In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a sequence of point expansion stages. During the recursive procedure of generation, we not only obtain the coarse-to-fine point clouds for the target 3D model from every expansion stage, but also unsupervisedly discover the semantic segmentation of the target model according to the hierarchical/parent-child relation between the points across expansion stages. Moreover, the expansion modules and other elements used in our recursive generator are mostly sharing weights thus making the overall framework light and efficient. Extensive experiments are conducted to show that our point cloud generator has comparable or even superior performance on both generation and reconstruction tasks in comparison to various baselines, and provides the consistent co-segmentation among instances of the same object class.

As most existing works of single-view 3D reconstruction aim at learning the better mapping functions to directly transform the 2D observation into the corresponding 3D shape for achieving state-of-the-art performance, there often comes a potential concern on having the implicit bias towards the seen classes learnt in their models (i.e. reconstruction intertwined with the classification) thus leading to poor generalizability for the unseen object categories. Moreover, such implicit bias typically stemmed from adopting the object-centered coordinate in their model designs, in which the reconstructed 3D shapes of the same class are all aligned to the same canonical pose regardless of different view-angles in the 2D observations. To this end, we propose an end-to-end framework to reconstruct the 3D mesh from a single image, where the reconstructed mesh is not only view-centered (i.e. its 3D pose respects the viewpoint of the 2D observation) but also preliminarily represented as a composition of volumetric 3D primitives before being further deformed into the fine-grained mesh to capture the shape details. In particular, the usage of volumetric primitives is motivated from the assumption that there generally exists some similar shape parts shared across various object categories, learning to estimate the primitive-based 3D model thus becomes more generalizable to the unseen categories. Furthermore, we advance to propose a novel mesh augmentation strategy, CvxRearrangement, to enrich the distribution of training shapes, which contributes to increasing the robustness of our proposed model and achieves better generalization. Extensive experiments demonstrate that our proposed method provides superior performance on both unseen and seen classes in comparison to several representative baselines of single-view 3D reconstruction.

Selecting non-ferrous scrap metal suppliers involves evaluating each supplier and its available scrap metals in terms of quantitative and qualitative criteria in each selection round. This paper presents a new approach for developing and selecting fuzzy multi-criteria decision making (MCDM) methods to address the non-ferrous scrap metal supplier selection problem with fuzzy data. The approach develops different fuzzy MCDM methods by combining three normalization processes and three aggregation processes commonly used in MCDM research. In particular, a specific defuzzification process is used to reflect the decision maker's risk attitude about the current global commodity market. A validation procedure using fuzzy c-means clustering is applied to select among inconsistent ranking results produced by different fuzzy MCDM methods. An empirical study on a non-ferrous scrap metal company in China is conducted to illustrate how fuzzy MCDM methods are developed and selected.

This paper presents a fuzzy multiattribute decision making (MADM) approach to evaluating airport performance in terms of the airport operator, the passenger, and the airline dimensions. Assessments for quantitative measures are represented by crisp values, and assessments for qualitative measures and attribute weights are represented by triangular fuzzy numbers, obtained via surveys using linguistic terms. An effective algorithm is developed by using the α-cut concept for incorporating the decision-maker's confidence level about respondents' fuzzy assessments on performance ratings and attribute weights. Based on the concept of the degree of optimality, the algorithm obtains a quantitative performance index for each airport evaluated, relative to other comparable airports with respect to each of three evaluation dimensions individually and all three evaluation dimensions as a whole. The performance index obtained would help individual airports understand the comparative level of their performance on measures relating to operational management, airport facilities, and passenger service quality. An empirical study of performance evaluation of 11 Asia-Pacific major international airports is conducted to illustrate how the approach works.

The bandwidth-constrained routing problem (BCRP) asks for a route that has sufficient bandwidth for data transmission. When BCRP is defined for wired networks, it can be solved in polynomial time. On the other hand, when it is defined for wireless ad hoc networks, it is NP-complete if the underlying MAC protocol is TDMA-based or CDMA-over-TDMA-based. In this paper, we show that BCRP is still NP-complete, even if CSMA-based or contention-based CDMA MAC prot.ocols are used. Besides, we show that BCRP is polynomial-time solvable if the channel model is collision-free and the scheduling policy is FIFO. In wireless ad hoc networks, no MAC protocol was designed before, which would lead to a polynomial-time solution to BCRP. The results of this paper suggest a design for MAC protocols that can support QoS routing well.

The Bandwidth-Constrained Routing Problem (BCRP) asks for a route that has sufficient bandwidth for data transmission. When BCRP is defined for wired networks, it can be solved in polynomial time. On the other hand, when it is defined for multi-hop wireless networks, it is NP-complete if the underlying MAC protocol is TDMA-based or CDMA-over-TDMA-based. In this paper, we show that BCRP is still NP-complete, even if CSMA-based or contention-based CDMA MAC protocols are used. Besides, we show that BCRP is polynomial-time solvable if the underlying MAC protocol adopts CDMA channel model and FIFO scheduling policy. In multi-hop wireless networks, no MAC protocol was designed before which would lead to a polynomial-time solution to BCRP. The results of this paper suggest a design principle for MAC protocols that can support QoS routing well.

Due to varying wireless channel conditions, the IEEE 802.11 wireless local area network (WLAN) standard supports multiple modulation types to accommodate the tradeoff between data rate and bit error rate. In Heusse(2003), Rousseau, Berger-Sabbatel and Duda theoretically analyzed a performance anomaly when multi-rate stations with different modulation types exist in IEEE 802.11 WLANs. The performance anomaly is: the aggregate throughput of those stations transmitting at a higher data rate will dramatically degrade below the same level as that of those stations transmitting at a lower data rate. In this paper, we address the anomaly problem and formulate a nonlinear mixed integer programming problem to maximize the total aggregate throughput of all stations subject to that the channel occupancy times among the stations transmitting at different data rates are kept at a fairness ratio. With its aid, a single-hop WLAN can dynamically accommodate the resource access usage to maximize the system throughput in varying fading environments. We prove that the optimization problem is intractable and propose a heuristic solution based on a penalty function with gradient-based approach to solve it. We show the effectiveness of the approach via computational experiments and provide some useful guidelines to regulate the parameters needed for the approach.

The IEEE 802.11 working group has been developing a new distributed MAC, called the enhanced distributed function (EDCF), to support service differentiation in the IEEE 802.11 MAC protocol. Besides, highspeed WLAN environments are also expected to provide wireless Internet services in hot spots such as airports, parks, etc. Since there are usually multiple service providers competing for providing wireless network access in hot spots, mobile users are free to choose their own service providers. In this paper, we analyze the resource management problem in the competitive environment, in which the EDCF protocol is implemented in all access points (APs) and mobile stations. We formulate admission control as a game and prove the game owns a Nash equilibrium solution. Based on the game, a service provider not only fulfills, for the most part, the QoS satisfaction of ongoing flows but also increases its own revenue. We evaluate the performance by means of throughput, packet delay and bandwidth violation ratio.

Fuzzy multiattribute decision making (MADM) has been widely used in ranking a finite number of decision alternatives characterized by fuzzy assessments with respect to multiple criteria. The overall utility of an alternative is obtained by aggregating the criteria weights and alternatives ratings, on which the ranking is based. However, different aggregation techniques often produce inconsistent outcomes for the same problem. This paper presents a validation procedure for examining the validity of fuzzy MADM methods, in order to assist in selecting a valid outcome for a given selection problem. The procedure uses fuzzy clustering as a basis to compare with the results from fuzzy MADM methods. A case study of the scrap metal buyer selection is conducted to demonstrate the effectiveness of the procedure.

With the provisioning of high-speed wireless LAN (WLAN) environments, traffic classes (e.g., VoIP or video-conference) with different QoS requirements will be introduced in future WLANs. The IEEE 802.11e draft is currently standardizing a distributed access approach, called the enhanced distributed coordination function (EDCF), to support service differentiation in the MAC layer. However, since each mobile station transmits data packets egotistically in a distributed environment, the QoS requirement of each traffic class may not be guaranteed. In this paper, we develop an admission control strategy to guarantee the QoS requirement of each traffic class. In order to provide a criterion for admission decision, we introduce an analytical model for EDCF to evaluate the expected bandwidth and the expected packet delay of each traffic class. The admission control strategy uses the performance measures derived from the analytical model to decide if a new traffic stream is permitted into the system. We validate the accuracy of the analytical model by using the ns-2 simulator. Some performance evaluations are also demonstrated to illustrate the effect of the proposed admission control strategy.