UTB SOI SRAM cell stability under the influence of intrinsic parameter fluctuation

Intrinsic parameter fluctuations steadily increases with CMOS technology scaling. Around the 90nm technology node, such fluctuations will eliminate much of the available noise margin in SRAM based on conventional MOSFETs. Ultra thin body (UTB) SOI MOSFETs are expected to replace conventional MOSFETs for integrated memory applications due to superior electrostatic integrity and better resistant to some of the sources of intrinsic parameter fluctuations. To fully realise the performance benefits of UTB SOI based SRAM cells a statistical circuit simulation methodology which can fully capture intrinsic parameter fluctuation information into the compact model is developed. The impact on 6T SRAM static noise margin characteristics of discrete random dopants in the source/drain regions and body-thickness variations has been investigated for well scaled devices with physical channel length in the range of 10nm to 5nm. A comparison with the behaviour of a 6T SRAM based on a conventional 35nm MOSFET is also presented

TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

Avoiding Stffness: Perspectives of agile technology diffusion

The increased pervasiveness of Information and Communication Technology (ICT) within the Architecture Engineering and Construction (AEC) sector, not only introduces unparalleled opportunities for enhancing the performance of design/engineering/construction processes per se, but also serves as a unique lever for improving and delivering overall competitiveness. However, whilst the onset and evolution of ICT keeps improving, it is also recognised that organisations often fail to match this evolution, most notably through the adoption, diffusion and dissemination of this technology. This has also been acknowledged as a barrier, particularly concerning innovation opportunities. Cognisant of this, organisations are increasingly looking to secure full advantage of emerging ICT developments. On this theme, this study identifies a series of priority areas for organisations, with the specific remit of securing agility (in the market) through ICT diffusion. A questionnaire, based on an Agile-Technology Diffusion framework, was used to capture the perceptions of management professionals working in the Turkish AEC sector. The ranking analysis of the survey results and comparison of the different management perceptions (levels) are presented for discussion. Research findings identify several priority areas that need to be addressed. These findings also uncover significant differences in the perceptions of different management levels - which can help decision makers appreciate the holistic interdependencies, especially the factors which impinge (or impede) overall competitiveness

The impact logic of mobile technology usage on job production

Research on mobile technologies has received an increasing attention. Most of the existing literature focuses on use of mobile technologies on a managerial level, with technology as a device for information and communication exchange. The impact potential and their corresponding functionalities at the worker level has not yet been analyzed. This study tries to address this gap. It is the key objective to develop a theoretical model how mobile technologies impact business processes in job production (construction industry) on the operational level. Thus, a generic model will be developed on the basis of existing literature, especially the concept of Task-Technology-Fit. It emphasizes how task complexity affects the required effort of individual information access, information capturing as well as the timeliness of information. These mediators will influence the utilization of information for resource planning and coordination which in turn will affect the performance of operational processes. Then, it will be deduced how mobile technologies affect the forces and relationships in this model. There exists a trade-off between the increasing effort to capture information and the reduced effort for accessing information. Moreover, the way how the captured information is utilized for status tracking, resource utilization and resource coordination is considered to be the key factor in improving operational process performance

A sensing platform for physiological and contextual feedback to tennis athletes

In this paper we describe our work on creating a multi-modal sensing platform for providing feedback to tennis coaches and players. The platform includes a fixed installation around a tennis court consisting of a video camera network and a localisation system as well as wearable sensing technology deployed to individual athletes. We describe the various components of this platform and explain how we can capture synchronised multi-modal sensor data streams for games or training sessions. We then describe the content-based retrieval system we are building to facilitate the development of novel coaching tools. We provide some examples of the queries that the system can support, where these queries are chosen to be suitably expressive so as to reflect a coach's complex information needs regarding tennis-related performance factors

Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio