Mitsubishi Electric

About: Mitsubishi Electric is a company organization based out in Ratingen, Germany. It is known for research contribution in the topics: Signal & Voltage. The organization has 23024 authors who have published 27591 publications receiving 255671 citations. The organization is also known as: Mitsubishi Electric Corporation & Mitsubishi Denki K.K..

CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Abstract: Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. Source code and trained models are available online at https://bitbucket.org/columbiadvmm/cdc.

Voice puppetry

Abstract: We introduce a method for predicting a control signal from another related signal, and apply it to voice puppetry: Generating full facial animation from expressive information in an audio track. The voice puppet learns a facial control model from computer vision of real facial behavior, automatically incorporating vocal and facial dynamics such as co-articulation. Animation is produced by using audio to drive the model, which induces a probability distribution over the manifold of possible facial motions. We present a lineartime closed-form solution for the most probable trajectory over this manifold. The output is a series of facial control parameters, suitable for driving many different kinds of animation ranging from video-realistic image warps to 3D cartoon characters. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation; I.2.9 [Artificial Intelligence]: Robotics—Kinematics and Dynamics; I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Time-varying images; G.3 [Mathematics of Computing]: Probability and Statistics—Time series analysis; E.4 [Data]: Coding and Information Theory—Data compaction and compression; J.5 [Computer Applications]: Arts and Humanities—Performing Arts

Asymptotic behavior of nonlinear networked control systems

Abstract: The defining characteristic of a networked control system (NCS) is having a feedback loop that passes through a local area computer network. Our two-step design approach includes using standard control methodologies and choosing the network protocol and bandwidth in order to ensure important closed-loop properties are preserved when a computer network is inserted into the feedback loop. For sufficiently high data rates, global exponential stability is preserved. Simulations are included to demonstrate the theoretical result.

The VolumePro real-time ray-casting system

Abstract: This paper describes VolumePro, the world’s first single-chip realtime volume rendering system for consumer PCs. VolumePro implements ray-casting with parallel slice-by-slice processing. Our discussion of the architecture focuses mainly on the rendering pipeline and the memory organization. VolumePro has hardware for gradient estimation, classification, and per-sample Phong illumination. The system does not perform any pre-processing and makes parameter adjustments and changes to the volume data immediately visible. We describe several advanced features of VolumePro, such as gradient magnitude modulation of opacity and illumination, supersampling, cropping and cut planes. The system renders 500 million interpolated, Phong illuminated, composited samples per second. This is sufficient to render volumes with up to 16 million voxels (e.g., 256) at 30 frames per second. CR Categories: B.4.2 [Hardware]: Input/Output and Data Communications—Input/Output DevicesImage display; C.3 [Computer Systems Organization]: Special-Purpose and ApplicationBased Systems—Real-time and embedded systems; I.3.1 [Computer Graphics]: Hardware Architecture—Graphics processor;

A method for solving key equation for decoding goppa codes

Abstract: In this paper we show that the key equation for decoding Goppa codes can be solved using Euclid's algorithm. The division for computing the greatest common divisor of the Goppa polynomial g(z) of degree 2t and the syndrome polynomial is stopped when the degree of the remainder polynomial is less than or equal to t − 1. The error locator polynomial is proved the multiplier polynomial for the syndrome polynomial multiplied by an appropriate scalar factor. The error evaluator polynomial is proved the remainder polynomial multiplied by an appropriate scalar factor. We will show that the Euclid's algorithm can be modified to eliminate multiplicative inversion, and we will evaluate the complexity of the inversionless algorithm by the number of memories and the number of multiplications of elements in GF(qm). The complexity of the method for solving the key equation for decoding Goppa codes is a few times as much as that of the Berlekamp—Massey algorithm for BCH codes modified by Burton. However the method is straightforward and can be applied for solving the key equation for any Goppa polynomial.