Implementing primal-dual network flow algorithms

"Most of the results of this report were presented at the National ORSA/TIMS meeting, Chicago, May 1975." "7-113-77." Cover title.Bibliography: p. 35-38.Supported in part by the U.S. Deaprtment of Transportation, Transportation Advanced Research Program (TARP) contract no. DOT-TSC-1058 Supported in part by the Office of Naval Research under contract. N00014-75-C-0556by H. A. [i.e. Z] Aashtiani and T. L. Magnanti

Short-term generation scheduling in a hydrothermal power system.

SIGLEAvailable from British Library Document Supply Centre- DSC:D173872 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Network Flows

Not Availabl

Relaxation methods for linear programs

Bibliography: p. 44-45.National Science Foundation grant NSF-ECS-3217668by Paul Tseng, Dimitri P. Bertsekas

Efficient revocation and Data Retrieval in Cloud Storage using CP-ABE

As cloud computing ends up common, more encrypted data are being incorporated into the cloud. For the insurance of data protection, encrypted data more often than not need to be scrambled before redistributing, which makes powerful data usage an extremely difficult assignment. In this paper, we propose another technique to empower successful fuzzy keyword seek in a multi-client framework over encoded cloud data while keeping up catchphrase security. In this new framework, differential looking benefits are bolstered, which is accomplished with the strategy of characteristic based encryption. Data proprietors are inspired to store their profitable data in the cloud, now the issue emerges how to ensure significant data against assaults and dangers. In this paper we are presenting secure Attribute Based Encryption for ensuring profitable data and further quicker looking and getting to of encoded data dependent on programmed comment based seeking method

Intelligent Query Answering Through Rule Learning and Generalization

The Department of Defense (DoD) relies heavily on information systems to complete a myriad of tasks, from day-to-day personnel actions to mission critical imagery retrieval, intelligence analysis, and mission planning. The astronomical growth in size and performance of data storage systems leads to problems in processing the amount of data returned on any given query. Typical relational database systems return a set of unordered records. This approach is acceptable in small information systems, but in large systems, such as military image retrieval systems with more than 1 million records, it requires considerable time (often hours to days) to sort through thousands of records and select the relevant for analysis. This research introduces Intelligent Query Answering (IQA) as a novel approach to information retrieval. IQA implements the FOIL algorithm to learn rules based upon user feedback QUI90. The Winnow algorithm adjusts rule weights based on user classification, for improved document orderings BLU97. A semantic tree specific to the domain allows rule generalization across the domain. Testing shows a document sort accuracy rate of 63-93% against a controlled test dataset and 78-89% accuracy rate on a subset of declassified National Air Intelligence Center imagery metadata. These results demonstrate that this research provides groundwork for future efforts in rule learning and rule generalization in the information retrieval field

Low Latency Displays for Augmented Reality

The primary goal for Augmented Reality (AR) is bringing the real and virtual together into a common space. Maintaining this illusion, however, requires preserving spatially and temporally consistent registration despite changes in user or object pose. The greatest source of registration error is latency—the delay between when something moves and the display changes in response—which breaks temporal consistency. Furthermore, the real world varies greatly in brightness; ranging from bright sunlight to deep shadows. Thus, a compelling AR system must also support High-Dynamic Range (HDR) to maintain its virtual objects’ appearance both spatially and temporally consistent with the real world. This dissertation presents new methods, implementations, results (both visual and performance), and future steps for low latency displays, primarily in the context of Optical See-through Augmented Reality (OST-AR) Head-Mounted Displays, focusing on temporal consistency in registration, HDR color support, and spatial and temporal consistency in brightness: 1. For registration temporal consistency, the primary insight is breaking the conventional display paradigm: computers render imagery, frame by frame, and then transmit it to the display for emission. Instead, the display must also contribute towards rendering by performing a post-rendering, post-transmission warp of the computer-supplied imagery in the display hardware. By compensating in the display for system latency by using the latest tracking information, much of the latency can be short-circuited. Furthermore, the low latency display must support ultra-high frequency (multiple kHz) refreshing to minimize pose displacement between updates. 2. For HDR color support, the primary insight is developing new display modulation techniques. DMDs, a type of ultra-high frequency display, emit binary output, which require modulation to produce multiple brightness levels. Conventional modulation breaks low latency guarantees, and modulation of bright LEDs illuminators at frequencies to support kHz-order HDR exceeds their capabilities. Thus one must directly synthesize the necessary variation in brightness. 3. For spatial and temporal brightness consistency, the primary insight is integrating HDR light sensors into the display hardware: the same processes which both compensate for latency and generate HDR output can also modify it in response to the spatially sensed brightness of the real world.Doctor of Philosoph