ALBERT

Description: The aim of this study is to exam the effect of microsprinkler irrigation technology under plastic film (MSPF) and to evaluate the reasonable micropore group spacing and capillary arrangement density in the greenhouse. Compared with drip irrigation under plastic film (DIPF) and microsprinkling irrigation (MSI) conditions, the effects of different micropore group spacing (L1: 30 cm micropore group spacing, L2: 50 cm micropore group spacing) and capillary arrangement density (C1: one pipe for one row, C2: one pipe for two rows, and C3: one pipe for three rows) with the MSPF on photosynthetic characteristics and fruit yield of tomatoes were studied using completely randomized trial design. The results showed that under the same irrigation amount, compared with DIPF and MSI, the photosynthetic rate of tomatoes treated with L1C2 increased by 8.24% and 13.55%, respectively. The total dry matter accumulation, yield, and water use efficiency at condition of L1C2 increased by 12.16%, 19.39%, and 10.03% compared with DIPF and 26.38%, 20.46%, and 31.02% compared with MSI, respectively. The results provide evidence that the MSPF can be applied to greenhouse tomatoes. The photosynthetic rate, total dry matter accumulation, yield, and water use efficiency of tomato leaves cultivated at a micropore group spacing of 30 cm were 1.07, 1.13, 1.14, and 1.13 times higher than those of 50 cm, respectively. With the decrease in capillary arrangement density, the photosynthetic characteristics of the tomato leaves, the total dry matter accumulation, and yield of tomatoes all experienced a decline. It is recommended to use a combination of one pipe for two rows of capillaries at a 30 cm micropore group spacing as the technical parameter of greenhouse tomato with MSPF in arid and semiarid sandy loam soils.

Description: The growing usage of the industrial cognitive radio sensor network (ICRSN) brings profound changes to the Internet of Things. The ICRSN is an emerging technique to transfer industrial data, which has strict and accurate communication requirements in a large number of areas such as environmental surveillance, building monitoring, control, and many other areas. The problem of maximizing the sum bandwidth by using a spectrum allocation algorithm has been extensively studied in this paper. Inspired by chaos theory and quantum computing, this work presents a new chaotic hybrid immune genetic algorithm (CHIGA). We then introduce a spectrum allocation model that considers both network reward, throughput, and convergence time. The improvement of CHIGA performance through experimental simulations is evaluated in terms of the sum network reward compared to methods based on simulated annealing (SA), ant colony optimization (ACO), and particle swarm optimization (PSO). Simulation results show that the CHIGA has a higher network reward and throughput existing optimized algorithms while maintaining total system throughput.

Description: Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

Description: The rapid advancement in the field of wireless sensor and cellular networks have established a rigid foundation for the Internet of Things (IoT). IoT has become a novel standard that incorporates various physical objects by allowing them to collaborate with each other. A large number of services and applications emerging in the field of IoT that include healthcare, surveillance, industries, transportation, and security. A service provider (SP) offers several services that are accessible through smart applications from any time, anywhere, and any place via the Internet. Due to the open nature of mobile communication and the Internet, these services are extremely susceptible to various malicious attacks, e.g., unauthorized access from malicious intruders. Therefore, to overcome these susceptibilities, a robust authentication scheme is the finest solution. In this article, we introduce a lightweight identity-based remote user authentication and key agreement scheme for IoT environment that enables secure access to IoT services. Our introduced scheme utilizes lightweight elliptic curve cryptography (ECC), hash operations, and XOR operations. The theoretical analysis and formal proof are presented to demonstrate that our scheme provides resistance against several security attacks. Performance evaluation and comparison of our scheme with several related schemes for IoT environment are carried out using the PyCrypto library in Ubuntu and mobile devices. The performance analysis shows that our scheme has trivial storage and communication cost. Hence, the devised scheme is more efficient not only in terms of storage, communication, and computation overheads but also in terms of providing sufficient security against various malicious attacks.

Description: The harsh testing environments of underwater scenarios make it extremely hard to plan a reasonable routing protocol for Underwater Sensor Networks (UWSNs). The main challenge in UWSNs is energy confinement. It is needed to plan an energy effective scheme which increases the life span of the network and also reduces the energy usage in data transfer from supplier to sink. In this research, we present the design of a routing protocol known as Energy Harvesting in UWSN (EH-UWSN). EH-UWSN is a compact, energy efficient, and high throughput routing protocol, in which we present utilization of energy gaining with coordinating transfer of data packets through relay nodes. Through Energy Harvesting, the nodes are capable to recharge their batteries from the outside surrounding with the ultimate objective to improve the time span of network and proceed data through cooperation, along with restricting energy usage. At the sink node, the mixing plan applied is centered on Signal-to-Noise Ratio Combination (SNRC). Outcomes of EH-UWSN procedure reveal good results in terms of usage of energy, throughput, and network life span in comparing with our previous Cooperative Routing Scheme for UWSNs (Co-UWSN). Simulation results show that EH-UWSN has consumed considerably lesser energy when compared with Co-UWSN along with extending network lifetime and higher throughput at the destination.

Description: This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.

Description: The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

Description: Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

Description: Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with R2 of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was 10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with R2〉0.9 from L8’s red, blue, and green bands, as compared to 0.47≤R2≥0.61 from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of R2〉70% from the visible and NIR bands, with equal RMSE at

Description: Diabetes is a metabolic disease that affects the ability of the body to process blood glucose, otherwise known as blood sugar. Diabetes occurs when the body produces minimal or no insulin. The diabetes patients check their glycaemic index after each meal and intake medicine to control glycaemic index. Traditionally, glycaemic index estimates the glucometer by acquiring blood sample. In this paper, we propose a noninvasive method to estimate glycaemic index from the pancreas. The magnetic signal from the pancreas acquires with Giant Magneto Resistance (GMR) sensor for glycaemic index estimation. The GMR acquired pancreatic magnetic signal process with Multi Synchro Squeezing Transform (MSST) for feature extraction. The MSST analysis shows significant changes in instantaneous frequency of the pancreas biomagnetic signal before and after meal consumption. The signal statistical parameters help to predict glycaemic index via regression modelling. The proposed method estimates glycaemic index with 88% accuracy.