Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432896
Erick O. Arwa;Komla A. Folly
This paper proposes an improved Q-learning method to obtain near-optimal schedules for grid and battery power in a grid-connected electric vehicle charging station for a 24-hour horizon. The charging station is supplied by a solar PV generator with a backup from the utility grid. The grid tariff model is dynamic in line with the smart grid paradigm. First, the mathematical formulation of the problem is developed highlighting each of the cost components considered including battery degradation cost and the real-time tariff for grid power purchase cost. The problem is then formulated as a Markov Decision Process (MDP), i.e., defining each of the parts of a reinforcement learning environment for the charging station’s operation. The MDP is solved using the improved Q-learning algorithm proposed in this paper and the results are compared with the conventional Q-learning method. Specifically, the paper proposes to modify the action-space of a Q-learning algorithm so that each state has just the list of actions that meet a power balance constraint. The Q-table updates are done asynchronously, i.e., the agent does not sweep through the entire state-space in each episode. Simulation results show that the improved Q-learning algorithm returns a 14% lower global cost and achieves higher total rewards than the conventional Q-learning method. Furthermore, it is shown that the improved Q-learning method is more stable in terms of the sensitivity to the learning rate than the conventional Q-learning.
{"title":"Improved Q-learning for Energy Management in a Grid-tied PV Microgrid","authors":"Erick O. Arwa;Komla A. Folly","doi":"10.23919/SAIEE.2021.9432896","DOIUrl":"10.23919/SAIEE.2021.9432896","url":null,"abstract":"This paper proposes an improved Q-learning method to obtain near-optimal schedules for grid and battery power in a grid-connected electric vehicle charging station for a 24-hour horizon. The charging station is supplied by a solar PV generator with a backup from the utility grid. The grid tariff model is dynamic in line with the smart grid paradigm. First, the mathematical formulation of the problem is developed highlighting each of the cost components considered including battery degradation cost and the real-time tariff for grid power purchase cost. The problem is then formulated as a Markov Decision Process (MDP), i.e., defining each of the parts of a reinforcement learning environment for the charging station’s operation. The MDP is solved using the improved Q-learning algorithm proposed in this paper and the results are compared with the conventional Q-learning method. Specifically, the paper proposes to modify the action-space of a Q-learning algorithm so that each state has just the list of actions that meet a power balance constraint. The Q-table updates are done asynchronously, i.e., the agent does not sweep through the entire state-space in each episode. Simulation results show that the improved Q-learning algorithm returns a 14% lower global cost and achieves higher total rewards than the conventional Q-learning method. Furthermore, it is shown that the improved Q-learning method is more stable in terms of the sensitivity to the learning rate than the conventional Q-learning.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45814570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432891
{"title":"Copyright","authors":"","doi":"10.23919/SAIEE.2021.9432891","DOIUrl":"10.23919/SAIEE.2021.9432891","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9432888/09432891.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46496290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432893
Yuval Genga;Olutayo O. Oyerinde;Jaco Versfeld
In this paper, a bit-level decoder is presented for soft-input soft-output iterative decoding of Reed-Solomon (RS) codes. The main aim for the development of the proposed algorithm is to reduce the complexity of the decoding process, while yielding a relatively good error correction performance, for the efficient use of RS codes. The decoder utilises information set decoding techniques to reduce the computational complexity cost by lowering the iterative convergence rate during the decoding process. As opposed to most iterative bit-level soft-decision decoders for RS codes, the proposed algorithm is also able to avoid the use of belief propagation in the iterative decoding of the soft bit information, which also contributes to the reduction in the computational complexity cost of the decoding process. The performance of the proposed decoder is investigated when applied to short RS codes. The error correction simulations show the proposed algorithm is able to yield a similar performance to that of the Adaptive Belief Propagation (ABP) algorithm, while being a less complex decoder.
{"title":"Iterative Soft-Input Soft-Output Bit-Level Reed-Solomon Decoder Based on Information Set Decoding","authors":"Yuval Genga;Olutayo O. Oyerinde;Jaco Versfeld","doi":"10.23919/SAIEE.2021.9432893","DOIUrl":"10.23919/SAIEE.2021.9432893","url":null,"abstract":"In this paper, a bit-level decoder is presented for soft-input soft-output iterative decoding of Reed-Solomon (RS) codes. The main aim for the development of the proposed algorithm is to reduce the complexity of the decoding process, while yielding a relatively good error correction performance, for the efficient use of RS codes. The decoder utilises information set decoding techniques to reduce the computational complexity cost by lowering the iterative convergence rate during the decoding process. As opposed to most iterative bit-level soft-decision decoders for RS codes, the proposed algorithm is also able to avoid the use of belief propagation in the iterative decoding of the soft bit information, which also contributes to the reduction in the computational complexity cost of the decoding process. The performance of the proposed decoder is investigated when applied to short RS codes. The error correction simulations show the proposed algorithm is able to yield a similar performance to that of the Adaptive Belief Propagation (ABP) algorithm, while being a less complex decoder.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43688159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432897
Aviwe Kohlakala;Johannes Coetzer
In this paper novel semi-automated and fully automated ear-based biometric authentication systems are proposed. The region of interest (ROI) is manually specified and automatically detected within the context of the semi-automated and fully automated systems, respectively. The automatic detection of the ROI is facilitated by a convolutional neural network (CNN) and morphological postprocessing. The CNN classifies sub-images of the ear in question as either foreground (part of the ear shell) or background (homogeneous skin, hair or jewellery). Prominent contours associated with the folds of the ear shell are detected within the ROI. The discrete Radon transform (DRT) is subsequently applied to the resulting binary contour image for the purpose of feature extraction. Feature matching is achieved by implementing an Euclidean distance measure. A ranking verifier is constructed for the purpose of authentication. In this study experiments are conducted on two independent ear databases, that is (1) the Mathematical Analysis of Images (AMI) ear database and (2) the Indian Institute of Technology (IIT) Delhi ear database. The results are encouraging. Within the context of the proposed semi-automated system, accuracies of 99.20% and 96.06% are reported for the AMI and IIT Delhi ear databases respectively.
{"title":"Ear-based biometric authentication through the detection of prominent contours","authors":"Aviwe Kohlakala;Johannes Coetzer","doi":"10.23919/SAIEE.2021.9432897","DOIUrl":"10.23919/SAIEE.2021.9432897","url":null,"abstract":"In this paper novel semi-automated and fully automated ear-based biometric authentication systems are proposed. The region of interest (ROI) is manually specified and automatically detected within the context of the semi-automated and fully automated systems, respectively. The automatic detection of the ROI is facilitated by a convolutional neural network (CNN) and morphological postprocessing. The CNN classifies sub-images of the ear in question as either foreground (part of the ear shell) or background (homogeneous skin, hair or jewellery). Prominent contours associated with the folds of the ear shell are detected within the ROI. The discrete Radon transform (DRT) is subsequently applied to the resulting binary contour image for the purpose of feature extraction. Feature matching is achieved by implementing an Euclidean distance measure. A ranking verifier is constructed for the purpose of authentication. In this study experiments are conducted on two independent ear databases, that is (1) the Mathematical Analysis of Images (AMI) ear database and (2) the Indian Institute of Technology (IIT) Delhi ear database. The results are encouraging. Within the context of the proposed semi-automated system, accuracies of 99.20% and 96.06% are reported for the AMI and IIT Delhi ear databases respectively.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44417116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432895
S. Perumal;A.L.L. Jarvis;M.Z. Gaffoor
In this research supercapacitors were fabricated using graphene oxide (GO) as the electrode material. GO was synthesized using natural graphite precursor with varying flake sizes. GO was characterized by High-Resolution Transmission Electron Microscopy (HRTEM), Elemental Analysis, Fourier Transform Infrared (FTIR) spectroscopy and Raman spectroscopy. Cyclic voltammetry was carried out at different scan rates to determine the specific capacitance and energy density of the electrode material. An increase in specific capacitance was seen with an increase in graphite precursor flake size. A specific capacitance and energy density of 204.22 F.g −1 and 102.11 kJ.kg −1 respectively at scan rate 10 mV.s −1 was obtained for the GO sample synthesized from graphite precursor with an average particle size of 0.45 mm. This sample also had the highest specific capacitance for all scan rates.
{"title":"Effect of Graphite Precursor Flake Size on Energy Storage Capabilities of Graphene Oxide Supercapacitors","authors":"S. Perumal;A.L.L. Jarvis;M.Z. Gaffoor","doi":"10.23919/SAIEE.2021.9432895","DOIUrl":"https://doi.org/10.23919/SAIEE.2021.9432895","url":null,"abstract":"In this research supercapacitors were fabricated using graphene oxide (GO) as the electrode material. GO was synthesized using natural graphite precursor with varying flake sizes. GO was characterized by High-Resolution Transmission Electron Microscopy (HRTEM), Elemental Analysis, Fourier Transform Infrared (FTIR) spectroscopy and Raman spectroscopy. Cyclic voltammetry was carried out at different scan rates to determine the specific capacitance and energy density of the electrode material. An increase in specific capacitance was seen with an increase in graphite precursor flake size. A specific capacitance and energy density of 204.22 F.g\u0000<sup>−1</sup>\u0000 and 102.11 kJ.kg\u0000<sup>−1</sup>\u0000 respectively at scan rate 10 mV.s\u0000<sup>−1</sup>\u0000 was obtained for the GO sample synthesized from graphite precursor with an average particle size of 0.45 mm. This sample also had the highest specific capacitance for all scan rates.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67797001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432890
{"title":"Editors and Reviewers","authors":"","doi":"10.23919/SAIEE.2021.9432890","DOIUrl":"10.23919/SAIEE.2021.9432890","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44265159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432894
{"title":"Guest Editorial: SAUPEC/RobMech/PRASA 2020","authors":"","doi":"10.23919/SAIEE.2021.9432894","DOIUrl":"10.23919/SAIEE.2021.9432894","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44865030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.23919/SAIEE.2021.9432898
S. Josias;W. Brink
An image can be described by the objects within it, and interactions between those objects. A pair of object labels together with an interaction label is known as a visual relationship, and is represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in images is a challenging task, owing to the combinatorially large number of possible relationship triplets, which leads to an extreme multiclass classification problem. In addition, the distribution of visual relationships in a dataset tends to be long-tailed, i.e. most triplets occur rarely compared to a small number of dominating triplets. Three strategies to address these issues are investigated. Firstly, instead of predicting the full triplet, models can be trained to predict each of the three elements separately. Secondly a multitask learning strategy is investigated, where shared network parameters are used to perform the three separate predictions. Thirdly, a class-selective mini-batch construction strategy is used to expose the network to more of the rare classes during training. Experiments demonstrate that class-selective mini-batch construction can improve performance on classes in the long tail of the data distribution, possibly at the expense of accuracy on the small number of dominating classes. It is also found that a multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial. In an effort to better understand the behaviour of the various models, a novel evaluation approach for visual relationship recognition is introduced. We conclude that the use of semantics can be helpful in the modelling and evaluation process.
{"title":"Class-Selective Mini-Batching and Multitask Learning for Visual Relationship Recognition","authors":"S. Josias;W. Brink","doi":"10.23919/SAIEE.2021.9432898","DOIUrl":"10.23919/SAIEE.2021.9432898","url":null,"abstract":"An image can be described by the objects within it, and interactions between those objects. A pair of object labels together with an interaction label is known as a visual relationship, and is represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in images is a challenging task, owing to the combinatorially large number of possible relationship triplets, which leads to an extreme multiclass classification problem. In addition, the distribution of visual relationships in a dataset tends to be long-tailed, i.e. most triplets occur rarely compared to a small number of dominating triplets. Three strategies to address these issues are investigated. Firstly, instead of predicting the full triplet, models can be trained to predict each of the three elements separately. Secondly a multitask learning strategy is investigated, where shared network parameters are used to perform the three separate predictions. Thirdly, a class-selective mini-batch construction strategy is used to expose the network to more of the rare classes during training. Experiments demonstrate that class-selective mini-batch construction can improve performance on classes in the long tail of the data distribution, possibly at the expense of accuracy on the small number of dominating classes. It is also found that a multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial. In an effort to better understand the behaviour of the various models, a novel evaluation approach for visual relationship recognition is introduced. We conclude that the use of semantics can be helpful in the modelling and evaluation process.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47908457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-29DOI: 10.23919/SAIEE.2021.9340532
J. Tlouyamma;M. Velempini
The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencinganincreasingusage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission(FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and makea final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlabwas used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalizedpredictive channel selection algorithm (EXGPCSA) outperformed otherschemes in literature in terms of throughput, service timeandprobability of detection.
{"title":"Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks","authors":"J. Tlouyamma;M. Velempini","doi":"10.23919/SAIEE.2021.9340532","DOIUrl":"https://doi.org/10.23919/SAIEE.2021.9340532","url":null,"abstract":"The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencinganincreasingusage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission(FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and makea final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlabwas used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalizedpredictive channel selection algorithm (EXGPCSA) outperformed otherschemes in literature in terms of throughput, service timeandprobability of detection.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9340532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-29DOI: 10.23919/SAIEE.2021.9340533
Oluwaseyi P. Babalola;Ayinde M. Usman;Olayinka O. Ogundile;Daniel J. J. Versfeld
Passive acoustic monitoring (PAM) is generally usedto extract acoustic signals produced by cetaceans. However, the large data volume from the PAM process is better analyzed using an automated technique such as the hidden Markovmodels (HMM). In this paper, the HMM is used as a detection and classification technique due to its robustness and low time complexity. Nonetheless, certain parameters, such as the choice of features to be extracted from the signal, the frame duration, and the number of states affect the performance of the model. Theresults show that HMM exhibits best performances as the number of states increases with short frame duration. However, increasing the number of states creates more computational complexity in the model. The inshore Bryde's whales produce short pulse calls with distinct signal features, which are observable in the time-domain. Hence, a time-domain feature vector is utilized to reduce the complexity of the HMM. Simulation results also show that average power as a time-domain feature vector provides the best performance compared to other feature vectors for detecting the short pulse call of inshore Bryde's whales based on the HMM technique. More so, the extracted features such as the average power, mean, and zero-crossing rate, are combined to form a single 3-dimensional vector (PaMZ). The PaMZ-HMM shows improved performance and reduced complexity over existing feature extraction techniques such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). Thus, making the PaMZ-HMM suitable for real-time detection.
{"title":"Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models","authors":"Oluwaseyi P. Babalola;Ayinde M. Usman;Olayinka O. Ogundile;Daniel J. J. Versfeld","doi":"10.23919/SAIEE.2021.9340533","DOIUrl":"https://doi.org/10.23919/SAIEE.2021.9340533","url":null,"abstract":"Passive acoustic monitoring (PAM) is generally usedto extract acoustic signals produced by cetaceans. However, the large data volume from the PAM process is better analyzed using an automated technique such as the hidden Markovmodels (HMM). In this paper, the HMM is used as a detection and classification technique due to its robustness and low time complexity. Nonetheless, certain parameters, such as the choice of features to be extracted from the signal, the frame duration, and the number of states affect the performance of the model. Theresults show that HMM exhibits best performances as the number of states increases with short frame duration. However, increasing the number of states creates more computational complexity in the model. The inshore Bryde's whales produce short pulse calls with distinct signal features, which are observable in the time-domain. Hence, a time-domain feature vector is utilized to reduce the complexity of the HMM. Simulation results also show that average power as a time-domain feature vector provides the best performance compared to other feature vectors for detecting the short pulse call of inshore Bryde's whales based on the HMM technique. More so, the extracted features such as the average power, mean, and zero-crossing rate, are combined to form a single 3-dimensional vector (PaMZ). The PaMZ-HMM shows improved performance and reduced complexity over existing feature extraction techniques such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). Thus, making the PaMZ-HMM suitable for real-time detection.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9340533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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