Limited Memory Resources Research Articles

The Impact of Persistent Pain on Working Memory and Learning

The study reviewed the evidence that persistent pain has the capacity to interrupt and consume working memory resources. It was argued that individuals with persistent pain essentially operate within a compromised neurocognitive paradigm of limited working memory resources that impairs task performance. Using cognitive load theory as a theoretical framework, the study investigated if multimedia materials could be used to support individuals with persistent pain. A 2 × 2 design was used where the first factor was the pain status of the participant (absence vs. presence for more than 6 months), and the second was instructional strategy (written + illustrations vs. written). Fifty-eight full-time teachers from two schools in New South Wales (Australia) were randomly assigned to an instructional strategy to learn about lightning formation. Participants that identified as experiencing pain for 6 or more months demonstrated clinically low levels of pain, but nevertheless performed significantly worse than pain-free participants on retention and transfer tests. For both pain and pain-free participants, there was a significant benefit in learning from multimedia instruction compared to a written text only strategy.

Reconfigurable Data Planes for Scalable Network Virtualization

Network virtualization presents a powerful approach to share physical network infrastructure among multiple virtual networks. Recent advances in network virtualization advocate the use of field-programmable gate arrays (FPGAs) as flexible high performance alternatives to conventional host virtualization techniques. However, the limited on-chip logic and memory resources in FPGAs severely restrict the scalability of the virtualization platform and necessitate the implementation of efficient forwarding structures in hardware. The research described in this manuscript explores the implementation of a scalable heterogeneous network virtualization platform that integrates virtual data planes implemented in FPGAs with software data planes created using host virtualization techniques. The system exploits data plane heterogeneity to cater to the dynamic service requirements of virtual networks by migrating networks between software and hardware data planes. We demonstrate data plane migration as an effective technique to limit the impact of traffic on unmodified data planes during FPGA reconfiguration. Our system implements forwarding tables in a shared fashion using inexpensive off-chip memories and supports both Internet Protocol (IP) and non-IP-based data planes. Experimental results show that FPGA-based data planes can offer two orders of magnitude better throughput than their software counterparts, and FPGA reconfiguration can facilitate data plane customization within 12 seconds. An integrated system that supports up to 15 virtual networks has been validated on the NetFPGA platform.

A Novel Key Management Scheme for Cluster Based Wireless Sensors Networks

WSN comprise of large number of sensor nodes with different hardware abilities and functions. Due to the limited memory resources and energy constraints, complex security algorithms cannot be used in sensor networks. Therefore, it is necessary to well balance between the security level and the associated energy consumption overhead to mitigate the security risks. In this paper, we present A Novel Key Management Scheme for Cluster Based Wireless Sensors Networks (NKMS). The security analysis demonstrates that NKMS is an effective solution to the key management of cluster based wireless sensor networks.

Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Observation of own exploration movements impairs haptic spatial perception

The present study was designed to assess whether the visibility of ones' own exploratory movements impairs or enhances perceptual speed and precision of haptic stimuli with varying complexity. Previous studies have shown that noninformative vision of steady surroundings improves haptic spatial perception. However, due to the serial nature of haptic processing and limited capacity of working memory resources, we hypothesized that noninformative vision of limb movements may impair haptic perception. The study sample consisted of ninety-eight healthy adults who were randomized into two groups, matched for sex and age. Participants were required to explore two-dimensional haptic stimuli with varying complexity and to recognize them visually. The difference between the two experimental groups was a screen that would prevent the participants from viewing their hands during exploration in the nonobservation condition (NonOb). The other half of participants were able to see their hands in the manual movement observation condition (MovOb) thanks to the special design of the stimuli. As hypothesized, the persons in the MovOb condition made significantly more errors. The difference in error frequency between participants of the MovOb and NonOb condition was greater for complex stimuli than for simple ones. These results suggest that incoming visual information about own manual exploration movements increases competitive pressure for limited working memory resources, and therefore, more recognition errors are made. Covering the hands during exploration may constitute a helpful simplification of the task's demands by supporting the maintenance of information in working memory.Additionally, the relation of haptic complexity and stimulus characteristics was analyzed.

Fuzzy-based Leader Selection for Topology Controlled PEGASIS Protocol for Lifetime Enhancement in Wireless Sensor Network

Wireless sensor Networks present a new generation of real time embedded systems with limited computation, energy and memory resources that are being used in wide variety of applications where traditional networking infrastructure is practically infeasible. Legitimate leader selection can drastically improve the lifetime of the sensor network. This paper proposes fuzzy logic methodology for leader election in PEGASIS based protocol PEGASIS-TC [1] based on two descriptors- residual energy of node and its proximity to Base Station. Simulation results show that depending on network configuration, a substantial increase in stability of network lifetime can be accomplished as compared to PEGASIS-TC.

Micro-ANP：A Novel Network Protocol Architecture for Underwater Sensor Network

Underwater sensor network (UWSN) adopts acoustic communication and is characterized by high delay, low bandwidth, high error rate, low energy-consumption requirement and sparse topology, which lead to conventional network protocols for terrestrial WSN or other wireless multi-hop networks are unable to satisfy the performance of UWSN. On the other hand, the limited resources of energy, CPU and memory cause that the protocol stack running on the sensor node shouldn’t be much complicated. The paper proposes a novel, green network protocol architecture model tailored for UWSN, referred to as Micro-ANP, which is three layered structure including application, network transport, and physical layer. Furthermore, basing on Micro-ANP we realize the packet size optimization of UWSN through Matlab simulation. Micro-ANP protocol architecture and packet size optimization improve the energy efficiency of UWSN, prolong the network lifetime and achieve green network while meeting the requirement of QoS with the constraints of limited resource.

Automatyczna aktualizacja oprogramowania w urządzeniach embedded

In this article implementation of a system of automatic software updates on embedded device with a single-chip microcontroller was presented. Mechanisms and protocols used in existing solutions working under control of operating systems was analyzed. Necessary optimizations were suggested that allow for implementation of an update mechanism in systems with limited memory resources. Implementations of chosen variants of proposed solutions in the microcontroller with Cortex core were described. Furthermore, this work also emphasizes a question of providing security of automatic software updates, with regard to unauthorized access, as well as resistance to failure of communication network.

Dynamic Data Storage Estimation for Multiple Concurrent Applications Using Probability Distribution Modeling in WSNs

Wireless sensor networks (WSN) have become a mainstream technology for environmental monitoring and observing various variables of interest over extended periods of time via large-scale networks of sensors. WSNs have a wide range of applications including wildfire detection, healthcare, military, and habitat monitoring. In all such application areas, gathering and then relaying captured data to a central unit is often considered the primary task of the network. Scientific analysis however often requires WSNs to capture and store variables for long periods of time. Storing and managing flows of data tend to be challenging issues because WSNs often consist of nodes with limited processing, memory, and power resources. Therefore the software layer in WSNs needs to implement an efficient data storage allocation mechanism in order to provide sufficient memory space for multiple applications. In this paper we propose a novel statistical approach for estimating applications storage requirements. Our proposed mechanism has been originally developed and implemented in a new WSN middleware called Sensomax, which is an agent-based decentralized middleware with multiple concurrent applications support for dynamic data gathering in WSNs. The mechanism described here proved to be an effective technique for proactively allocating memory to multiple applications with different operational paradigms.

Out‐of‐Core GPU‐based Change Detection in Massive 3D Point Clouds

AbstractIf sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out‐of‐core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point‐based rendering technique adapted for attributed 3D point clouds, to enable effective out‐of‐core real‐time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.

DCC: A Replacement Strategy for DBT System Based on Working Sets

Limited memory resource has always been deemed as the major bottleneck of the program performance, not excepting for dynamic binary translation (DBT) [1] system. However, traditional methods seldom enable this issue mentioned to be solved better due to a fix cache size routinely assigned for codes without considering the program behavior on the fly. Though some prediction methods implemented via LRU [2] histograms can achieve better performance in Operating System, unlike pages in memory, blocks in DBT have unfixed sizes each other, making the prediction imprecise. In this paper, we present a new replace policy named DCC (Dynamic Code Cache) based on working set [3] for bounded code cache, which would dynamically change the size of the code cache and clear the cache according to the information of working set detected. It would sacrifice a small part of the performance to save the space, especially when many great or complicated applications are running on the same physical machine, and the performance might be better under this condition. For Evaluating, we test this strategy on a DBT system named Crossbit [4].We get around 30% space saved up with about 12% running time sacrificed.

TeraStitcher - A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images

BackgroundFurther advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data.ResultsWe propose a free and fully automated 3D Stitching tool designed to match the special requirements coming out of teravoxel-sized tiled microscopy images that is able to stitch them in a reasonable time even on workstations with limited resources. The tool was tested on teravoxel-sized whole mouse brain images with micrometer resolution and it was also compared with the state-of-the-art stitching tools on megavoxel-sized publicy available datasets. This comparison confirmed that the solutions we adopted are suited for stitching very large images and also perform well on datasets with different characteristics. Indeed, some of the algorithms embedded in other stitching tools could be easily integrated in our framework if they turned out to be more effective on other classes of images. To this purpose, we designed a software architecture which separates the strategies that use efficiently memory resources from the algorithms which may depend on the characteristics of the acquired images.ConclusionsTeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (<8 GB) and processing power. It exploits the knowledge of approximate tile positions and uses ad-hoc strategies and algorithms designed for such very large datasets. The produced images can be saved into a multiresolution representation to be efficiently retrieved and processed. We provide TeraStitcher both as standalone application and as plugin of the free software Vaa3D.

Guided self-management of transient information in animations through pacing and sequencing strategies

Learning with instructional animations may overstretch limited working memory resources due to intense processing demands associated with transient information. The authors investigated whether explicit instructional advice coupled with a task-specific learner control mechanism (such as a timeline scrollbar) could facilitate the successful self-management of transient information. The effectiveness of a timeline scrollbar that allowed self-pacing and self-sequencing of animations was compared with computer-controlled animations. Experiment 1 demonstrated that a timeline scrollbar (with instructional advice on its strategic use) enhanced the retention of stroke sequences in writing Chinese characters. In Experiment 2, a timeline scrollbar was used in an integrated set of narrated animations dealing with complex scientific information. Retention and comprehension post-tests indicated that although a scrollbar accompanied by instructional advice in its use assisted novice learners, no such effect was found with participants who possessed higher levels of prior knowledge. The findings have implications for the formulation of criteria for the effective incorporation of learner control into the design of instructional animations.

Cooperative Localization and Tracking of Mobile Ad Hoc Networks

Cooperative localization and tracking technique relies on pairwise measurements to jointly estimate the positions and the velocities of multiple nodes in a mobile ad hoc network. The pairwise measurements include the range and the radial velocity between the transmitting and receiving nodes. For a large-scale network, we formulate the state-space models for the subsystems and develop the distributed extended Kalman filters for cooperative localization and tracking. The decentralized approach takes into account the limited resources of node memory, embedded computation, and communication bandwidth. The algorithm works well in a sparsely connected mobile network and can adapt to changes in network connectivity. Numerical results show that the performances of the decentralized cooperative localization and node velocity estimation are close to the posterior Cramer-Rao lower bounds of the centralized approach.

COVRA: A compression‐domain output‐sensitive volume rendering architecture based on a sparse representation of voxel blocks

AbstractWe present a novel multiresolution compression‐domain GPU volume rendering architecture designed for interactive local and networked exploration of rectilinear scalar volumes on commodity platforms. In our approach, the volume is decomposed into a multiresolution hierarchy of bricks. Each brick is further subdivided into smaller blocks, which are compactly described by sparse linear combinations of prototype blocks stored in an overcomplete dictionary. The dictionary is learned, using limited computational and memory resources, by applying the K‐SVD algorithm to a re‐weighted non‐uniformly sampled subset of the input volume, harnessing the recently introduced method of coresets. The result is a scalable high quality coding scheme, which allows very large volumes to be compressed off‐line and then decompressed on‐demand during real‐time GPU‐accelerated rendering. Volumetric information can be maintained in compressed format through all the rendering pipeline. In order to efficiently support high quality filtering and shading, a specialized real‐time renderer closely coordinates decompression with rendering, combining at each frame images produced by raycasting selectively decompressed portions of the current view‐ and transfer‐function‐dependent working set. The quality and performance of our approach is demonstrated on massive static and time‐varying datasets.

Flash-aware linux swap system for portable consumer electronics

Because of limited memory resource of portable consumer electronics, portable consumer electronics currently exploit swap space with flash memory as a cost effective solution to extend limited memory space. Original swap space management scheme used in Linux for magnetic disk is not available to flash memory-based swap space due to the distinct characteristics of flash memory. In this paper, we propose a flash-aware Linux swap system, called FLSS, which adopts Linux kernel 2.6 to manage flash memory-based swap space directly without FTL. We introduce: 1) a partial block alignment scheme to perform an efficient swap-in read-ahead algorithm, 2) a swap-aware victim block selection method and the redefined concept of hot page and cold page to design a swap-aware garbage collection policy called SACATA, and 3) the notion of overage as well as the notion of frozen applied to blocks to exploit a wear leveling-aware block management scheme. Experimental results show that the proposed FLSS greatly outperforms existing swap space management techniques and evaluate the effectiveness of proposed SACATA.

Elaborated Intrusion Theory: A Cognitive-Emotional Theory of Food Craving

A clear understanding of the cognitive-emotional processes underpinning desires to overconsume foods and adopt sedentary lifestyles can inform the development of more effective interventions to promote healthy eating and physical activity. The Elaborated Intrusion Theory of Desires offers a framework that can help in this endeavor through its emphases on the roles of intrusive thoughts and elaboration of multisensory imagery. There is now substantial evidence that tasks that compete for limited working memory resources with food-related imagery can reduce desires to eat that food, and that positive imagery can promote functional behavior. Meditation mindfulness can also short-circuit elaboration of dysfunctional cognition. Functional Decision Making is an approach that applies laboratory-based research on desire, to provide a motivational intervention to establish and entrench behavior changes, so healthy eating and physical activity become everyday habits.

Trie shifting scheme with depth adjusting for multiple virtual routers

In network virtualisation, which enables multiple virtual routers to share one physical router, how to ensure the scalability and performance of concurrent virtual routers is a challenging problem in virtual router design. Since physical router only has limited memory resource, it is significant to efficiently store multiple forwarding tables for virtual routers. Motivated by the idea of diminishing the dissimilarities in forwarding tables, the authors propose a novel trie shifting scheme with depth adjusting, and finally obtain a memory-efficient shared trie for all forwarding tables. In this scheme, by diminishing the dissimilarity in the depth and in the shape, memory is saved by reducing the trie nodes needed in the shared trie. In the simulation, the scheme needs only 10% of trie nodes, compared with storing forwarding tables separately. Extensive simulation based on three sets of forwarding tables, which are collected from five backbone routers, shows that the scheme saves memory by reducing the number of trie nodes between 4.7 and 7.8%, compared with the latest scheme in related work. Moreover, this scheme achieves more improvement when the dissimilarity is increasing.

Energy Efficient Routing in Wireless Sensor Network

Wireless sensor networks can provide a low cost solution to variety of real-world problems. Since sensor nodes are small and energy-constrained, they possess severely limited computational capabilities and memory resources, Energy efficiency is an essential aspect in the applications of WSN. Clustering is an effective topology control approach to improve energy efficiency. Data gathering also improve energy efficiency to prolong the network lifetime. A minimum connected dominating set (MCDS) can be used as a virtual backbone for efficient routing and broadcasting in WSN. Construction of the minimum connected dominating set is based on the construction of a maximal independent set (MIS). In this paper there is a framework of three phases, in first phase an algorithm is proposed for finding minimum connected dominating set using maximal independent set .In second phase we propose an algorithm for data gathering by Leader selection and in third phase we try to minimize the data transmission power consumption among the sensor nodes.

An Efficient and Secure Key Management Scheme for Hierarchical Wireless Sensor Networks

 Abstract—Key management in wireless sensor network is a complex task due to its nature of environment. Wireless sensor network comprise of large number of sensor nodes with different hardware abilities and functions. Due to the limited memory resources and energy constraints, complex security algorithms cannot be used in sensor networks. Therefore, an energy efficient key management scheme is necessary to mitigate the security risks. In this paper, we present an Efficient and Secure Key Management Scheme for Hierarchical Wireless Sensor Network (ESKMS). The proposed technique distributes the keys within a cluster efficiently and updates the pre-deployed keys to mitigate the node compromise attack. We also provide a detailed security analysis of our ESKMS protocol and show its advantages in avoiding different type of attacks from malicious nodes. Finally, using NS-2 simulator, the results shows that ESKMS is more energy efficient and provides a longer network lifetime compared to the existing key management schemes.