This study aimed to evaluate the performance of an unmanned aerial vehicle based remote sensing to quantify Bambara groundnut crop state variables. During Malaysia’s 2018/19 Bambara growing season, remotely sensed colour infrared images and in-situ crop state variables were acquired at vegetative, flowering, podding, podfilling, maturity and senescence phenological stages. Five common vegetation indices (VIs) were calculated from the images resulting in single stage and cumulative VIs (∑VIs). Correlation analyses were used to examine the relationship between crop variables and single stage VIs/∑VIs. Linear and machine learning (ML) regression were used to estimate crop variables using the VIs/∑VIs as input. The best single-stage correlations were observed at flowering. ∑VIs from vegetative to senescence had strongest correlations with crop variables. K-Nearest Neighbour regression outperformed other ML regression in estimating crop variables. However, multiple linear regression were as good as the best performing ML algorithms in estimating groundnut crop variables.

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.

Visual design plays an important role in online display advertising: changing the layout of an online ad can increase or decrease its effectiveness, measured in terms of click-through rate (CTR) or total revenue. The decision of which lay- out to use for an ad involves a trade-off: using a layout provides feedback about its effectiveness (exploration), but collecting that feedback requires sacrificing the immediate reward of using a layout we already know is effective (exploitation). To balance exploration with exploitation, we pose automatic layout selection as a contextual bandit problem. There are many bandit algorithms, each generating a policy which must be evaluated. It is impractical to test each policy on live traffic. However, we have found that offline replay (a.k.a. exploration scavenging) can be adapted to provide an accurate estimator for the performance of ad layout policies at Linkedin, using only historical data about the effectiveness of layouts. We describe the development of our offline replayer, and benchmark a number of common bandit algorithms.

The availability of a large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a Bulk Synchronous Processing (BSP) framework, based on a fixed or predefined sequence of communication, to distribute the join processing loads over a shared-nothing cluster. We consider various processing and communication overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues.We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.

ABSTRACT In mobile wireless data access networks, remote data access is expensive in terms of bandwidth consumption. An efficient caching scheme can reduce the amount of data transmission, hence, bandwidth consumption. However, an update event makes the associated cached data objects obsolete and useless for many applications. Data access frequency and update play a crucial role in deciding which data objects should be cached. Seemingly, frequently accessed but infrequently updated objects should have higher preference while preserving in the cache. Other objects should have lower preference or be evicted, or should not be cached at all, to accommodate higher‐preference objects. In this paper, we proposed Optimal Update‐based Replacement, a replacement or eviction scheme, for cache management in wireless data networks. To facilitate the replacement scheme, we also presented two enhanced cache access schemes, named Update‐based Poll‐Each‐Read and Update‐based Call‐Back. The proposed cache management schemes were supported with strong theoretical analysis. Both analysis and extensive simulation results were given to demonstrate that the proposed schemes guarantee optimal amount of data transmission by increasing the number of effective hits and outperform the popular Least Frequently Used scheme in terms of both effective hits and communication cost. Copyright © 2011 John Wiley & Sons, Ltd.

With the tremendous growth in mobile applications, communication accounts for a significant portion of a smartphone's total energy consumption. We studied the traffic pattern of smartphones and observed that a good portion of the packets are of small size and the generated traffic is bursty in nature. Motivated by these observations, we propose a Low Energy Data-packet Aggregation Scheme (LEDAS) in this paper. It accumulates a number of upper layer packets into a burst at medium access control (MAC) level, based on accumulation time, size, and number of packets. With this scheme, larger bursts lead to longer inactivity periods during which the communication module can be kept in doze mode. In addition, fewer MAC frames lead to less overheads and contentions in the wireless medium. However, the data packets incur delays due to the accumulation process. We have given a detail flowchart description of the technique. By means of analysis, we have derived expressions for the average values of burst size, burst inter-arrival times, and number of packets in a burst. We also evaluated the efficacy of the technique by simulations and showed the energy-delay trade-offs. Finally, we explained a test-bench to evaluate the energy saving potential of LEDAS on a smartphone.

Smartphones are constrained in battery-energy and resources such as processing power, storage, and Internet bandwidth. The capabilities of the smartphones can be enhanced if they can access the hardware, software resources and data on a laptop in an energy efficient way. This enables the owner of a smartphone and a laptop to use his/her devices with flexibility. With this view, we propose Universal Computing and Communication Interface (UCCI) to facilitate such sharing of resources between two wireless portable devices using wireless personal area network (WPAN). We developed prototypes of UCCI using BlackBerry 9700 and HTC Nexus One smartphones, and performed experiments on an energy measurement testbed. We show the efficacy of the proposed model through experiments, and evaluated the energy costs of an application which transfers file through 3G, WiFi and Bluetooth links. We also measured the costs of file compression and decompression on a HTC Nexus One smartphone, and discuss how they affect the file transfer costs.

One of the working requirements for many data access applications is the availability of the most updated information. In wireless communications service networks, remote data access consumes expensive wireless spectrum. An efficient cache can reduce the data access cost, by cutting down the amount of data transferred over the wireless channels. However, deploying an efficient cache in wireless environment is challenging, where caches are distributed all over the network and an update event invalidates all the cached copies of the updated data. In this paper, we formalize the concept of caching frequently accessed but infrequently updated data objects, and propose a cache replacement policy accordingly. To facilitate the replacement policy, two enhanced cache access policies are also proposed. The proposed caching policies are supported with strong theoretical analysis. We demonstrate that the policies guarantee an optimal number of cache hits in a caching system. Results from both analysis and our extensive simulations demonstrate that the proposed policies outperform the popular Least Frequently Used (LFU) scheme in terms of effective hits.

We consider the problem of processing exact results for sliding window joins over data streams with limited memory. Existing approaches deal with memory limitations by shedding loads, and therefore cannot provide exact or even highly accurate results for sliding window joins over data streams showing time varying rate of data arrivals. We provide an exact window join (EWJ) algorithm incorporating disk storage as an archive. Our algorithm spills window data onto the disk on a periodic basis, refines the output result by properly retrieving the disk resident data, and maximizes output rate by employing techniques to manage the memory blocks. The problem of managing the window blocks in memory-similar in nature to the caching issue-captures both the temporal and frequency related properties of the stream arrivals. At the same, we improve I/O efficiency by amortizing a disk scan over a large number of input tuple. We provide experimental results demonstrating the performance and effectiveness of the proposed algorithm.

Active warehousing has emerged in order to meet the high user demands for fresh and up-to-date information. Online refreshment of the source updates introduces processing and disk overheads in the implementation of the warehouse transformations. This paper considers a frequently occurring operator in active warehousing which computes the join between a fast, time varying or bursty update stream S and a persistent disk relation R, using a limited memory. Such a join operation is the crux of a number of common transformations (e.g., surrogate key assignment, duplicate detection etc) in an active data warehouse. We propose a partition-based join algorithm that minimizes the processing overhead, disk overhead and the delay in output tuples. The proposed algorithm exploits the spatio-temporal locality within the update stream, and improves the delays in output tuples by exploiting hot-spots in the range or domain of the joining attributes, and at the same time shares the I/O cost of accessing disk data of relation R over a volume of tuples from update stream S. We present experimental results showing the effectiveness of the proposed algorithm.

Extending the battery life of portable wireless devices has been in the focus of researchers for close to a decade. Several energy management techniques have been investigated at different levels of system design -- starting from silicon at the bottom to application design at the top, with communication protocols and operating system in between. In this paper, we present a model to estimate the energy cost of an application running on a portable wireless device. To develop the cost model, we partition a wireless device into two components, namely, computation and communication. Each component is modeled by a state-transition diagram. Two attributes are associated with each state: an average power cost and a state residence time. The cost of each state of the state-transition diagrams is validated by actual measurements. For a constant voltage supply, the average power cost of a state is denoted by the average current drawn by the component. The state residence times are estimated from the behavior of applications. The cost model has been validated by performing actual measurement of energy cost. We find that the estimated cost and the actual energy cost are within 5-10% of each other. This study will help us in improving the design of energy efficient software for portable devices. Moreover, the energy consumption breakdown into components will be an essential guide for future research in energy management of hardware and software systems.

In infrastructure wireless networks, nodes at the edge of the coverage area need to spend more energy to transmit their packets than those close to the Access Point (AP). Less energy is required if intermediate nodes can be used to forward data. To enable this, intermediate nodes need an incentive and there must be a mechanism for selecting these nodes. In this paper we propose Energy-aware Co-Operative (ECO) relaying for selecting relays to forward packets. The technique is based on the idea of Relative Energy Usage (REU), which reflects the proportion of energy that a node saves by forwarding its packets through relays. A node which saves more energy by using relays is more likely to be chosen as a relay. Conversely, nodes are only permitted to use relays proportionate to the amount of energy they themselves have spent as relays. We compare our scheme with direct transmission, minimum energy path (MnEP), and maximum residual energy path (MxRE). We show that ECO can transmit 50% more data than direct transmission, while using less energy on average. Although MnEP and MxRE can also transmit more data than direct transmission, they do so at severe energy cost to a small number of nodes, doubling the average energy usage, making them ill-suited to commercial networks.

Retrieving data from the memory of a remote computer is significantly faster than retrieving data from a remote storage device. Therefore, sharing caches among several computers can reduce the number of storage device access. In this paper, we propose a new collaboration scheme among clients in a computer cluster to share their caches with each other. The collaboration is designed based on peer-to-peer computing model. The scheme not only provides an easy to develop software architecture but also yields a near optimal performance with rea- sonably low overheads. Simulations are conducted to demonstrate the performance of the proposed scheme.

Modern single and multi-processor computer systems incorporate, either directly or through a LAN, a number of storage devices with diverse performance characteristics. These storage devices have to deal with workloads with unpredictable burstiness. Storage aware caching scheme - that partitions the cache among the disks, and aims at balancing the work across the diskssnecessary in this environment. Moreover, maintaining proper size for these partitions is crucial. The existing storage aware caching schemes assume linear relationship between cache size and hit ratio. But, in practice a (disk) partition may accumulate cache blocks (thus, choke the remaining disks) without increasing the hit ratio significantly. This disk choking phenomenon may degenerate the performance of the disk system. In this paper, we address this issue of disk choking and present a repartitioning framework based on the notion of marginal gains. Experimental results shows the effectiveness of our approach. We show that our scheme outperforms the existing storage-aware caching schemes while supplied with a workload showing the non-linear relationship between cache size and hit ratio.

In this paper, we propose a novel peer-to-peer architecture for wireless mobile networks where a cross-layered gossip-like protocol is the heart of the architecture. The goal of this architecture is to reduce the bandwidth consumption and at the same time, to provide more user participation flexibility. Simulation results are given to demonstrate the performance of the proposed peer-to-peer architecture.

For almost a decade we have been working at developing and using template-based models for parallel computing. Template-based models separate the specification of the parallel structuring aspects from the application code that is to be parallelized. A user provides the application code and specifies the parallel structure of the application using high-level icons, called templates. The parallel programming system then generates the code necessary for parallelizing the application. The goal here is to provide a mechanism for quick and reliable development of coarse-grain parallel applications that employ frequently occurring parallel structures. Our initial template-based system, FrameWorks, was positively received but had a number of shortcomings. The Enterprise parallel programming environment evolved out of this work. Now, after several years of experience with the system, its shortcomings are becoming evident. Controlled experiments have been conducted to assess the usability of our system in comparison with other systems. The paper outlines our experiences in developing and using these systems. A list of desirable characteristics of template-based models is given. The FrameWorks and Enterprise systems are discussed in the context of these characteristics and the results of our usability experiments. Many of our observations are relevant to other parallel programming systems, even though they may be based on different assumptions. Although template-base models have the potential for simplifying the complexities of parallel programming, they have yet to realize these expectations for high-performance applications. © 1998 John Wiley & Sons, Ltd.

High complexity of building parallel applications is often cited as one of the major impediments to the mainstream adoption of parallel computing. To deal with the complexity of software development, abstractions such as macros, functions, abstract data types, and objects are commonly employed by sequential as well as parallel programming models. This paper describes the concept of a design pattern for the development of parallel applications. A design pattern in our case describes a recurring parallel programming problem and a reusable solution to that problem. A design pattern is implemented as a reusable code skeleton for quick and reliable development of parallel applications. A parallel programming system, called DPnDP (Design Patterns and Distributed Processes), that employs such design patterns is described. In the past, parallel programming systems have allowed fast prototyping of parallel applications based on commonly occurring communication and synchronization structures. The uniqueness of our approach is in the use of a standard structure and interface for a design pattern. This has several important implications: first, design patterns can be defined and added to the system's library in an incremental manner without requiring any major modification of the system (extensibility). Second, customization of a parallel application is possible by mixing design patterns with low level parallel code resulting in a flexible and efficient parallel programming tool (flexibility). Also, a parallel design pattern can be parameterized to provide some variations in terms of structure and behavior.

The objective of this work is to investigate the issue of automatically detecting regions of interest (ROI's) in medical images. It is assumed that the regions to be detected can be roughly segmented by a threshold based on a likelihood measure of the ROI. First, an analysis of the global histogram is used to compute a preliminary threshold that is likely near the optimal one. The histogram analysis is motivated by the analytical result of a bell image intensity model proposed in this work. Then, the preliminary threshold is used to segment the input image, resulting in an attention map, which contains an attention region that approximates the ROI as well as many spurious ones. Due to the nonoptimality of the preliminary threshold, it can happen that the attention region contains a part of, or more regions than, the ROI. Learning takes place in two stages: 1) learning for automatic selection of the preliminary threshold value and 2) learning for automatically selecting the ROI from the attention map while dynamically tuning the threshold according to the learned-likelihood function. Experiments have been conducted to approximately locate the endocardium boundaries of the left and right ventricles from gradient-echo magnetic resonance (MR) images. Cardiac computed tomography (CT) images have also been used for testing. The boundary of the segmented region provided by this algorithm is not very accurate and is meant to be used for further fine tuning based on other application-specific measures.

A transitory image sequence is one in which no scene element is visible through the entire sequence. This article deals with some major theoretical and algorithmic issues associated with the task of estimating structure and motion from transitory image sequences. Two representations, world-centered (WC) and camera-centered (CC), behave very differently with a transitory sequence. The asymptotical error properties derived in this article indicate that one representation is significantly superior to the other, depending on whether one uses camera-centered or world-centered estimates. Rigorous experiments were conducted with real-image sequences taken by a fully calibrated camera system. The comparison demonstrated that a good accuracy can be obtained from transitory image sequences.>

Data parallel languages based on user-supplied data distribution directives significantly simplify the development of the initial version of a parallel application. However, selection of good data distributions leading to efficient computations is often quite difficult. Therefore, performance debuggers are usually employed to yield insights into effects of data distribution. This paper presents design and implementation of a performance debugger that is specifically targeted to meet the performance debugging requirements of a data-parallel programming model based on user-specified data distributions. The visual interface of the performance debugger provides feedback regarding data distributions amongst processor nodes, data access patterns during program execution, as well as statistical performance data. The feedback provided by the performance debugger is explicitly related back to objects in the programmer's source code.

The author describes a recursive technique to enhance image-sequences. This technique has the effect of motion-compensated temporal smoothings. It suppresses noise but does not blur the moving regions. The method does not require that the motion-field be explicitly computed in advance. Instead it incorporates an implicit model for temporal rate of change of intensity. Based on this model, the technique computes the optimal estimate of the instantaneous image intensity in an incremental fashion. The estimate improves over time. Experimental results are shown on image-sequences from various application domains.>

A new framework is described for computing image flow from time-varying imagery and recovering scene-depth from image flow. In this framework, image flow information available in the time-varying imagery is classified into two categories-conservation information and neighborhood information. Each type of information is recovered in the form of an estimate accompanied by a covariance matrix. Image flow is then computed, along with confidence measures, by fusing the two estimates on the basis of their covariance matrices. Dense depth-maps are computed from image flow using Kalman filtering.>

A computational model and system for the generation of distributed applications in a workstation environment are presented. The well-known RPC model is modified by a novel concept known as template attachment. A computation consists of a network of sequential procedures which have been encapsulated in templates. A small selection of templates is available from which a distributed application with the desired communication behavior can be rapidly built. The system generates all the required low-level code for correct synchronization, communication, and scheduling. This results in a system that is easy to use and flexible and can provide a programmer with the desired amount of control in using idle processing power over a network of workstations. The practical feasibility of the model has been demonstrated by implementing it for Unix-based workstation environments.>

A novel framework for computing image flow from time-varying imagery is described. This framework offers the following principal advantages. First, it allows estimation of certain types of discontinuous flow fields without any prior knowledge about the location of discontinuities. The flow fields thus recovered are not blurred at motion boundaries. Second, covariance matrices (or alternatively, confidence measures) are associated with the estimate of image flow at each stage of computation. The estimation-theoretic nature of the framework and its ability to provide covariance matrices make it very useful in the context of applications such as incremental estimation of scene-depth using techniques based on Kalman filtering. The framework is used to recover image flow from two image sequences. To illustrate an application, the image-flow estimates and their covariance matrices thus obtained are also used to recover scene depth.>