting time and compressive strength. Due to scanty research on the impacts of varying concentrations of different ingredients in any questionable water on selected properties of reinforced concrete and its durability, thus, further research is recommended.
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    Concrete Production and Curing with Recycled Wastewater: A Review on the Current State of Knowledge and Practice
    (Hindawi, 2022) Tobby Michael, Agwe; Philip, Tibenderana; Moses N, Twesigye-Omwe; Joel Webster, Mbujje; Abdulkadir, Sholagberu Taofeeq
    A number of factors have combined to put excessive pressure on the finite available freshwater resources. These include increasing population, rapid urbanization, industrialization, changed land pattern usage and land cover, change in the overall ecological system, and increased temperature and unscientific compromises in the extraction of water are at alarming threshold putting pressure on the finite available freshwater resources. As a result, many countries have been stressed or are at the verge of being stressed. The problem is worsened day by day by prolonged drought, unchecked discharge of untreated or partially treated wastewater to the freshwater reservoirs and lack of proper water quality control measures and management. Many initiatives such as Zero Liquid Discharge of industrial wastewater into freshwater bodies such as reservoirs, lakes, and ponds, and the use of recycled wastewater for irrigation and domestic purposes have started to be embraced as measures to put a check on the fast depleting freshwater resources for sustainable socio-economic development. The construction industry is the second largest consumer of freshwater just after agriculture. Concreting alone consumes, annually, over one trillion m3 of freshwater globally while the concept of the use of wastewater and/or recycled water in the concrete-making processes is yet to be adopted. Hence, this paper presents a general review of the current state of knowledge and practice on concrete production and curing using recycled wastewater from industrial, commercial, and domestic activities. An extensive review of the existing literature revealed that recycled water is fit for concrete production and curing purposes. The observations made are based on the assessment of wastewater quality parameters and their impacts on some selected concrete properties such as initial setting time and compressive strength. Due to scanty research on the impacts of varying concentrations of different ingredients in any questionable water on selected properties of reinforced concrete and its durability, thus, further research is recommended.
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    Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features
    (IEEE, 2022) Samuel, Kakuba; Alwin, Poulose; Dong, Seog Han; Senior Member, Ieee
    The detection and classification of emotional states in speech involves the analysis of audio signals and text transcriptions. There are complex relationships between the extracted features at different time intervals which ought to be analyzed to infer the emotions in speech. These relationships can be represented as spatial, temporal and semantic tendency features. In addition to emotional features that exist in each modality, the text modality consists of semantic and grammatical tendencies in the uttered sentences. Spatial and temporal features have been extracted sequentially in deep learning-based models using convolutional neural networks (CNN) followed by recurrent neural networks (RNN) which may not only be weak at the detection of the separate spatial-temporal feature representations but also the semantic tendencies in speech. In this paper, we propose a deep learning-based model named concurrent spatial-temporal and grammatical (CoSTGA) model that concurrently learns spatial, temporal and semantic representations in the local feature learning block (LFLB) which are fused as a latent vector to form an input to the global feature learning block (GFLB). We also investigate the performance of multi-level feature fusion compared to single-level fusion using the multi-level transformer encoder model (MLTED) that we also propose in this paper. The proposed CoSTGA model uses multi-level fusion first at the LFLB level where similar features (spatial or temporal) are separately extracted from a modality and secondly at the GFLB level where the spatial-temporal features are fused with the semantic tendency features. The proposed CoSTGA model uses a combination of dilated causal convolutions (DCC), bidirectional long short-term memory (BiLSTM), transformer encoders (TE), multi-head and self-attention mechanisms. Acoustic and lexical features were extracted from the interactive emotional dyadic motion capture (IEMOCAP) dataset. The proposed model achieves 75.50% and 75.82% of weighted and unweighted accuracy, 75.32% and 75.57% of recall and F1 score respectively. These results imply that concurrently learned spatial-temporal features with semantic tendencies learned in a multi-level approach improve the model’s effectiveness and robustness.
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    Attention-Based Multi-Learning Approach for Speech Emotion Recognition With Dilated Convolution
    (IEEE, 2022-11-21) Samuel, Kakuba; Alwin, Poulose
    The success of deep learning in speech emotion recognition has led to its application in resource-constrained devices. It has been applied in human-to-machine interaction applications like social living assistance, authentication, health monitoring and alertness systems. In order to ensure a good user experience, robust, accurate and computationally efficient deep learning models are necessary. Recurrent neural networks (RNN) like long short-term memory (LSTM), gated recurrent units (GRU) and their variants that operate sequentially are often used to learn time series sequences of the signal, analyze long-term dependencies and the contexts of the utterances in the speech signal. However, due to their sequential operation, they encounter problems in convergence and sluggish training that uses a lot of memory resources and encounters the vanishing gradient problem. In addition, they do not consider spatial cues that may exist in the speech signal. Therefore, we propose an attention-based multi-learning model (ABMD) that uses residual dilated causal convolution (RDCC) blocks and dilated convolution (DC) layers with multi-head attention. The proposed ABMD model achieves comparable performance while taking global contextualized long-term dependencies between features in a parallel manner using a large receptive field with less increase in the number of parameters compared to the number of layers and considers spatial cues among the speech features. Spectral and voice quality features extracted from the raw speech signals are used as inputs. The proposed ABMD model obtained a recognition accuracy and F1 score of 93.75% and 92.50% on the SAVEE datasets, 85.89% and 85.34% on the RAVDESS datasets and 95.93% and 95.83% on the EMODB datasets. The model’s robustness in terms of the confusion ratio of the individual discrete emotions especially happiness which is often confused with emotions that belong to the same dimensional plane with it also improved when validated on the same datasets