The use of functional size measurement (FSM) methods in software development organizations is growing during the years. Also, object oriented (OO) techniques have become quite a standard to design the software and, in particular, Use Cases is one of the most used techniques to specify functional requirements. Main FSM methods do not include specific rules to measure the software functionality from its Use Cases analysis. To deal with this issue some other methods like Kramer’s functional measurement method have been developed. Therefore, one of the main issues for those organizations willing to use OO functional measurement method in order to facilitate the use cases count procedure is how to convert their portfolio functional size from the previously adopted FSM method towards the new method. The objective of this research is to find a statistical relationship for converting the software functional size units measured by the International Function Point Users Group (IFPUG) function point analysis (FPA) method into Kramer-Smith’s use cases points (UCP) method and vice versa. Methodologies for a correct data gathering are proposed and results obtained are analyzed to draw the linear and non-linear equations for this correlation. Finally, a conversion factor and corresponding conversion intervals are given to establish the statistical relationship.

Both time-of-flight (ToF) cameras and passive stereo can provide the depth information for their corresponding captured real scenes, but they have innate limitations. ToF cameras and passive stereo are intrinsically complementary for certain tasks. It is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. Although some fusion methods have been presented recently, they fail to consider ToF reliability detection and ToF based improvement of passive stereo. As a result, this study proposes an approach to integrating ToF cameras and passive stereo to obtain high-accuracy depth maps. The main contributions are: (1) An energy cost function is devised to use data from ToF cameras to boost the stereo matching of passive stereo; (2) A fusion method is used to combine the depth information from both ToF cameras and passive stereo to obtain high-accuracy depth maps. Experiments show that the proposed approach achieves improved results with high accuracy and robustness.

Any node in a wireless sensor network is a resource constrained device in terms of memory, bandwidth, and energy, which leads to a large number of packet drops, low throughput, and significant waste of energy due to retransmission. This paper presents a new approach for predicting congestion using a probabilistic method and controlling congestion using new rate control methods. The probabilistic approach used for prediction of the occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses a back-off selection scheme and also rate allocation schemes, namely rate regulation (RRG) and split protocol (SP), to improve throughput and reduce packet drop. A back-off interval selection scheme is introduced in combination with rate reduction (RR) and RRG. The back-off interval selection scheme considers channel state and collision-free transmission to prevent congestion. Simulations were conducted and the results were compared with those of decentralized predictive congestion control (DPCC) and adaptive duty-cycle based congestion control (ADCC). The results showed that the proposed method reduces congestion and improves performance.

The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.

We propose a robust scheme to achieve the synchronization of chaotic systems with modeling mismatches and parametric variations. The proposed algorithm combines high-order sliding mode and feedback control. The sliding mode is used to estimate the synchronization error between the master and the slave as well as its time derivatives, while feedback control is used to drive the slave track the master. The stability of the proposed design is proved theoretically, and its performance is verified by some numerical simulations. Compared with some existing synchronization algorithms, the proposed algorithm shows faster convergence and stronger robustness to system uncertainties.

Reinforcement learning (RL) in real-world problems requires function approximations that depend on selecting the appropriate feature representations. Representational expansion techniques can make linear approximators represent value functions more effectively; however, most of these techniques function well only for low dimensional problems. In this paper, we present the greedy feature replacement (GFR), a novel online expansion technique, for value-based RL algorithms that use binary features. Given a simple initial representation, the feature representation is expanded incrementally. New feature dependencies are added automatically to the current representation and conjunctive features are used to replace current features greedily. The virtual temporal difference (TD) error is recorded for each conjunctive feature to judge whether the replacement can improve the approximation. Correctness guarantees and computational complexity analysis are provided for GFR. Experimental results in two domains show that GFR achieves much faster learning and has the capability to handle large-scale problems.

We propose a new power conversion system for a permanent magnet synchronous generator (PMSG) based grid-connected wind energy conversion system (WECS) operating with fully-controlled back-to-back current-source converters. On the generator side, two independent current-source rectifiers (CSRs) with space-vector pulse width modulation (SVPWM) are employed to regulate and stabilize DC-link currents. Between DC-link and the electrical grid, a direct-type three-phase five-level current-source inverter (CSI) is inserted as a buffer to regulate real and reactive power fed to the grid and thus adjusts the grid side power-factor. We also present a current-based maximum power point tracking (MPPT) scheme, which helps the generator extract the maximum power through closed-loop regulation of the generator speed. By applying the multilevel modulation and control strategies to the grid-side five-level CSI, a multilevel output current waveform with less distortion is produced, and the bulk requirement of the output capacitor filter to eliminate the harmonic current is reduced. All the proposed concepts are verified by simulation models built in a PSIM environment.