This work is on 3-D localization of sensor motes in massive swarms based solely on 1-D relative distance-measurements between neighbouring motes. We target applications in remote and difficult-to-access environments such as the exploration and mapping of the interior of oil reservoirs where hundreds or thousands of motes are used. These applications bring forward the need to use highly miniaturized sensor motes of less than 1 centimeter, thereby significantly limiting measurement and processing capabilities. These constraints, in combination with additional limitations posed by the environments, impede the communication of unique hardware identifiers, as well as communication with external, fixed beacons.
Even though mango productivity in Ethiopia is low due to moisture stress, there is no report on how such constraint could alleviate using Cocoon water-saving technology. Cocoon is small water reservoir technology which uses for plant growth in dry season. The objectives of this study were to introduce and evaluate effectiveness of water-saving techniques on mango seedlings survival and growth in Mihitsab-Azmati watershed, northern Ethiopia. In this experiment, five treatments of water-saving techniques with mango seedlings were evaluated. These were: Cocoon sprayed by tricel (T1), Cocoon painted by used engine oil (T2), Cocoon without tricel and oil (T3), manually irrigated seedlings (T4) and mango seedlings planted during rainy season (T5). The survival and growth performance of mango seedlings were recorded at six months and one-year after transplanting. Data on plant survival, height, number of leaves per plant, shoot length, stem diameter and crown width were subjected to analysis of variance and t-test. There were significant differences in the treatment effects on mango seedlings transplanted survival, plant height, number of leaves per plant, shoot length, stem diameter and crown width measured at six months and one-year after transplanting. The lowest survival rate (20 %) was found during both data collection time in T5. Six months after transplanting, the highest growth parameters were measured from T1 whereas the lowest was from T5. However, one-year after transplanting, the highest growth parameters were measured from T3. Plant heights increments between the two measurement periods for T3, T2, T1, T4 and T5 were 45.1, 38.5, 24.8, 9.8 and 7.0 cm, respectively; indicating that T3 performed better than the other treatments. The t-test on mean differences between the same growth parameter measured at 12 and six months after transplanting also showed significant differences. The Cocoon water-saving technology was superior in improving mango seedlings survival and growth in the study area. This study generalized that Cocoon seems promising, sustainable and highly scalable with mango seedlings at large-scale in the study area conditions. However, this technology should not be assumed to perform uniformly well in all environmental conditions and with all tree species before demonstrated on a pilot study.
MULTIFILE
In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
