13241097
Dictionary based code compression for variable length instruction encodings
Most of the work done in the field of machine code compression is for fixed length instruction encodings. In this work we apply code compression on variable length instruction set processors whose encodings are already optimized to a certain extent with respect to their usages. We develop a dictionary based algorithm which utilizes unused encoding space of an instruction set architecture to encode code-words, and addresses issues arising out of variable length instructions. We test the algorithm with a RISC processor and include results for compression and performance in terms of cycle-counts and memory accesses respectively. We also present an efficient scheme for searching relocated branch addresses and analyze its performance.
5790802
Object-oriented symbol management in syntax-directed compiler systems
Each symbol in a source program has a series of attributes that govern the operations performed on it. In this paper, we explore object-oriented techniques for representing the attributes of symbols and translating the operations performed on them. This approach is shown superior in memory and run-time speed to the procedural style traditionally used for writing compilers. We demonstrate object-oriented symbol management in a compiler for an arithmetic subset of C. We have used C++ --- a single-dispatching language --- to implement the same, but we emphasize the need for multiple dispatching to develop programs in a heavily-object-oriented style.
24684227
Adaptive Control of Anytime Algorithm Parameters
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.
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.
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.
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.
7841699
Dynamic data fusion for future sensor networks
DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.
14410544
Fluid modeling of pollution proliferation in P2P networks
P2P systems are highly vulnerable to pollution attacks in which attackers inject multiple versions of corrupted content into the system, which is then further proliferated by unsuspecting users. However, to our knowledge, there are no closed-form solutions that describe this phenomenon, nor are there models that describe how the injection of multiple versions of corrupted content impacts a clients' ability to receive a valid copy. In this paper we develop a suite of fluid models that model pollution proliferation in P2P systems. These fluid models lead to systems of non-linear differential equations. We obtain closed-form solutions for the differential equations; for the remaining models, we efficiently solve the differential equations numerically. The models capture a variety of user behaviors, including propensity for popular versions, abandonment after repeated failure to obtain a good version, freeloading, and local version blacklisting. Our analysis reveals intelligent strategies for attackers as well as strategies for clients seeking to recover non-polluted content within large-scale P2P networks.
9633354
Real-time personalized sports video filtering and summarization
We demonstrate a real-time fully automated software system for filtering important events in sports video. Events represent occurrences of actions or state changes in video content. In the current prototype, we demonstrate detection of pitching in baseball and serving in tennis. For wireless video applications, we propose and apply a unique notion of content-based adaptive streaming,in which video encoding rate and media modality is dynamically varied according to the event filtering results. Our system includes an event detection module, an adaptive encoding module, and a buffer management module for adaptive streaming. We achieve the real-time performance by exploring compressed-domain techniques and multi-stage multi-resolution content-analysis processes.
960483
Selecting devices for aggregation
As intelligent devices become affordable and wireless infrastructure becomes pervasive, the potential to combine, or aggregate, device functionality to provide a user with a better experience grows. Often, there will be multiple devices providing similar functionality that the user will have to choose from for the aggregation. We present the design and prototype implementation of a system for automatic selection of devices for aggregation in a dynamic environment. It allows a user to express trade-offs between the quality of device attributes, user distraction, and aggregation stability. This approach enables a user to have a richer experience without having to constantly worry about the device and the environment details.