16967659
Content-based adaptive compression of educational videos using phase correlation techniques
A serious bottleneck towards multimedia e-learning is the non-availability of required bandwidth to view the lecture videos at good resolution because of large size of lecture videos. Content-based compression of video data can greatly enhance the bandwidth utilization over scarce resource networks. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase-correlation-based motion estimation and compensation algorithm that assists in the compression of important moving objects in an efficient manner. Temporal coherence is exploited in a two-phase manner. First, the frames with high similarity are categorized and encoded efficiently. Second, the compression ratio is adapted according to the frame content. Shots that are of high importance are stored at a higher bit rate as compared to the frames of relatively low importance. The importance and priority of the frames is computed automatically by our algorithm. Results over several hours of educational videos and comparison with the state-of-the-art compression algorithms illustrate the high compression performance of our technique.
39123560
High Quality Compression of Educational Videos Using Content-Adaptive Framework
With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.
14308872
Criticality of detailed modeling in semiconductor supply chain simulation
Supply chain management offers a large potential for organizations to reduce costs and improve customer service performance. Simulation of supply chains can help in these objectives by evaluating the impact of alternative inventory control policies. Supply chain simulation involves the modeling of multiple factories across the chain and can get quite complex. Analysts typically carry out such simulation at a coarse level of detail to keep the complexity and computing resources manageable. However, modeling at coarse levels may reduce the accuracy of outputs and affect the quality of decisions. In this paper, we report on a study to compare the quality of results at different levels of detail in a semiconductor supply chain simulation.
47220301
Expert simulation for on-line scheduling
The state-of-the-art in manufacturing has moved toward flexibility, automation and integration. The efforts spent on bringing computer-integrated manufacturing (CIM) to plant floors have been motivated by the overall thrust to increase the speed of new products to market. One of the links in CIM is plant floor scheduling, which is concerned with efficiently orchestrating the plant floor to meet the customer demand and responding quickly to changes on the plant floor and changes in customer demand. The Expert System Scheduler (ESS) has been developed to address this link in CIM. The scheduler utilizes real-time plant information to generate plant floor schedules which honor the factory resource constraints while taking advantage of the flexibility of its components.
The scheduler uses heuristics developed by an experienced human factory scheduler for most of the decisions involved in scheduling. The expertise of the human scheduler has been built into the computerized version using the expert system approach of the discipline of artificial intelligence (AI). Deterministic simulation concepts have been used to develop the schedule and determine the decision points. As such, simulation modeling and AI techniques share many concepts, and the two disciplines can be used synergistically. Examples of some common concepts are the ability of entities to carry attributes and change dynamically (simulation—entities/attributes or transaction/parameters versus AI—frames/slots); the ability to control the flow of entities through a model of the system (simulation—conditional probabilities versus AI—production rules); and the ability to change the model based upon state variables (simulation—language constructs based on variables versus AI—pattern-invoked programs). Shannon [6] highlights similarities and differences between conventional simulation and an AI approach. Kusiak and Chen [3] report increasing use of simulation in development of expert systems.
ESS uses the synergy between AI techniques and simulation modeling to generate schedules for plant floors. Advanced concepts from each of the two areas are used in this endeavor. The expert system has been developed using frames and object-oriented coding which provides knowledge representation flexibility. The concept of “backward” simulation, similar to the AI concept of backward chaining, is used to construct the events in the schedule. Some portions of the schedule are constructed using forward or conventional simulation.
The implementation of expert systems and simulation concepts is intertwined in ESS. However, the application of the concepts from these two areas will be treated separately for ease of presentation. We will first discuss the expert system approach and provide a flavor of the heuristics. The concept of backward simulation and the motive behind it will then be explored along with some details of the implementation and the plant floor where the scheduler is currently being used. We will then highlight some advantages and disadvantages of using the expert simulation approach for scheduling, and, finally, the synergetic relationship between expert systems and simulation.
15265101
Banishing Robust Turing Completeness
This paper shows that “promise classes” are so fragilely structured that they do not robustly possess Turing-hard sets even in classes far larger than themselves. We show that FewP does not robustly possess Turing-hard sets for UP ∩ coUP and IP ∩ coIP does not robustly possess Turing-hard sets for ZPP. It follows that ZPP, R, coR, UP ∩ coUP, UP, FewP ∩ coFewP, FewP, and IP ∩ coIP do not robustly possess Turing complete sets. This both resolves open questions of whether promise classes lacking robust downward closure under Turing reductions (e.g., R, UP, FewP) might robustly have Turing complete sets, and extends the range of classes known not to robustly contain many-one complete sets.
28659602
An approximation to the greedy algorithm for differential compression
We present a new differential compression algorithm that combines the hash value techniques and suffix array techniques of previous work. The term "differential compression" refers to encoding a file (a version file) as a set of changes with respect to another file (a reference file). Previous differential compression algorithms can be shown empirically to run in linear time, but they have certain drawbacks; namely, they do not find the best matches for every offset of the version file. Our algorithm, hsadelta (hash suffix array delta), finds the best matches for every offset of the version file, with respect to a certain granularity and above a certain length threshold. The algorithm has two variations depending on how we choose the block size. We show that if the block size is kept fixed, the compression performance of the algorithm is similar to that of the greedy algorithm, without the associated expensive space and time requirements. If the block size is varied linearly with the reference file size, the algorithm can run in linear time and constant space. We also show empirically that the algorithm performs better than other state-of-the-art differential compression algorithms in terms of compression and is comparable in speed.
17470610
Load balancing for conservative simulation on shared memory multiprocessor systems
Load balancing is a crucial factor in achieving good performance for parallel discrete event simulations. We present a load balancing scheme that combines both static partitioning and dynamic load balancing. The static partitioning scheme maps simulation objects to logical processes before simulation starts while the dynamic load balancing scheme attempts to balance the load during runtime. The static scheme involves two steps. First, the simulation objects that contribute to small lookahead are merged together by using a merging algorithm. Then a partitioning algorithm is applied. The merging is needed to ensure a consistent performance for our dynamic scheme. Our dynamic scheme is tailor-made for an asynchronous simulation protocol that does not rely on null messages. The performance study on a supply chain simulation shows that the partitioning algorithm and dynamic load balancing are important in achieving good performance.