Modern agricultural machinery demands adoption of embedded electronic and remote sensing technology for precision agriculture. One of the electronic devices commonly used is the virtual terminal (VT) for tractors. A VT’s functions and terminology are described in the ISO 11783 standard. This paper presents a control system design and implementation for a VT and some other electronic control units (ECUs) for agricultural vehicles based on that standard. Hardware and software development for the VT is implemented using the ISOAgLib open library, in the advanced embedded system. The main part of the system is an embedded board based on a Samsung S3C6410 ARM11 core microprocessor with a controller area network (CAN) module. Its working environment is Windows Embedded CE 6.0 (WinCE6.0). The ISOAgLib library provides abundant open sources consistent implementation of ISO 11783. It is written in C++ programming language using object-oriented technology. In this paper, we describe an ISO 11783-based tractor control system with a CAN and its implementation in the embedded system. This paper also explains the operation of a CAN-bus device driver in WinCE6.0 and some modifications of ISOAgLib for our target system. The target system consists of the VT, an ECU for the global positioning system (GPS), and an ECU for lighting for an agricultural tractor. The ECU for GPS and the ECU of a light controller are implemented using STM32F107F ARM Cortex M3-based development boards.

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments. As a computational approach to learning through interaction with the environment, reinforcement learning algorithms have been widely used for intelligent robot control, especially in the field of autonomous mobile robots. However, the learning process is slow and cumbersome. For practical applications, rapid rates of convergence are required. Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning, a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments. The state chain is built during the searching process. After one action is chosen and the reward is received, the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning. With the increasing number of Q-values updated after one action, the number of actual steps for convergence decreases and thus, the learning time decreases, where a step is a state transition. Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments. The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time, compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.

DeConverter is core software in a Universal Networking Language (UNL) system. A UNL system has EnConverter and DeConverter as its two major components. EnConverter is used to convert a natural language sentence into an equivalent UNL expression, and DeConverter is used to generate a natural language sentence from an input UNL expression. This paper presents design and development of a Punjabi DeConverter. It describes five phases of the proposed Punjabi DeConverter, i.e., UNL parser, lexeme selection, morphology generation, function word insertion, and syntactic linearization. This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter. Syntactic linearization is the process of defining arrangements of words in generated output. The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph, a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper. Special cases of syntactic linearization with respect to Punjabi language for UNL relations like ‘and’, ‘or’, ‘fmt’, ‘cnt’, and ‘seq’ have also been presented in this paper. This paper also provides implementation results of the proposed Punjabi DeConverter. The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology (IIT), Bombay, India, as gold-standards. The proposed system generates 89.0% grammatically correct sentences, 92.0% faithful sentences to the original sentences, and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale. The system is also able to achieve a bilingual evaluation understudy (BLEU) score of 0.72.

Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment, in which an F-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features, random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

This paper proposes an efficient PSP-based model for cross-coupled metal-oxide-semiconductor field-effect transistors (MOSFETs) with optimized layout in the voltage controlled oscillator (VCO). The model employs a PSP charge model to characterize the bias-dependent extrinsic capacitance instead of numerical functions with strong non-linearity. The simulation convergence is greatly improved by this method. An original scheme is developed to extract the parameters of the PSP charge model based on S-parameters measurement. The interconnection parasitics of the cross-coupled MOSFETs are modeled based on vector fitting. The model is verified with an LC VCO design, and exhibits excellent convergence during simulation. The results show improvements as high as 60.5% and 61.8% in simulation efficiency and accuracy, respectively, indicating that the proposed model better characterizes optimized cross-coupled MOSFETs in advanced radio frequency (RF) circuit design.

A novel approach to enlarge the guaranteed region of attraction in nonlinear systems with bounded parametric uncertainties based on the design of a nonlinear controller is proposed. The robust domain of attraction (RDA) is estimated using the parameter-dependent quadratic Lyapunov function and enlarged by the optimal controlling parameters. The problem of extending the RDA is indicated in a form of three-layer optimization problem. Some examples illustrate the efficiency of the proposed strategy in enlarging RDA.

Due to the importance of metal layers in the product yield, serpentine test structures are usually fabricated on test chips to extract parameters for yield prediction. In this paper, the confidence level and estimation precision of the average defect density on metal layers are investigated to minimize the randomness of experimental results and make the measured parameters more convincing. On the basis of the Poisson yield model, the method to determine the total area of all serpentine test structures is obtained using the law of large numbers and the Lindeberg-Levy theorem. Furthermore, the method to determine an adequate area of each serpentine test structure is proposed under a specific requirement of confidence level and estimation precision. The results of Monte Carlo simulation show that the proposed method is consistent with theoretical analyses. It is also revealed by wafer experimental results that the method of designing serpentine test structure proposed in this paper has better performance.