Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of aboveground biomass of potato based on ground hyperspectral

Biomass is an important indicator of crop population characteristics and growth monitoring. Rapid and accurate monitoring of crop biomass is important for precise management of farmland. The spectral indices of the combination of any two bands of 350~2500nm were obtained that have good correlation with biomass were screened out through correlation analysis. At the same time, they were as input variables of biomass estimation models. Above-biomass of potato estimation models were established with partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF). The result showed the potato tuber formation period and the tuber growth period, the combination index using the PLSR method to construct the potato biomass estimation model is higher, the starch accumulation period and the mature period, the combination index using MLR method to construct the biomass estimation model is high, can be better to realize the potato biomass estimation
Real-Time Driver-Drowsiness Detection System Using Facial Features

The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. In this paper, we propose a system called DriCare, which detects the drivers’ fatigue status, such as yawning, blinking, and duration of eye closure, using video images, without equipping their bodies with devices. Owing to the shortcomings of previous algorithms, we introduce a new face-tracking algorithm to improve the tracking accuracy. Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers’ state. By combining the features of the eyes and mouth, DriCare can alert the driver using a fatigue warning. The experimental results showed that DriCare achieved around 92% accuracy.
A Booster Analysis of Extreme Gradient Boosting for Crop Classification using PolSAR Imagery

This study evaluates the impacts of three booster types (two tree-based and one linear model) in extreme gradient boosting (XGBoost) for crop classification using multi-temporal PolSAR (Polarimetric Synthetic Aperture Radar) images. Ensemble learning algorithms have received great attention in remote sensing for classification due to their greater performance compared to single classifiers in terms of accuracy. Extreme gradient boosting is the regularized extension of traditional boosting techniques and could overcome the overfitting constrain of gradient boosting (a.k.a gradient boosting machine). Three types of booster which are linear booster, tree booster and DART (Dropouts meet Multiple Additive Regression Trees) booster were tested on XGBoost for crop classification. From the multi-temporal PolSAR data, two types of polarimetric dataset (linear backscatter coefficients and Cloude–Pottier decomposed parameters) were extracted and incorporated into the classification step. The impacts of polarimetric features for crop classification were also analyzed in detailed besides exploring the boosting types of XGBoost. Our experimental results demonstrated that tree booster and DART booster were found to be superior compared the linear booster in terms of overall classification accuracy for both polarimetric dataset. The highest classification accuracy (87.97%) was achieved by tree booster with linear backscatter coefficients. Furthermore, linear backscatter coefficients achieved higher performance with respect to Cloude–Pottier decomposition in terms of classification accuracy.
Optimizing the Parity Check Matrix for Efficient Decoding of RS-Based Cloud Storage Systems

In large scale distributed systems such as cloud storage systems, erasure coding is a fundamental technique to provide high reliability at low monetary cost. Compared with the traditional disk arrays, cloud storage systems use an erasure coding scheme with both flexible fault tolerance and high scalability. Thus, Reed-Solomon (RS) Codes or RS-based codes are popular choices for cloud storage systems. However, the decoding performance for RS-based codes is not as good as XOR-based codes, which are optimized via investigating the relationships among different parity chains or reducing the computational complexity of matrix multiplications. Therefore, exploring an efficient decoding method is highly desired. To address the above problem, in this paper, we propose an Advanced Parity-Check Matrix (APCM) based approach, which is extended from the original Parity-Check Matrix based (PCM) approach. Instead of improving the decoding performance of XOR-based codes in PCM, APCM focuses on optimizing the decoding efficiency for RS-based codes. Furthermore, APCM avoids the matrix inversion computations and reduces the computational complexity of the decoding process. To demonstrate the effectiveness of the APCM, we conduct intensive experiments by using both RS-based and XOR-based codes under cloud storage environment. The results show that, compared to typical decoding methods, APCM improves the decoding speed by up to 32.31% in the Alibaba cloud storage system.
Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM

The increase of network size and sensory data leads to many serious problems to the wireless sensor networks due to the limited energy. Data prediction method is helpful to reduce network traffic and increase the network lifetime accordingly, especially by exploring data correlation among the sensory data. Data prediction can also be used to recover abnormal/lost data in case these sensor nodes fail to work. The current prediction methods in wireless sensor networks do not make full usage of the spatial-temporal correlation between wireless sensor nodes, and thus leads to higher prediction error relatively. This paper proposes a novel model for multi-step sensory data prediction in wireless sensor network. Firstly, we introduce the artificial neural networks based on 1-D CNN (One-Dimensional Convolutional Neural Network) and Bi-LSTM (Bidirectional Long and Short-Term Memory) to get the abstract features of different attributes via the pre-processed sensory data. Then, these abstract features are used to obtain one-step prediction. Finally, the multi-step prediction is introduced by using historical data and the prediction results of the previous step iteratively. Experiment results show that after selecting suitable node combinations in which the spatial-temporal correlation is highlighted, the proposed multi-step predictive model can predict multi-step (short and medium term) sensory data, and its performance is better compared with other related methods.
Recognition of Speed Limit from Traffic Signs Using Naive Bayes Classifier

The detection and recognition of road speed limit signs is an important task in advanced driver assistance system (ADAS). The speed limit signs are important in informing the driver about allowable speed in a particular area. It increases the safety, as it provides information about the circumstances of the road. Different systems are being implemented by the government authorities to prevent accidents due to over speed. Here the proposed system provides efficient detection and identification of speed limit signs. The system operates in the following way: first it involves the detection of the sign board and then performs segmentation followed by geometric detection. Hough transform algorithm is employed to detect the sign board with saliency based approach. Second the system detects the characters from the extracted sign board. Finally it involves recognition of speed limit sign using Naive Baye’s employed classifier. The algorithm is successfully tested and shows 94% accuracy.
Use Of Deep Neural Networks For Crop Yield Prediction: A Case Study Of Soybean Yield in Lauderdale County, Alabama, USA

World population is constantly increasing and it is necessary to have sufficient crop production. Monitoring crop growth and yield estimation are very important for the economic development of a nation. The prediction of crop yield has direct impact on national and international economies and play important role in the food management and food security. Deep learning gains importance on crop monitoring, crop type classification and crop yield estimation applications with the recent advances in image classification using deep Convolutional Neural Networks. Traditional crop yield prediction approaches based on remote sensing consist of classical Machine Learning methods such as Support Vector Machines and Decision Trees. Convolutional Neural Network (CNN] and Long-Short Term Memory Network (LSTM] are deep neural network models that are proposed for crop yield prediction recently. This study focused on soybean yield prediction of Lauderdale County, Alabama, USA using 3D CNN model that leverages the spatiotemporal features. The yield is provided from USDA NASS Quick Stat tool for years 2003-2016. The satellite data used is collected from NASA’s MODIS land products surface reflectance, land surface temperature and land surface temperature via Google Earth Engine. The root mean squared error (RMSE] is used as the evaluation metric in order to be able to compare the results with other methods that generally uses RMSE as the evaluation metric.
Ultra-Efficient Silicon-on-Insulator Grating Couplers With Backside Metal Mirrors

Coupling of light from off-chip into highly complex silicon-based devices is currently focus of efforts and research. In this work, the experimental demonstration of a −0.50 dB (89%) coupling efficiency in the C-band with the help of an aperiodic grating coupler design in a 250 nm silicon-on-insulator (SOI) technology is shown. Further, this work reports about the latest results regarding focusing grating couplers with footprints of only 30 μm × 30 μm and a measured coupling efficiency of up to −0.93 dB (81%) at 1549 nm. Efficient grating coupler arrays are realized by using an expanded backside metal mirror-strip for the application of fiber arrays. In addition, this work presents aperiodic silicon grating couplers with an enhanced bandwidth to overcome the limited usability of grating couplers in wavelength division multiplexing (WDM) systems.
Location Privacy Preservation in VANET using Mix Zones – A survey

A Vehicular Networks are intelligent transportation system which provides data dissemination, and traffic management services. In VANETs, privacy is the major issue in safety-related application. It becomes a challenging problem to maintain the privacy of the vehicle and the user. To avoid linking of messages which is sent between the vehicles in the network, many privacy schemes utilizes pseudonym changing mechanism. But the beacons containing spatiotemporal information makes the vehicle vulnerable to attacks and privacy is breached. Hence, preserving the context of privacy is a major challenge in the vehicular network. In order to enhance the privacy, mix zone is considered as the optimal method among pseudonym changing mechanism. The mix zones are created by multiple numbers of vehicles that concurrently change their pseudonym to admit uncertainty to the malevolent competitors such that it turns into inaccessible for them to extract the cooperation between the current and former pseudonyms employed by the vehicles. This paper reviews various pseudonym strategies and mix zone schemes for privacy preservation.
High Speed Multipliers using Counters based on Symmetric Stacking

High speed multipliers are essential in all computational units such as Arithmetic Logic Unit(ALU), Multiply Accumulate Unit and Digital Signal Processing (DSP) applications. In general, the performance of any DSP system is limited by its multiplication performance. Hence, the speed and power efficient multiplier algorithms are highly demanded in present scenario. In this Paper, high speed multipliers are designed by using binary counters based on symmetric stacking. In this work, 16-bit Wallace tree multiplier is considered for the analysis using conventional counters and symmetric stacking-based counters. Further, the performance is compared in terms of delay, area. All the blocks used in this work are programmed using Verilog HDL and they are Synthesized using Xilinx.
An Enhanced Energy Efficient Routing Method for Mobile Sink Using Ant Colony Optimisation Based Clustering in WSN

WSNs are composed of hundreds or thousands of tiny sensors that monitor the environment and send the gathered information to the base station. Since the sensors are typically powered by tiny irreplaceable batteries which usually cannot be recharged, the design of an energy efficient routing algorithm is an important research issue for wireless sensor networks. The sensor devices near the base station will be always busy with data transfer. Due to continuous data transfer these end nodes near the base station suffer from resource exhaustion. The revolution of mobile sink introduction in to the wireless sensor networks balance the load on the end nodes, by distributing the load with large number of nodes in the networks. Mobile sink moves throughout the network and collect the information from the nodes. Mobile sinks will save the resources in the WSNs and increases the life time of the WSNs by decreased multi hop communication. This paper proposes an enhanced routing method for the mobile sink which uses Ant colony optimisation for finding out the optimised path. Data gathering of the mobile sink is made effective by using an efficient clustering strategy which uses an energy efficient methodology for cluster head selection. A significant strategy for notifying the position of the mobile sink to the sensor nodes is also adopted.
GERP: A Grid-based Efficient Routing Protocol for WSN

Wireless sensor network (WSN) consisting a set of sensor nodes. The major function of sensor node is to monitor the physical or environmental conditions and send it to sink or base station. Sensor nodes have limited power batteries it lead to the network life time very difficult and expensive. To increase the network life time there is need an energy efficient routing protocol for WSN. In many of the existing energy efficient routing protocol in WSN having high amount of packet drop. So efficient data routing in WSN is still a challenging task. The proposed Grid-based Efficient Routing (GERP)Protocol for WSN provide energy efficient routing with less packet drop. In this routing protocol network is divide into equal sized grids and each grid having a grid head. The routing path from source to sink is established through grid heads. Each grid heads in active or semi-sleep mode which help to reduce the packet drop in network, and the remaining nodes in each grid are in sleep mode this save the energy and increase the network life time. Grid head re-election is done when any of these grid head fall below a threshold value. For handling the sink mobility the routing path changes for some grid heads which is nearer to the grid where mobile sink is currently positioned.
A Review on Supervised Machine Learning Text Categorization Approaches

The inception of abundant and hefty digitized text documents has emerged in recent years. Such texts need to be categorized technically for storage and retrieval purpose. Text Categorization Machine Learning techniques exist to manage the substantial data. These techniques can be divided into Supervised and Unsupervised Machine Learning techniques. In this paper we review Supervised Machine learning techniques that deal with generating patterns and hypothesis according to the prior knowledge. The objective of this paper is to highlight important features and methods of Supervised Machine Learning techniques. Selecting important features and then classifying the text is necessary for effective text categorization. The performance of Text Categorization and dimensionality improves by performing feature selection in the existing techniques. The importance of feature selection is considered in this paper. The general comparison table of techniques is presented for in depth parametric assessment.
Mixed Integer Linear Programming Model for the Optimal Operation of a Dual Source Heat Pump

This paper presents a mixed integer linear programming (MILP) model to operate a dual source heat pump (DSHP) optimally. The DSHP draws environmental energy from an underground ice storage tank (IST) or wind infrared sensitive collectors (WISC). WISC are further used to regenerate the IST. Thermal energy storage (TES) stores the thermal output of the DSHP. A single-objective optimisation approach is applied to minimise the operational cost of the DSHP over a scheduling horizon of 24h. The developed framework is tested on various days, which are representative for each season of the year. Furthermore, it is investigated how the variable electricity price market data influence the dynamic behaviour of the DSHP.
Two-Level Attention Model Based Video Action Recognition Network

The complex environment background, lighting conditions, and other action-irrelevant visual information in the video frame bring a lot of redundancy and noise to the action spatial features, which seriously affects the accuracy of action recognition. Aiming at this point, we propose a recurrent region attention cell to capture the action-relevant regional visual information in the spatial feature, and according to the temporal sequential natures of the video, on the basis of the recurrent region attention cell, a Recurrent Region Attention model (RRA) is proposed. The recurrent region attention cell in the RRA iterates according to the temporal sequence of the video, so that the attention performance of the RRA is gradually improved. Secondly, we propose a Video Frame Attention model (VFA) that can highlight the more important frames in the whole action video sequence, so as to reduce the interference caused by the similarity between the heterogeneous action video sequences. Finally, we propose an end-to-end trainable network: Two-level Attention Model based video action recognition network (TAMNet). We experimented on two video action recognition benchmark datasets: UCF101 and HMDB51. Experiments show that our end-to-end TAMNet network can reliably focus on the more important video frames in the video sequence, and effectively capture the action-relevant regional visual information in the spatial features of each frame of the video sequence. Inspired by the two-stream structure, we construct a two-modalities TAMNet network. In the same training conditions, the two-modalities TAMNet network achieved optimal performance on both datasets.
Improving Industrial Computing Capacity with Fog Computing and Smart Systems

Fog computing, edge devices and cloud computing together can provide an impressive amount of resources for ongoing industrial projects by improving the utilization of existing edge resources. This paper will examine fog computing architecture, it's characteristics and its interoperability to edge computing and cloud computing. The aim is to investigate the essential knowledge necessary to support adoption of fog computing in challenging conditions which arise in smart factories and smart agriculture. The present work was motivated by recent developments in Artificial Intelligence indicating that there is great potential for the combination of fog computing and AI to maximize the potential of intelligent systems. This paper aims to present earlier findings which will support an undergoing research project concerning a novel generation of Artificial Intelligent Agents created to self-discover and orchestrate existing resources, actually present in industrial environments.
Recommendations for Nitrogen Fertilizer in Winter wheat Based on Nitrogen Nutrition Index

A critical nitrogen (Nc) concentration, defined as the minimum nitrogen (N) concentration required for maximum plant growth, could be used as an intermediate variable between remote sensing data and recommendation for N fertilizer. In this study, the critical N concentration dilution curve was established based on aboveground biomass (AGB) from data at jointing, booting, anthesis and filling stages. The quantitative correlations between normalized difference vegetation (NDVI) and nitrogen nutrition index (NNI) were established. Finally, an N recommendation model combined with the N fertilizer effect function and NDVI was established and verified by field test data. Results showed that the N concentration of winter wheat decreased gradually during the reproductive growth period, and The plant N concentration could be described by $N _{\mathrm{c}} =$ 6.27*AGB-0.54, with the R2 value of 0.80. Moreover, the thresholds of NDVI were 0.87, 0.91, 0.91 and 0.81 at jointing, booting, anthesis and filling stages, respectively, and the amounts of recommending nitrogen fertilizer were 2.63, 10.00, 11.11 and 10.00 kg N/hm2 when NNI value lowed 1% of corresponding period’s thresholds. Field experiments illustrated that jointing and booting stage could be used as the N fertilizer periods to guarantee winter wheat yield, and integrating Nc curve and hyperspectral data had advantages in N fertilizer recommendations.
New Ordered Policy Routing Protocol for Active Data Transmission in Mobile Ad-hoc Networks

MANET is a self-configurable data communication network to avoid collisions between mobility nodes with different channel allocation with different positions for different mobile nodes to communicate and leave communication. Analysis of random mobility nodes simulation at different situations is a complex task in mobile ad hoc networks. There are different types of lightweight protocol based approaches to configure mobile nodes and address load maintenance, packet loss in ad hoc networks. Different mobility models are introduced to explore node modulations in real time network applications. So that in this paper, we propose and hierarchical model i.e. Dynamic Position & Quorum based Opportunistic Routing Protocol (DPQORP) is introduced to capture node information with respect to server’s high level measures in mobile ad hoc networks with novel objectives. This approach also consist constraint based approach to explore different node information with different positions throughout the network with communication. This approach gives more results to increase quality of service (QOS) through mobile ad hoc networks and define flexibility on node information because of dynamic changes of mobile nodes. Simulated results of proposed approach gives better and efficient performance in real time ad hoc networks.
New Platform Affordances for Encouraging Social Interaction in MOOCs: The "Comment Discovery Tool" Interactive Visualisation Plugin

This paper describes the development of a tool which aggregates learner MOOC contributions into a wordcloud. It is designed to serve either as a concept filter or a way to discover new conversations. This is analysed using a measure developed as a heuristic for sociocultural learning in conversations and by a survey. A new pedagogical approach is suggested which adds to the theories behind MOOC pedagogy, by using novel platform affordances to increase active participation.