A system for controlling use and distribution of digital works, in which the owner
of a digital work (101) attaches usage rights (102) to that work. Usage rights are granted by the
"owner" of a digital work to "buyers" of the digital work. The usage rights define how a
digital work may be used and further distributed by the buyer. Each right has associated with it
certain optional specifications which outline the conditions and fees upon which the right may
be exercised. Digital works are stored in a repository. A repository will process each request
(103,104) to access a digital work by examining the corresponding usage rights (105). Digital
work playback devices, coupled to the repository containing the work, are used to play, display
or print the work. Access to digital works for the purposes of transporting between
repositories (e.g. copying, borrowing or transfer) is carried out using a digital work transport
protocol. Access to digital works for the purposes of replay by a digital work playback
device(e.g. printing, displaying or executing) is carried out using a digital work playback
protocol. Access is denied (106) or granted (107) depending whether the requesting repository
has the required usage rights.

System for controlling the distribution and use of digital works

A system for controlling the distribution and use of digital works using digital tickets. In the present invention, a “digital ticket” is used to entitle the ticket holder to exercise some usage right with respect to a digital work. Usage rights are used to define how a digital work may be used or distributed. Each usage right may specify a digital ticket which must be present before the right may be exercised. Digital works are stored in repositories which enforce a digital works usage rights. Each repository has a “generic ticket agent” which punches tickets. In some instances only the generic ticket agent is necessary. In other instances, punching by a “special ticket agent” residing on another repository may be needed.

System for controlling the distribution and use of digital works using digital tickets

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

A trusted rendering system for use in a system for controlling the distribution and use of digital works. A trusted rendering system facilitates the protection of rendered digital works which have been rendered on a system which controls the distribution and use of digital works through the use of dynamically generated watermark information that is embedded in the rendered output. The watermark data typically provides information relating to the owner of the digital work, the rights associated with the rendered copy of the digital work and when and where the digital work was rendered. This information will typically aid in deterring or preventing unauthorized copying of the rendered work to be made. The system for controlling distribution and use of digital works provides for attaching persistent usage rights to a digital work. Digital works are transferred between repositories which are used to request and grant access to digital works. Such repositories are also coupled to credit servers which provide for payment of any fees incurred as a result of accessing a digital work.

System for controlling the distribution and use of rendered digital works through watermarking

Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named "HASC Challenge" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named "HASC Tool" for management, evaluation and collection of the large number of activity sensor data.

HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings

Graphics processing units (GPUs) embrace many-core compute devices where massively parallel compute threads are offloaded from CPUs. This heterogeneous nature of GPU computing raises non-trivial data transfer problems especially against latency-critical real-time systems. However even the basic characteristics of data transfers associated with GPU computing are not well studied in the literature. In this paper, we investigate and characterize currently-achievable data transfer methods of cutting-edge GPU technology. We implement these methods using open-source software to compare their performance and latency for real-world systems. Our experimental results show that the hardware-assisted direct memory access (DMA) and the I/O read-and-write access methods are usually the most effective, while on-chip micro controllers inside the GPU are useful in terms of reducing the data transfer latency for concurrent multiple data streams. We also disclose that CPU priorities can protect the performance of GPU data transfers.

Data Transfer Matters for GPU Computing

A DES decrypting unit decrypts video data frames encrypted and compressed based on MPEG standards, when receiving them. The decrypted video data frames are expanded one by one in a MPEG expansion circuit. Each time the single frame is expanded, the MPEG expansion circuit outputs a completion-of-frame-expansion signal to a host control CPU. The host control CPU counts the completion-of-frame-expansion signals and reduces an accounting count value in an accounting data memory in accordance with this count value. The host control CPU output character pattern image data from a pattern generator when the accounting count value becomes 0. The character pattern image data is superimposed on a video signal in an adder circuit.

Apparatus for managing software using quantity

The authors have been developing the system, which analyzes pedestrian flow using Wi-Fi packet sensors. The sensors collect Wi-Fi packet called probe request packet, which is transmitted from a smartphone to search Wi-Fi access points. In addition, the cloud storage server is running to manage observed packets centrally and to compute pedestrian flow in real time. Additionally, user movement history is vitally important and we have to pay close attention to handling that kind of data. Therefore, the system runs with an anonymization method and a cryptographic function. Some kinds of demonstration experiments were held in real environment. As a result, it was confirmed that we can analyze the rough tendency of pedestrian flow using the present system and simple analysis methods.

A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.

/pdf/hasc2011corpus-towards-the-common-ground-of-human-activity-1t3iod3mx1.pdf

Nobuhiko Nishio

Papers

HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings

Data Transfer Matters for GPU Computing

Apparatus for managing software using quantity

A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment

HASC2011corpus: towards the common ground of human activity recognition