Browsing by Author "Usman, A. M."
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Item Design of a Bimodal Home Automation System using ESP8266 and ATMEGA328 Microcontroller(Department of Computer Engineering, Faculty of Computer Science, University of Sriwijaya, Indonesia, 2017-10-31) Zakariyya, Olayinka Sikiru; Salami, Abdulazeez Femi; Alabi, O. O.; Usman, A. M.Home automation systems are garnering increasing popularity and widespread use due to the relative ease of domestic management and comparatively high return on technology investment tied to its adoption. However, Nigeria and other emerging ICT economies are yet to fully actualize and maximize the inherent potential of these smart home technologies due to endemic challenges associated with poor infrastructure, erratic power supply and unreliable Internet connectivity. These challenges necessitate an innovative paradigmatic shift that could provide a pragmatic technological solution suitable to the context of Nigeria and other developing climes. For most smart home systems in this research context, the status quo is based on choosing whether the design would be for short- or long-range communication network. Short-range designs which are usually realized with Bluetooth technology suffer from limited range issues while poor connectivity, bandwidth and latency issues are some of the problems plaguing Wi-Fi-based long-range designs. Consequently, this research presents a hybrid adaptive architecture that combines desirable features of both short- and long-range modes. The proposed smart home system is based on using embedded systems which use mobile application to send messages to ESP8266 Wi-Fi module. Together with notifications received from the monitoring unit, these messages are parsed by Arduino's ATMEGA328 microcontroller from where instruction codes are sent for controlling the load by switching ON or OFF various relays connected to the load.Item Detection of Bryde’s Whale Short Pulse calls using Time Domain Features with Hidden Markov Models(the South African Institute of Electrical Engineers (SAIEE)., 2021-03) Babalola, O. P.; Usman, A. M.; Ogundile, O. O.; Versfeld, D. J. J.Passive acoustic monitoring (PAM) is generally used to 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 Markov models (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. The results 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.Item Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes’ vocalisations(Elsevier, 2021-04-28) Ogundile, O. O.; Usman, A. M.; Babalola, O. P.; Versfeld, D. J. J.The detection and classification of Mysticetes’ vocalisations have evoked the attention of researchers over the years because of their relevance to the marine ecosystem. These vocalisations are gathered employing different passive acoustic monitoring techniques. The vocalisation datasets are accumulated over a period; thus, they are large and cannot be easily analysed manually. Consequently, efficient machine learning (ML) tools such as the hidden Markov models (HMMs) are used extensively to automatically detect and classify these huge vocalisation datasets. As with most ML tools, the detection efficiency of the HMMs depend on the adopted feature extraction technique. Feature extraction techniques such as the Mel-scale frequency cepstral coefficient (MFCC), linear predictive coefficient (LPC), and empirical mode decomposition (EMD) have been adopted with the HMMs to detect different Mysticetes’ vocalisations. This article introduces the method of dynamic mode decomposition (DMD) as a feature extraction technique that can be adopted with the HMMs for the detection of Mysticetes’ vocalisations. The DMD has emerged as a robust tool for analysing the dynamics of non-stationary and non-linear signals. It is a completely data-driven tool that decomposes a signal over a certain period of time into relevant modes. Here, these spatial-temporal modes are reconstructed mathematically to form reliable feature vectors of the decomposed vocalisation signals. The performance of the proposed DMD-HMM detection technique is demonstrated using the acoustic datasets of two different Mysticetes’ vocalisations: Humpback whale songs and Inshore Bryde’s whale short pulse calls. In both species, the proposed DMD-HMM exhibits superior sensitivity and false discovery rate performances as compared to the MFCC-HMM, LPC-HMM, and EMD-HMM detection methods. Likewise, this proposed DMD-HMM detection method can be extended to other Mysticetes’ that produce characteristics sounds.Item An empirical mode decomposition based hidden Markov model approach for detection of Bryde’s whale pulse calls(The journal of the Acoustical Society of America, 2020-02-11) Ogundile, O. O.; Usman, A. M.; Versfeld, D. J. JThis letter proposes an empirical mode decomposition (EMD) based hidden Markov model (HMM) approach for the detection of mysticetes’ pulse calls such as the Bryde’s whales. The HMM detection capabilities depend on the deployed feature extraction (FE) technique. The EMD is proposed as a performance efficient alternative to the popular Mel-scale frequency cepstral coefficient (MFCC) and linear predictive coefficient (LPC) FE techniques. The amplitude modulation–frequency modulation components derived from the EMD process are modified to form feature vectors for the HMM. Also, the ensemble EMD (EEMD) is adapted in a similar way as the EMD. These proposed EMD-HMM and EEMD-HMM approaches achieved better performance in comparison to the MFCC-HMM and LPC-HMMapproaches.Item A hidden Markov model with selective time domain feature extraction to detect inshore Bryde's whale short pulse calls(Elsevier, 2020-04-02) Ogundile, O. O.; Usman, A. M.; Versfeld, D. J. JAn Increase in the study of cetaceans' sounds has motivated the development of different automated sound detection and classification techniques. Passive acoustic monitoring (PAM) is extensively used to study these cetaceans' sounds over a period to understand their daily activities within their ecosystem. Using PAM, the gathered sound datasets are usually large and impractical to manually analyse and detect. Thus, hidden Markov models (HMM) is one of the popular tools used to automatically detect and classify these cetaceans' sounds. Nonetheless, HMM rely heavily on the employed feature extraction method such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). In most cases, the more reliable the extracted feature vector from the known sound label, the higher the sensitivity of the HMM. Although these aforementioned feature extraction methods are widely used, their design is based on filters and requires windowing, fast Fourier transforms (FFT), and logarithm operations. Consequently, this increases the computational time complexity of the HMM. Here, we describe a selective time domain feature extraction method that can be easily adapted with the HMM. This proposed feature extraction method uses a combination of some simple but robust parameters such as the mean, relative amplitude and relative power/energy (MAP), which are selected based on empirical observations of the call to be detected. The performance of this proposed MAP-HMM was verified using the acoustic dataset of continuous recordings of an inshore Bryde's whale (Balaenoptera) short pulse calls collected in a single site in False bay, South-West of South Africa. Aside from exhibiting a low computational complexity, the proposed MAP-HMM offers superior sensitivity and false discovery rate performances in comparison to the LPCHMM and MFCC-HMM.Item A Review of Smart Grids Deployment Issues in Developing Countries.(Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria., 2017-12) Otuoze, A. O; Usman, A. M.; Mohammed, O. O; Jimoh, A. ASmart Grids (SGs) have taken a centre stage in achieving a smarter, more reliable, robust, secured, economically efficient and more environmentally friendly mode of power generation and utilisation. Massive deployment is being recorded in developed worlds. While most of these countries are investing heavily in the development of SGs, well-articulated areas of research and development are key aspects with special emphasis on its security since it involves complex interconnection of units and systems which are expensive to install and maintain. In developing nations, especially those of Africa, realisation of adequate power supply to meeting the ever-growing demand has been a mirage with demand on geometric increase and with every increase largely meaning a drift away from the supply. Hence, attention is focused on capacity expansion in most developing nations rather than SGs deployments especially considering the various challenges militating against the development despite the huge advantages. Although, some of these nations have made tremendous achievements in this regard, the associated challenges have become major source of worry for most of the nations. This paper gives highlights of these issues and possible measures of overcoming them in order to enhance sustainable SGs deployments in developing countries like Nigeria.Item A Review of Smart Grids Deployment Issues in Developing Countries.(Published by Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria., 2017) Otuoze, A. O.; Usman, A. M.; Mohammed, O.O.; Jimoh, A. A.