MuLSi-Co: Multilayer Sinks and Cooperation-Based Data Routing Techniques for Underwater Acoustic Wireless Sensor Networks (UA-WSNs)

Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA-WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi-Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi-Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

A numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance

This paper presents a numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance. The arm angle is used to represent the self-motion manifold within a global arm configuration. The joint limits are analytically mapped to the arm angle space for joint limit avoidance. To profile the relation between the joint angle and arm angle, it is critical to characterize the singular arm angle for each joint. In the-state-of-the art methods, the existence of the singular arm angle is triggered by comparing a discriminant with zero given a threshold. We will show this leads to numerical issues since the threshold is inconsistent among different target poses, leading to incorrect range of the arm angle. These issues are overcome by associating indeterminate joint angles of tangent joints with angles of 0 or pi of cosine joints, rather than using an independent threshold for each joint. The closed-form algorithm in C++ code to perform numerically stable inverse kinematics of 7-DoF S-R-S manipulators with global arm configuration control and joint limit avoidance is also given.

Reinforcement Learning for Options Trading

Reinforcement learning has been applied to various types of financial assets trading, such as stocks, futures, and cryptocurrencies. Options, as a novel kind of derivative, have their characteristics. Because there are too many option contracts for one underlying asset and their price behavior is different. Besides, the validity period of an option contract is relatively short. To apply reinforcement learning to options trading, we propose the options trading reinforcement learning (OTRL) framework. We use options’ underlying asset data to train the reinforcement learning model. Candle data in different time intervals are utilized, respectively. The protective closing strategy is added to the model to prevent unbearable losses. Our experiments demonstrate that the most stable algorithm for obtaining high returns is proximal policy optimization (PPO) with the protective closing strategy. The deep Q network (DQN) can exceed the buy and hold strategy in options trading, as can soft actor critic (SAC). The OTRL framework is verified effectively.

Chain graph reduction into Power Chain Graphs

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graph in which the properties of the reduced graph are to be induced from the properties of the larger original graph. This paper introduces both a new method for reducing chain graphs to simpler directed acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure for structure learning of this new type of graph from correlational data of a Gaussian Graphical model (GGM). A definition for PCGs is given, directly followed by the reduction method. The structure learning procedure is a two-step approach: first, the correlation matrix is used to cluster the variables; and then, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm. The results of simulations are provided to illustrate the theoretical proposal, which demonstrate initial evidence for the validity of our procedure to recover the structure of power chain graphs. The paper ends with a discussion regarding suggestions for future studies as well as some practical implications.

Full Explicit Numerical Modeling in Time-Domain for Nonlinear Electromagnetics Simulations in Ultrafast Laser Nanostructuring

The purpose of this paper is to present a new and accurate, fully explicit finite-difference time-domain method for modeling nonlinear electromagnetics. The approach relies on a stable algorithm based on a general vector auxiliary differential equation in order to solve the curl Maxwell’s equation in a frequency-dependent and nonlinear medium. The energy conservation and stability of the presented scheme are theoretically proved. The algorithms presented here can accurately describe laser pulse interaction with metals and nonlinear dielectric media interfaces where Kerr and Raman effects, as well as multiphoton ionization and metal dispersion, occur simultaneously. The approach is finally illustrated by simulating the nonlinear propagation of an ultrafast laser pulse through a dielectric medium transiently turning to inhomogeneous metal-like states by local free-electron plasma formation. This free carrier generation can also be localized in the dielectric region surrounding nanovoids and embedded metallic nanoparticles, and may trigger collective effects depending on the distance between them. The proposed numerical approach can also be applied to deal with full-wave electromagnetic simulations of optical guided systems where nonlinear effects play an important role and cannot be neglected.

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs.

An Optimal and Stable Algorithm for Clustering Numerical Data

In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtained for every run. In real-world applications, optimal and stable clustering is highly desirable. This report introduces a new clustering algorithm called the zero k-approximate modal haplotype (Zk-AMH) algorithm that uses a simple and novel seeding mechanism known as zero-point multidimensional spaces. The Zk-AMH provides cluster optimality and stability, therefore resolving the aforementioned issues. Notably, the Zk-AMH algorithm yielded identical mean scores to maximum, and minimum scores in 100 runs, producing zero standard deviation to show its stability. Additionally, when the Zk-AMH algorithm was applied to eight datasets, it achieved the highest mean scores for four datasets, produced an approximately equal score for one dataset, and yielded marginally lower scores for the other three datasets. With its optimality and stability, the Zk-AMH algorithm could be a suitable alternative for developing future clustering tools.

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

Three-Dimensional Unified Motion Control of a Robotic Standing Wheelchair for Rehabilitation Purposes

Technological advances in recent years have shown interest in the development of robots in the medical field. The integration of robotic systems in areas of assistance and rehabilitation improves the user’s quality of life. In this context, this article presents a proposal for the unified control of a robotic standing wheelchair. Considering primary and secondary tasks as control objectives, the system performs tasks autonomously and the change of position and orientation can be performed at any time. The development of the control scheme was divided in two parts: (i) kinematic controller to solve the desired motion problem; and (ii) dynamic compensation of the standing wheelchair–human system. The design of the two controllers considers the theory of linear algebra, proposing a low computational cost and an asymptotically stable algorithm, without disturbances. The stability and robustness analysis of the system is performed by analyzing the evolution of the control errors in each sampling period. Finally, real experiments of the performance of the developed controller are performed using a built and instrumented standing wheelchair.

CGRA MODULO SCHEDULING FOR ACHIEVING BETTER PERFORMANCE AND INCREASED EFFICIENCY

Coarse-Grained Reconfigurable Architectures (CGRA) is an effective solution for speeding up computer-intensive activities due to its high energy efficiency and flexibility sacrifices. The timely implementation of CGRA loops was one of the hardest problems in the analysis. Modulo scheduling (MS) was productive in order to implement loops on CGRAs. The problem remains with current MS algorithms, namely to map large and irregular circuits to CGRAs over a fair period of compilation with restricted computational and high-performance routing tools. This is mainly due to an absence of awareness of major mapping limits and a time consuming approach to solving temporary and space-related mapping using CGRA buffer tools. It aims to boost the performance and robust compilation of the CGRA modulo planning algorithm. The problem with the CGRA MS is divided into time and space and the mechanisms between the two problems have to be reorganized. We have a detailed, systematic mapping fluid that addresses the algorithms of the time mapping problem with a powerful buffer algorithm and efficient connection and calculation limitations. We create a fast-stable algorithm for spatial mapping with a retransmission and rearrangement mechanism. With higher performance and quicker build-up time, our MS algorithm can map loops to CBGRA. The results show that, given the same compilation budget, our mapping algorithm results in a better rate for compilation. The performance of this method will be increased from 5% to 14%, better than the standard CGRA mapping algorithms available.