Scheduling shipments in closed-loop sortation conveyors

At the very core of most automated sorting systems— for example, at airports for baggage handling and in parcel distribution centers for sorting mail—we find closed-loop tilt tray sortation conveyors. In such a system, trays are loaded with cargo as they pass through loading stations, and are later tilted upon reaching the outbound container dedicated to a shipment’s destination. This paper addresses the question of whether the simple decision rules typically applied in the real world when deciding which parcel should be loaded onto what tray are, indeed, a good choice. We formulate a short-term deterministic scheduling problem where a finite set of shipments must be loaded onto trays such that the makespan is minimized. We consider different levels of flexibility in how to arrange shipments on the feeding conveyors, and distinguish between unidirectional and bidirectional systems. In a comprehensive computational study, we compare these sophisticated optimization procedures with widespread rules of thumb, and find that the latter perform surprisingly well. For almost all problem settings, some priority rule can be identified which leads to a low-single-digit optimality gap. In addition, we systematically evaluate the performance gains promised by different sorter layouts.     

Design and implementation of a same-user identification system in invoked reality space

The objective of this study is to solve the problem of user data not being precisely received from sensors because of sensing region limitations in invoked reality (IR) space, distortion of colors or patterns by lighting, and blocking or overlapping of a user by other users. The sensing scope range is thus expanded using multiple sensors in the IR space. Moreover, user feature data are accurately identified by user sensing. Specifically, multiple sensors are employed when not all of user data are sensed because they overlap with data of other users. In the proposed approach, all clients share the user feature data from multiple sensors. Accordingly, each client recognizes that the user is the same individual on the basis of the shared data. Furthermore, the identification accuracy is improved by identifying the user features based on colors and patterns that are less affected by lighting. Therefore, accurate identification of the user feature data is enabled, even under lighting changes. The proposed system was implemented based on system performance analysis standards. The practicality and system performance in identifying the same person using the proposed method were verified through an experiment.     

Efficient computation of distance labeling for decremental updates in large dynamic graphs

Since today’s real-world graphs, such as social network graphs, are evolving all the time, it is of great importance to perform graph computations and analysis in these dynamic graphs. Due to the fact that many applications such as social network link analysis with the existence of inactive users need to handle failed links or nodes, decremental computation and maintenance for graphs is considered a challenging problem. Shortest path computation is one of the most fundamental operations for managing and analyzing large graphs. A number of indexing methods have been proposed to answer distance queries in static graphs. Unfortunately, there is little work on answering such queries for dynamic graphs. In this paper, we focus on the problem of computing the shortest path distance in dynamic graphs, particularly on decremental updates (i.e., edge deletions). We propose maintenance algorithms based on distance labeling, which can handle decremental updates efficiently. By exploiting properties of distance labeling in original graphs, we are able to efficiently maintain distance labeling for new graphs. We experimentally evaluate our algorithms using eleven real-world large graphs and confirm the effectiveness and efficiency of our approach. More specifically, our method can speed up index re-computation by up to an order of magnitude compared with the state-of-the-art method, Pruned Landmark Labeling (PLL).     

3D‐vision based detection,localization, and sizing of broccoli heads in the field

This paper describes a 3D vision system for robotic harvesting of broccoli using low‐cost RGB‐D sensors, which was developed and evaluated using sensory data collected under real‐world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground‐truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross‐validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low‐cost 3D imaging for commercial broccoli harvesting.     

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems.     

A deadlock-free routing algorithm for dynamically reconfigurable Networks-on-Chip

We address routing in Networks-On-Chip (NoC) architectures that use irregular mesh topologies with Long-Range Links (LRL). These topologies create difficult conditions for routing algorithms, as standard algorithms assume a static, regular link structure and exploit the uniformity of regular meshes to avoid deadlock and maintain routability. We present a novel routing algorithm that can cope with these irregular topologies and adapt to run-time LRL insertion and topology reconfiguration. Our approach to accommodate dynamic topology reconfiguration is to use a new technique that decomposes routing relations into two stages: the calculation of output ports on the current minimal path and the application of routing restrictions designed to prevent deadlock. In addition, we present a selection function that uses local topology data to adaptively select optimal paths.The routing algorithm is shown to be deadlock-free, after which an analysis of all possible routing decisions in the region of an LRL is carried out. We show that the routing algorithm minimises the cost of sub-optimally placed LRL and display the hop savings available. When applied to LRLs of less than seven hops, the overall traffic hop count and associated routing energy cost is reduced. In a simulated 8 × 8 network the total input buffer usage across the network was reduced by 6.5%.     

On the Use of Enumeration for Investigating the Performance of Hypothesis Tests for Economic Models with a Discrete Response Variable

This article notes that it is now practical to use the method of enumerationto analyse the performance of estimators and hypothesis tests of fullyparametric binary data models. The general method is presented and thenemployed to investigate the power performance of a common misspecificationtest for the Probit model. The advantages, disadvantages and limitations ofenumeration compared with standard Monte Carlo simulation are thendiscussed. Finally, an example from experimental economics is used todemonstrate that the methodology can also be used in small empirical studies.     

Contrast-optimal k out of n secret sharing schemes in visual cryptography

Visual cryptography and (k,n)-visual secret sharing schemes were introduced by Naor and Shamir (Advances in Cryptology — Eurocrypt 94, Springer, Berlin, 1995, pp. 1–12). A sender wishing to transmit a secret message distributes n transparencies amongst n recipients, where the transparencies contain seemingly random pictures. A (k,n)-scheme achieves the following situation: If any k recipients stack their transparencies together, then a secret message is revealed visually. On the other hand, if only k−1 recipients stack their transparencies, or analyze them by any other means, they are not able to obtain any information about the secret message. The important parameters of a scheme are its contrast, i.e., the clarity with which the message becomes visible, and the number of subpixels needed to encode one pixel of the original picture. Naor and Shamir constructed (k,k)-schemes with contrast 2^−(k−1). By an intricate result from Linial (Combinatorica 10 (1990) 349–365), they were also able to prove the optimality of these schemes. They also proved that for all fixed kn, there are (k,n)-schemes with contrast . For k=2,3,4 the contrast is approximately and . In this paper, we show that by solving a simple linear program, one is able to compute exactly the best contrast achievable in any (k,n)-scheme. The solution of the linear program also provides a representation of a corresponding scheme. For small k as well as for k=n, we are able to analytically solve the linear program. For k=2,3,4, we obtain that the optimal contrast is at least and . For k=n, we obtain a very simple proof of the optimality of Naor's and Shamir's (k,k)-schemes. In the case k=2, we are able to use a different approach via coding theory which allows us to prove an optimal tradeoff between the contrast and the number of subpixels.     

Path Analysis and Renaming for Predicated Instruction Scheduling

Increases in instruction level parallelism are needed to exploit the potential parallelism available in future wide issue architectures. Predicated execution is an architectural mechanism that increases instruction level parallelism by removing branches and allowing simultaneous execution of multiple paths of control, only committing instructions from the correct path. In order for the compiler to expose and use such parallelism, traditional compiler data-flow and path analysis needs to be extended to predicated code. In this paper, we motivate the need for renaming and for predicates that reflect path information. We present Predicated Static Single Assignment (PSSA) which uses renaming and introduces Full -Path Predicates to remove false dependences and enable aggressive predicated optimization and instruction scheduling. We demonstrate the usefulness of PSSA for Predicated Speculation and Control Height Reduction. These two predicated code optimizations used during instruction scheduling reduce the dependence length of the critical paths through a predicated region. Our results show that using PSSA to enable speculation and control height reduction reduces execution time from 12 to 68%.