Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

This 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-HMM approaches.

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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.

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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.

In homes and offices, it is very common for occupants to forget to switch OFF the lighting and fans when leaving the premises. This can be attributed to human forgetfulness and the epileptic power supply which causes interruption that results in users forgetting the state of their appliances (whether they are ON or OFF). Consequently, these appliances would continue to work whenever power is restored when the occupants might have vacated the premise. This action is not a small contributor to energy wastage in a country like Nigeria where there is an inadequate energy supply to go round the populace. In this work, a simple but robust automatic home and office power control system is developed to auto-detect the presence of an occupant in the room through the passive infrared (PIR) sensor and control the electrical appliances (lighting and fan source) in the room. Certain conditions must be met for the operation of lighting and the fan source. The lighting comes up when the PIR sensor senses the presence of an occupant and the room is in darkness, while the fan would work when there is an occupant and the temperature in the room is above 35°C. These conditions are programmed to suit the need of the occupant but cannot be changed by the user. The device automatically switches off within five minutes after the last occupant leaves the room. Keywords— Microcontroller, Passive infrared (PIR) sensor, Solid State Relay (SSR), and Switching

Replacing failed bulbs of streetlights in a location can be very tasking and expensive if the optimal time for replacement is not determined. In this paper, a model has been developed that helps to establish the optimal time for the replacement of streetlight bulbs. Burnt-out bulbs are replaced individually when they fail, and group replacement is carried out on all bulbs after a specified time. The costs for both individual replacement and group replacement are determined. The developed model was applied to locally sourced data from a field survey of a streetlight installation at the University of Ilorin, Ilorin, North-central Nigeria. The model gave the optimum replacement time of burnt-out bulbs as the eighteenth week when applied to the data used in this work. The optimum replacement time will be dependent on the dataset used. This makes the developed model useful in establishing the optimal replacement time of any stochastically failing items that are in large quantities. The model will help to reduce maintenance costs for facility managers.