20900631
ANCHOR - A Connectionist Architecture for Partitioning Feature Spaces and Hierarchical Nesting of Neural Nets
We present a novel connectionist architecture for handling arbitrarily complex neural computations. The architecture — which we call an Artificial Neural Network Compiler for Hierarchical ORganisation (ANCHOR) — facilitates network hierarchy and the partitioning of the input space into a number of small, simpler sub-mappings. This strategy is a mix of both a modular approach for reduction of the problem complexity, and an ensemble-based approach for increasing prediction accuracy. ANCHOR is implemented around the concept of a Superneuron which is a generalised view of a neuron-processing element; a superneuron is a single (or higher-order) perceptron which can be trained individually with the support of multiple learning algorithms. The indistinguishability between a superneuron, and a neuron is employed in hierarchical nesting of superneurons, up to (theoretically) infinite depth, within other superneurons as well as linear or tree-structured cascading. Hierarchical decomposition of simple boolean functions has been demonstrated as proof-of-concept. It is argued that this strategy of data-partitioning for subsequent mapping onto a hierarchical classifier reduces the learning complexity and offers better generalisation.
24684227
Adaptive Control of Anytime Algorithm Parameters
16795084
Analysis of a Multiobjective Evolutionary Algorithm on the 0-1 knapsack problem
Multiobjective Evolutionary Algorithms (MOEAs) are increasingly being used for effectively solving many real-world problems, and many empirical results are available. However, theoretical analysis is limited to a few simple toy functions. In this work, we select the well-known knapsack problem for the analysis. The multiobjective knapsack problem in its general form is NP-complete. Moreover, the size of the set of Pareto-optimal solutions can grow exponentially with the number of items in the knapsack. Thus, we formalize a (1 + e)-approximate set of the knapsack problem and attempt to present a rigorous running time analysis of a MOEA to obtain the formalized set. The algorithm used in the paper is based on a restricted mating pool with a separate archive to store the remaining population; we call the algorithm a Restricted Evolutionary Multiobjective Optimizer (REMO). We also analyze the running time of REMO on a special bi-objective linear function, known as LOTZ (Leading Ones : Trailing Zeros), whose Pareto set is shown to be a subset of the knapsack. An extension of the analysis to the Simple Evolutionary Multiobjective Optimizer (SEMO) is also presented. A strategy based on partitioning of the decision space into fitness layers is used for the analysis.
537233
Evolutionary Multimodal Optimization Revisited
We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computational effort.
6342150
Frame based fair multiprocessor scheduler: a fast fair algorithm for real-time embedded systems
This paper presents frame based fair multiprocessor scheduler (FBFMS) which provides accurate real-time proportional fair scheduling for a set of dynamic tasks on a symmetric multiprocessor environment with O(1) scheduling overhead. FBFMS meets these seemingly contradictory goals by applying the benefits of virtual time round-robin scheduling mechanism along with a frame based scheduling approach. Simulation results show that the algorithm provides 2.5 to 26 times speedup (over O(lg n) complexity schedulers) with distortion in fairness less than 1% in most cases, and less than 15% in the worst case.
39741624
An Efficient Technique for Dynamic Slicing of Concurrent Java Programs
Program slice has many applications such as program debugging, testing, maintenance and complexity measurement. We propose a new dynamic program slicing technique for concurrent Java programs that is more efficient than the related algorithms. We introduce the notion of Concurrent Program Dependence Graph (CPDG). Our algorithm uses CPDG as the intermediate representation and is based on marking and unmarking the edges in the CPDG as and when the dependencies arise and cease during run-time. Our approach eliminates the use of trace files and is more efficient than the existing algorithms.
5316084
On Improving Wireless Broadcast Reliability of Sensor Networks Using Erasure Codes
Efficient and reliable dissemination of information over a large area is a critical ability of a sensor network for various reasons such as software updates and transferring large data objects (e.g., surveillance images). Thus efficiency of wireless broadcast is an important aspect of sensor network deployment. In this paper, we study FBcast, a new broadcast protocol based on the principles of modern erasure codes. We show that our approach provides high reliability, often considered critical for disseminating codes. In addition FBcast offers limited data confidentiality. For a large network, where every node may not be reachable by the source, we extend FBcast with the idea of repeaters to improve reliable coverage. Simulation results on TOSSIM show that FBcast offers higher reliability with lower number of retransmissions than traditional broadcasts.
6817196
Design of Adaptive Overlays for Multi-scale Communication in Sensor Networks
In wireless sensor networks, energy and communication bandwidth are precious resources. Traditionally, layering has been used as a design principle for network stacks; hence routing protocols assume no knowledge of the application behavior in the sensor node. In resource-constrained sensor-nodes, there is simultaneously a need and an opportunity to optimize the protocol to match the application. In this paper, we design a network architecture that efficiently supports multi-scale communication and collaboration among sensors. The architecture complements the previously proposed Abstract Regions architecture for local communication and collaboration. We design a self-organizing hierarchical overlay that scales to a large number of sensors and enables multi-resolution collaboration. We design effective Network Programming Interfaces to simplify the development of applications on top of the architecture; these interfaces are efficiently implemented in the network layer. The overlay hierarchy can adapt to match the collaboration requirements of the application and data both temporally and spatially. We present an initial evaluation of our design under simulation to show that it leads to reduced communication overhead, thereby saving energy. We are currently building our architecture in the TinyOS environment to demonstrate its effectiveness.
47199334
Computation Hierarchy for In-Network Processing
In this paper we explore the network level architecture of distributed sensor systems that perform in-network processing. We propose a system with heterogeneous nodes that organizes into a hierarchical structure dictated by the computational capabilities. The presence of high-performance nodes amongst a sea of resource-constrained nodes exposes new tradeoffs for the efficient implementation of network-wide applications. Our experiments show that even for a low relative density of resource-constrained nodes to high-performance nodes there are certain gains in performance for a heterogeneous and hierarchical network over a homogeneous one. The introduction of hierarchy enables partitioning of the application into sub-tasks that can be mapped onto the heterogeneous nodes in the network in multiple ways. We analyze the tradeoffs between the execution time of the application, accuracy of the output produced and the overall energy consumption of the network for the different mapping of the sub-tasks onto the heterogeneous nodes. We evaluate the performance and energy consumption of a typical sensor network application of target tracking via beamforming and line of bearing (LOB) calculations on the different nodes. Our experiments also include the study of the overall performance and energy consumption of the LOB calculation using two different types of resource constrained sensor nodes (MICA and MICA2 nodes) and show how these metrics are affected by changes in the node architecture and operation. Our results indicate that when using MICA motes as resource-constrained nodes, 85% of the time on average the hierarchical network outperforms a homogeneous network for approximately the same energy budget. When using MICA2 motes as resource-constrained nodes, 54% of the time the hierarchical network performs better than a homogeneous network with approximately the same energy budget.
15931484
Optimal slope bin classification in gradient adjusted predictor for lossless compression of medical images
Gradient adjusted predictor (GAP) uses seven fixed range of slope quantization bins and different predictors associated with each bin, for prediction of pixels of all kinds of images. Criteria for range of slope in the bins and associated predictors are not reported in the literature. This paper presents a technique for slope quantization bins which are optimum for a given set of images. It also presents a technique for finding a statistically optimal predictor for a given range of slope bin. Simulation results, for medical images, using optimal slope bins and associated predictors show a significant better compression performance as compared to the other methods such as GAP and edge-directed prediction (EDP) method. The proposed method and GAP has same order of computational complexity while EDP is computationally much expensive.
2162080
Harbor: software-based memory protection for sensor nodes
Many sensor nodes contain resource constrained microcontrollers where user level applications, operating system components, and device drivers share a single address space with no form of hardware memory protection. Programming errors in one application can easily corrupt the state of the operating system or other applications. In this paper, we propose Harbor, a memory protection system that prevents many forms of memory corruption. We use software based fault isolation ("sandboxing") to restrict application memory accesses and control flow to protection domains within the address space. A flexible and efficient memory map data structure records ownership and layout information for memory regions; writes are validated using the memory map. Control flow integrity is preserved by maintaining a safe stack that stores return addresses in a protected memory region. Run-time checks validate computed control flow instructions. Cross domain calls perform low-overhead control transfers between domains. Checks are introduced by rewriting an application's compiled binary. The sand-boxed result is verified on the sensor node before it is admitted for execution. Harbor's fault isolation properties depend only on the correctness of this verifier and the Harbor runtime. We have implemented and tested Harbor on the SOS operating system. Harbor detected and prevented memory corruption caused by programming errors in application modules that had been in use for several months. Harbor's overhead, though high, is less than that of application-specific virtual machines, and reasonable for typical sensor workloads.