Real-Time Detection of Abandoned Object using Centroid Difference Method

An abandoned object is one that remains stationary for an extended period. Such object might contain explosives and if left on purpose could cause death and injuries to people especially in crowded places. Abandoned objects need to be detected on time to prevent what might endanger people’s lives and health. Various methods have been developed to detect abandoned objects. The most reliable one is the vision-based method which automatically detects the abandoned object using image processing. The efficiency of the method was tested and evaluated on the customized datasets as well as the i-Lids advanced video surveillance system database. The Self -organizing Background Subtraction (SOBS) method overrides other methods in terms of its detection accuracy and simplicity of implementation, but fails for dynamic background scenarios. This work presents a real time vision-based object detection method using the centroid difference to improve on the accuracy of the detection and to tackle challenges of dynamic background of the SOBS method. Matlab Image processing toolbox was used to achieve this goal. The strategy is basically decomposed into two; foreground detection and stationary foreground object (SFO) detection. Gaussian Mixture Model is used for detecting the presence of newly introduced object into a scene (foreground detection), while the blob tracking approach based on frame counting is used to determine whether the detected foreground object is static/ abandoned or not. The results show that the detection accuracy of 83% was obtained which outperform the SOBS method with 67% accuracy. Future research should focus on tracking the person that abandoned the object for onward prosecution

Health Implications of Work-Related Stress among Academic Staff of Tertiary Institutions in Katsina State

This study investigated the health implications of work-related stress among academic staff of tertiary institutions in Katsina State, Nigeria. This research adopts a descriptive survey design. The population of this study is 2,036 academic staff from thirteen institutions. A sample of 328 academic staff was drawn, using proportionate stratified sampling technique. A self-developed questionnaire (HIWRS-Q) with reliability of 0.75 was used. Chi-square and t-test were used to test the hypotheses at 0.05 level of significance. Findings of this study revealed that 105 (32%) of the respondents do not experienced health implications of work-related stress while, 223 (68%) of the respondents experienced health implications of work-related stress. Also, there is significant health implication of work-related stress among academic staff (P=0.0010.05). It is recommended among others that the State government should improve on the working environment and conditions of academic staff to be health-friendly, health enhancing conditions for achieving academic excellence, and sustainable productivity in the State

Analisis Pengembangan Karakter, Keterampilan Proses Sains, Dan Penguasaan Konsep Siswa Pada Topik Koloid Melalui Pembelajaran Inkuiri Terbimbing

This study aims to obtain a model of learning that develop character values​​, science process skills, and mastery of concepts students. This study was designed with a quasi-experimental methods, to form "the two-group pretest-posttest design". The instrument used in this study is the science process skills test items and mastery of concepts, student worksheets, observation sheets and questionnaires of students and teachers. The subjects of this study were students at one of the high schools in Kampar regency of Riau as many as 24 peoples in experimental class and 24 peoples in control class. The results showed that the guided inquiry learning students can develop character, enhancing science process skills and mastery of concepts students colloid significantly compared to conventional learning

Analysis of Agricultural Extension Methods Used by Extension Workers for Conflict Resolution among Agro – Pastoralists in Adamawa State, Nigeria

The study was conducted to analyze the agricultural extension methods used in conflict resolution among agro – pastoralists in Adamawa State, Nigeria. A multi – stage random sampling technique was used to select 160 respondents who were administered interview schedules. Data were analyzed using descriptive statistics (frequencies and percentages) statistics and inferential (multiple regression) statistics. The study indicated that livestock destruction of farmlands was the major (50.63%) source of conflict among the respondents. The results revealed that the major (55.0%) source of conflict resolution among the respondents was community leaders in conflict resolutions. The study showed that truce was the most important type of conflict resolution used by respondents (59.38%). Majority (82.5%) of the respondents preferred face to face extension contact methods used for learning conflict resolution. All the positive significant relationship at 5% levels indicated that, an increase in each of these extension methods is likely to increase in conflict resolutions among agro – pastoralists. The study recommended that extension working environment should be strengthened with motivational mechanism to achieve the desired impact on conflict resolution among agro – pastoralists in the study area. Key words: Sources of conflict, Conflict Resolution and Agro – Pastoralists, Adamawa State, Nigeri

PERFORMANCE OF MULTIPLE LINEAR REGRESSION AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS IN PREDICTING ANNUAL TEMPERATURES OF OGUN STATE, NIGERIA

The performance of Autoregressive Moving Average and Multiple Linear Regression Models in predicting minimum and maximum temperatures of Ogun State is herein reported. Maximum and Minimum temperatures data covering a period of 29 years (1982 -2009) obtained from the Nigerian Meteorological Agency (NiMet), Abeokuta office, Nigeria, were used for the analyses. The data were first processed and aggregated into annual time series. Mann-Kendal non-parametric test and spectral analysis were carried out to detect whether there is trend, seasonal pattern, and either short or long memory in the time series. Mann-Kendal Z-values obtained are –0.47 and –2.03 for minimum and maximum temperatures respectively, indicating no trend, though the plot shows a slight change. The Lo’s R/S Q(N,q) values for minimum and maximum temperatures are 3.67 and 4.43, which are not within the range 0.809 and 1.862, thus signifying presence of long memory. The data was divided into two and the first 20 years data was used for model development, while the remaining was used for validation. Autoregressive Moving Average (ARMA) model of order (5, 3) and Autoregressive (AR) model of order 2 are found best for predicting minimum and maximum temperatures respectively. Multiple Linear Regression (MLR) model with 4 features (moving average, exponential moving average, rate of change and oscillator) were fitted for both temperatures. The ARMA and AR models were found to perform better with Mean Absolute Percentage Error (MAPE) values of -2.89 and -1.37 for minimum and maximum temperatures, compared with the Multiple Linear Regression Models with MAPE values of 141 and 876 respectively. Results of ARMA model can be relied on in generating forecast of temperature of the study area because of their minimal error values. However, it is recommended other climatic elements that were not captured in this paper due to unavailability of information be considered too in order to see which model is best for them. &nbsp

Calibration of ZMPT101B voltage sensor module using polynomial regression for accurate load monitoring

Smart Electricity is quickly developing as the results of advancements in sensor technology. The accuracy of a sensing device is the backbone of every measurement and the fundamental of every electrical quantity measurement is the voltage and current sensing. The sensor calibration in the context of this research means the marking or scaling of the voltage sensor so that it can present accurate sampled voltage from the ADC output using appropriate algorithm. The peakpeak input voltage (measured with a standard FLUKE 115 meter) to the sensor is correlated with the peak-peak ADC output of the sensor using 1 to 5th order polynomial regression, in order to determine the best fitting relationship between them. The arduino microcontroller is used to receive the ADC conversion and is also programmed to calculate the root mean square value of the supply voltage. The analysis of the polynomials shows that the third order polynomial gives the best relationship between the analog input and ADC output. The accuracy of the algorithm is tested in measuring the root mean square values of the supply voltage using instantaneous voltage calculation and peak-peak voltage methods. The error in the measurement is less than 1% in the peak-peak method and less than 2.5% in the instantaneous method for voltage measurements above 50V AC, which is very good for measurements in utility. Therefore, the proposed calibration method will facilitate more accurate voltage and power computing for researchers and designers especially in load monitoring where the applied voltage is 240V or 120V ranges

Organic Matter and Heavy Metals Leachate Effect on Soils of Selected Dumpsites in Selected North Central States of Nigeria

This research was conducted to assess the presence  of some heavy metals Chromium (Cr), Iron (Fe), Copper (Cu), Manganese (Mn), Lead (Pb), Zinc (Zn) and Aluminum (Al) in municipal solid waste dumps. Heavy metals in the soils were determined at varying depths of 0-5cm, 5-15cm and 15-30cm to assess the extent of pollution and the effects of pH and organic matter in the soil. Heavy metals concentrations were analysed using Atomic Absorption Spectrophotometer. The results revealed a significant difference (p < 0.05) in the concentrations of heavy metals across varying depth at the dumpsites when compared with control points in the same location. Mn had the highest mean 131.22 ± 25.98 mgkg-1 followed by Fe, which is 69 ± 11.02 mgkg-1 at the dumpsites. Mean concentration of all the Heavy metals investigated at the various dumpsites studied were significantly higher than at the control point and below the maximum standard levels set by FAO and WHO for agricultural soils. Result of particle distribution indicated higher sand content (> 80.0%) and lower clay and silt contents in both dumpsite and control site which implies the ease of movement of dissolved metals in the soil environment. Organic Matter (OM) content at dumpsites was observed to be slightly higher than at the control sites. The data also signifies that as pH decreases and %OM decreases the concentration of these heavy metals decreases down the soil profile. It is, therefore, concluded that accumulation of heavy metals in depth was highly correlated with pH and organic matter content

Genetic variability and heritability of some selected of cowpea (Vigna unguiculata (L) Walp) lines

The success of most crop improvement programs largely depends upon the genetic variability and the heritability of desirable traits. The magnitude and type of genetic variability help the breeder to determine the selection criteria and breeding schemes to be used for improvement purposes. Two different but related experiments, one in 2011 rainy season (August to November) and the second in 2012 dry season (February to May) were carried out at ICRISAT-Kano, Nigeria screen house to estimate the genetic variability and heritability of some traits in selected cowpea lines. Results of the study showed that there were considerable variations among the lines for duration of vegetative and reproductive phase and for yield characters (seed/pod, number of pod/plant, weight of pod/plant, and 100 seed weight). Broad sense heritability estimate (h2) was 83% for 100 seed weight, 53% for Number of seed per pod, 48% for days to first flower and 46% for number of root nodules. This information showed that there is sufficient genetic variability to justify selection for improvement in the cowpea. This result will be of immense practical uses for plant breeders to choose parent of interest to meet different breeding objectives

Parameters identification and optimization of photovoltaic panels under real conditions using Lambert W-function

This paper proposes a new approach based on Lambert W-function to extract the electrical parameters of photovoltaic (PV) panels. This approach can extract the optimal electrical characteristics of the PV panel under variable conditions of irradiation and temperature. Three benchmarking panels (shell SP70 monocrystalline silicon, shell ST40 thin film, and KC200GT Polycrystalline Silicon) are demonstrated and analyzed considering the electrical characteristics provided by the manufacturers. A comprehensive assessment is carried out under different weather condition to validate the capability and the robustness of the proposed approach. Furthermore, the simulated output characteristics of the three modules Photovoltaic are almost comparable and reproduce faithfully the manufacturer’s experimental data The novelty of this study is the using a new hybrid analytical and numerical method that straight forward and effective given value of Root mean square error less than those obtained by others methods that indicate the estimated results are very close to the experimental values provided by the manufacturers