Hewlett-Packard

About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Substrate (printing). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.

Universally accessible healthcare devices with on the fly generation of HTML files

Abstract: A universally accessible healthcare device having a communication path and a server. The healthcare device generates a set of medical information and the server provides access to the medical information using an open standard network protocol on the communication path. HTML Files may be generated on the fly by the server in response to an HTTP command from a requesting web client.

Memristors for Energy-Efficient New Computing Paradigms

Abstract: In this Review, memristors are examined from the frameworks of both von Neumann and neuromorphic computing architectures. For the former, a new logic computational process based on the material implication is discussed. It consists of several memristors which play roles of combined logic processor and memory, called stateful logic circuit. In this circuit configuration, the logic process flows primarily along a time dimension, whereas in current von Neumann computers it occurs along a spatial dimension. In the stateful logic computation scheme, the energy required for the data transfer between the logic and memory chips can be saved. The non-volatile memory in this circuit also saves the energy required for the data refresh. Neuromorphic (cognitive) computing refers to a computing paradigm that mimics the human brain. Currently, the neuromorphic or cognitive computing mainly relies on the software emulation of several brain functionalities, such as image and voice recognition utilizing the recently highlighted deep learning algorithm. However, the human brain typically consumes ≈10–20 Watts for selected “human-like” tasks, which can be currently mimicked by a supercomputer with power consumption of several tens of kilo- to megawatts. Therefore, hardware implementation of such brain functionality must be eventually sought for power-efficient computation. Several fundamental ideas for utilizing the memristors and their recent progresses in these regards are reviewed. Finally, material and processing issues are dealt with, which is followed by the conclusion and outlook of the field. These technical improvements will substantially decrease the energy consumption for futuristic information technology.

Nonuniformity correction of infrared image sequences using the constant-statistics constraint

Abstract: Using clues from neurobiological adaptation, we have developed the constant-statistics (CS) algorithm for nonuniformity correction of infrared focal point arrays (IRFPAs) and other imaging arrays. The CS model provides an efficient implementation that can also eliminate much of the ghosting artifact that plagues all scene-based nonuniformity correction (NUC) algorithms. The CS algorithm with deghosting is demonstrated on synthetic and real infrared (IR) sequences and shown to improve the overall accuracy of the correction procedure.

Disposable ink jet head

Abstract: A thermal ink jet head is disclosed in which the jetting resistors, fluid interconnections, ink reservoir, electrical connections, and jetting orifices are fully integrated to provide an inexpensive, disposable jetting head. The entire hydraulic ink system is sealed to eliminate user interaction with the liquid ink, and ink can only exit the head via the jetting orifices under the influence of the jetting resistors. Once the ink is expended the user disposes with the old head and installs a new one by breaking and making a simple mechanical and low voltage electrical connection.

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Abstract: Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.