Department of Electrical and Electronic Engineering
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Electrical and Electronic Engineering is an exciting and dynamic field. Electrical engineers are responsible for the generation, transfer and conversion of electrical power, while electronic engineers are concerned with the transfer of information using radio waves, the design of electronic circuits, the design of computer systems and the development of control systems such as aircraft autopilots. These sought-after engineers can look forward to a rewarding and respected career.
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Browsing Department of Electrical and Electronic Engineering by Subject "Accelerometers"
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- ItemAnimal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis)(BioMed Central, 2017-11-21) Le Roux, Solomon Petrus; Marias, Jacques; Wolhuter, Riaan; Niesler, ThomasBackground: The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results: The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions: We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.
- ItemDevelopment and evaluation of an animal-borne sensor prototype with integrated kinetic energy harvesting for rhinoceros conservation(Stellenbosch : Stellenbosch University, 2023-03) Robinson, Simon; Niesler, Thomas; Wolhuter, Riaan; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: This project aimed to evaluate the potential of a previously developed kinetic energy harvester as a means to improve the battery life and prolong the deployment of an animal-borne sensor. It builds on animal-borne sensor hardware which was developed previously and forms a part of the ongoing RhinoNET research project. This project has already lead to the development of a GPS enabled animal-borne sensor with accelerometer-based on-board behaviour classification, and which can communicate over a LORA radio network. This project extends this work by extending the existing hardware and integrating a kinetic energy harvester and associated energy management circuitry into the sensor. Energy management software for this updated animal-borne sensor is included, in order to allow the minimisation of its energy usage. This updated sensor and software are tested by practical measurement of the energy output of the kinetic energy harvester and the energy consumption of the animal-borne sensor. Because physical measurements on rhinoceros was not possible, this evaluation was achieved first t hrough s imulation. A rhinoceros behaviour model was designed based on previously gathered behaviour data and used to simulate the operation of the entire animal-borne sensor when subjected to rhinoceros activity. This included the modelling of the energy management software of the sensor, the energy consumption of the sensor, and the energy generation of the kinetic energy harvester. Subsequently, the energy generation of the kinetic energy harvester was measured on a human volunteer. Through this combination of practical measurement and simulation, it was estimated that, when attached to a rhinoceros, the kinetic energy harvester would generate on average 14J of energy per day, which is enough to power the sensor and transmit a GPS location update four times. Alternatively, it can increase the battery life of the sensor by between 3 and 60 days, depending on the frequency of GPS updates. The simulation also showed that when compared to a naïve approach, the energy management software could reduce the energy usage of the animal-borne sensor from 1548.09J to 507.2J per day. It was observed that the energy output waveform of the energy harvester is not ideal for charging a lithium-polymer battery, as used in the prototype. Therefore, future work should include consideration of other energy storage technologies. If these could be found, the integration of the energy harvester would in principle allow indefinite operation of the animal-borne sensor.
- ItemDiscriminating coughs in a multi-bed ward environment(Stellenbosch : Stellenbosch University, 2021-12) Leng, Corwynne; Niesler, Thomas; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: Automatic cough detection algorithms play a key role in cough monitoring systems. These systems assess a patient’s recovery by monitoring the frequency and other characteristics of their coughs during treatment. An audio-based cough detection system evaluates features extracted from an audio signal and classifies these as either a cough or non-cough. However, these systems can struggle to distinguish between coughs of different individuals, such as a ward with more than one patient. This study designs and tests a cough detection algorithm that can reliably detect cough sounds in such an environment. We hypothesise that including the vibrations of the patient’s bed with the audio features will allow the classifier to differentiate between coughs originating from different beds in a hospital ward. Two datasets were compiled, containing simultaneously captured audio and accelerometer signals recorded by devices attached to the frame of each bed in a ward. These datasets were manually annotated with labels representing a cough from the bed in question, a cough from another source, and noise. The first dataset was used to train the classifier while the second was used to measure its performance. We extracted audio and accelerometer feature vectors using methods that have proved effective in automatic speech recognition systems, such as mel filter bank energies and mel-frequency cepstral coefficients. For the accelerometer signals, we included time-domain features. These feature vectors were presented to several classifiers, including convolutional neural networks and deep neural networks. Classifier training employed nested cross-validation to compensate for the small size of the training dataset and to allow for robust hyperparameter optimisation. The best classifier using audio and accelerometer features achieved an area under the receiver operating characteristic curve (AUC) score of 0.9842 while the best classifier using only audio features achieved an AUC score of 0.9334. Therefore, the inclusion of accelerometer features increased the AUC score by 0.0508 and improves the classifier’s ability to reject coughs from other sources by 10.72%. Additionally, the accelerometer features reduce the false detection of coughs from other sources and noise from 21.08% to 6.83%, while maintaining a sensitivity of 95%. We conclude that including the accelerometer signals of the patient’s bed with the audio features allows the classifier to better reject coughing sounds from sources other than the patient being monitored.
- ItemInformal public transport -- Safety measures(2014-07) Zeeman, A. S.; Booysen, Marthinus J.The informal transport industry in Sub-Saharan Africa is notoriously dangerous, leading to many fatalities annually. This paper presents an innovative way of monitoring driver behaviour, in real-time, by taking into account road design standards and vehicle dynamics. A theoretical model is presented that combines acceleration and speed data into an erratic driving detection algorithm. The model presents a novel use of commonly used civil engineering principles, used in road design. Evaluation of the models, using actual minibus taxi data, demonstrates that it successfully detect reckless driving. An online platform is presented to visualise the tracked vehicle and the driving behaviour.
- ItemPublic transport sector driver behaviour : measuring recklessness using speed and acceleration(Southern African Transport Conference, 2014-07) Zeeman, Adriaan Siebrits; Booysen, Marthinus J.The informal transport industry in Sub-Saharan Africa is notoriously dangerous, leading to many fatalities annually. This paper presents an innovative way of monitoring driver behaviour, in real-time, by taking into account road design standards and vehicle dynamics. A theoretical model is presented that combines acceleration and speed data into an erratic driving detection algorithm. The model presents a novel use of commonly used civil engineering principles, used in road design. Evaluation of the models, using actual minibus taxi data, demonstrates that it successfully detect reckless driving. An online platform is presented to visualise the tracked vehicle and the driving behaviour.
- ItemVehicle acceleration estimation using smartphone-based sensorsBruwer, F. J.; Booysen, Marthinus J.Recent advances in smartphone technology, including motion sensing and wireless communications, have resulted in these devices being used for vehicle-based driver behaviour sensing applications, replacing existing bespoke vehicle-based solutions. Acceleration is normally used as the primary indicator for recklessness. Despite the many benefits of using a smartphone to determine vehicle acceleration, the mobility of the phone relative to the vehicle, and the vehicle relative to the earth, causes the earth's gravitational force to obscure the true vehicle acceleration as perceived by the phone. The design and test results in this paper demonstrate how quaternions and an unscented Kalman filter can be used to remove the gravitational vector from the sensed acceleration, which enables reckless driving detection.