The transient and dynamic performance analysis of a proposed line-start, three-phase concentrated dual-winding synchronous reluctance motor (cDWSynRM) in comparison with the conventional concentrated winding synchronous reluctance motor (cSynRM) was presented. Both windings are overlapping windings. The cDWSynRM consist of main and auxiliary windings with capacitive assistance for power factor improvement. The modelling of the synchronous reluctance motors (SynRM) was done in direct-phase variables considering only the fundamental magneto-motive force (MMF). The machine inductances of both machine models were determined using winding function theory (WFT). These derived inductances were used to determine machine performance characteristics such as Torque, Speed, Phase currents etc. The performance characteristics of both motors were monitored using MATLAB/Simulink, and the proposed line-start cDWSynRM with capacitive assistance was observed to have improved performance characteristics when compared to the cSynRM.

An algorithm for detection of crude oil spills in visible light images has been developed and tested on 50 documented crude oil spill images from Shell Petroleum Development Company (SPDC) Nigeria. A set of three 25 x 25 pixels crude oil filters, with unique red, green, and blue (RGB) colour values, homogeneity, and power spectrum density (PSD) features were cross-correlated with the documented spill images. The final crude oil spill Region of Interest (ROI) was determined by grouping interconnected pixels based on their proximity, and only selecting ROIs with an area greater than 5,000 pixels. The crude oil filter cross correlation algorithm demonstrated a sensitivity of 84% with a False Positive per Image (FPI) of 0.82. Future work includes volume estimation of detected spills using crude oil filters, and utilizing this information in the recommendation of appropriate spill clean-up and remediation procedures for the detected spills. Keywords: Crude Oil Spill Detection, Crude oil image filters, Cross correlation, Visible sensor imaging, Oil Spill Segmentation.

Every tertiary academic institution has the obligation of recording and taking the attendance of its students during lecture periods. Over the years the process and accuracy of this attendance has been marred by many challenges which range from the cumbersome nature of paper sheets used in recording, to the manipulation of attendance by unscrupulous students and so on. This paper is aimed at designing and implementing a biometric students’ time and attendance logging system that helps in mitigating these challenges. This biometric student time and attendance logging system uses the fingerprint biometric characteristics to accurately identify the student. The system also generates a report showing the number of times a particular student was present or absent in a semester. This feat was achieved through the use of an atmega328P microcontroller, a fingerprint RS305 module, a liquid crystal display (LCD), a Red Green Blue (RGB) light emitting diode (LED) and a Personal Computer (PC) amongst others. The software used in this work was developed using visual studio C# and MySQL2008, and the final work was simulated using proteus before it was constructed and tested. The results of the test show a significant improvement over the manual attendance system.

Adulteration of petrol is difficult to detect at point of sale terminals (POS) because current detection methods require chemical laboratory experiments to measure parameters such as density, API gravity and evaporation point, are extremely bulky, time-consuming, and require experienced technicians to operate. This paper explores a new technique for adulteration detection at POS terminals, known as Gaseous Vapor Emission (GVE). GVE was performed on 1 L of pure petrol obtained from a Nigerian National Petroleum Corporation (NNPC) Retail Outlet, using a portable Petroleum Product Volume Estimator and Tracker (PPVET). Results showed that pure petrol gave a peak methane emission in 30 seconds, and a peak butane emission in 60 seconds. In an enclosed space of 19,000 cm 3 , a sample of pure petrol emits 4,466,841-5,308,924 ppm of methane, 12.23-19.09 ppm of LPG, and 216,667-383,408 ppm of butane. GVE correctly identified the petrol sample as being pure, by verifying the presence of the characteristic methane and butane emission peaks, and the technique can be used at POS terminals to test for petrol adulteration. Future work includes the expansion of the GVE chemical signature for petrol to include other gases in addition to methane, LPG, and butane, the ability to utilize GVE in detecting adulterated petrol, and when present, estimation of the level of adulteration, as well as accurate identification of the adulterant used. Contribution/Originality: This paper proposes and explores a new technique for detecting petrol adulteration at Point of Sale (POS) terminals.

In the Oil and Gas Industry, price disparity between Premium Motor Spirit (PMS), Automotive Gas Oil (AGO), and Dual Purpose Kerosene (DPK), often leads to adulteration of these petroleum products by marketers for monetary gains. Adulteration is the illegal introduction of a foreign undesirable substance to a substrate which affects the quality of the substrate. Adulteration of petroleum products are difficult to detect at Point of Sale (POS) terminals. Current methods for adulteration detection are time-consuming, require specialized equipment and experienced technicians to operate them, and cannot be used at POS terminals. Gaseous Vapor Technique (GVE) is an innovative adulteration detection technique that can be employed at POS terminals and the PePVEAT device utilized in this study is the first portable electronic device that performs GVE on petroleum products. GVE testing was performed on pure 1 L samples of PMS, AGO, and DPK obtained from the Nigerian National Petroleum Corporation (NNPC) using PePVEAT. The results obtained from GVE analysis of AGO, PMS, and DPK showed that the three petroleum products exhibited unique and varying chemical characteristics during GVE. AGO gives off its peak emissions between 10-20 seconds from test onset, DPK gives off its peak emissions between 10-30 seconds from test onset, and PMS gives off its peak emissions between 50-70 seconds from test onset. AGO emits 17.52-46.58 ppm of methane, 5.35-11.93 ppm of LPG, 35.51-84.6 ppm of butane, and 10.38-69.86 ppm of toluene. PMS emits 92,063.67-152,168.18 ppm of methane, 301.035-573.61 ppm of LPG, 2210.89-3424.94 ppm of butane, and 1983.02-7187.29 ppm of toluene. DPK emits 27.13-62.14 ppm of methane, 20.2-74.1 ppm of LPG, 120.41-1635.85 ppm of butane, and 1159.75- 1633.09 ppm of toluene. These variations in timing and concentrations of emissions shows that GVE can be utilized to detect and distinguish between AGO, PMS and DPK. The results obtained from GVE analysis of AGO, PMS, and DPK showed that Since PMS, AGO and DPK, each have unique chemical emissions during GVE, as was demonstrated in this paper, it is possible that GVE can be utilized to detect the adulterations of PMS with AGO and the adulteration of AGO with DPK. Future work involves investigating the ability of GVE to detect AGO-adulterated PMS, DPK-adulterated AGO, DPK-adulterated PMS, AGO-adulterated DPK,and PMS-adulterated DPK. The degree and percentage of adulteration that can be detected using the GVE technique will also be examined.