Full duplex reflection amplifier tag

The authors describe a reflection amplifier adapted to have both a reflection and a transmission port. The amplifier uses a single silicon bipolar transistor and demonstrates a reflection gain of 13 dB, transmission gain of 10 dB and 3.4 dB noise figure at 5.25 GHz. The added feature of transmission gain in the reflection amplifier permits practical implementation of full duplex microwave radiofrequency indentification (RFID) tag operation. By using a simple subcarrier modulation scheme full duplex RFID operation utilising this amplifier is demonstrated. These results indicate that for 27 dBm (0.5 W) effective isotropic radiated power (EIRP) transmit power it should be possible to obtain approximately 8 m downlink range and 25 m uplink range

An abstract annotation model for skeletons

Multi-core and many-core platforms are becoming increasingly heterogeneous and asymmetric. This significantly increases the porting and tuning effort required for parallel codes, which in turn often leads to a growing gap between peak machine power and actual application performance. In this work a first step toward the automated optimization of high level skeleton-based parallel code is discussed. The paper presents an abstract annotation model for skeleton programs aimed at formally describing suitable mapping of parallel activities on a high-level platform representation. The derived mapping and scheduling strategies are used to generate optimized run-time code. © 2013 Springer-Verlag Berlin Heidelberg.

GWAS identifies an NAT2 acetylator status tag single nucleotide polymorphism to be a major locus for skin fluorescence

Skin fluorescence (SF) is a non-invasive marker of AGEs and is associated with the long-term complications of diabetes. SF increases with age and is also greater among individuals with diabetes. A familial correlation of SF suggests that genetics may play a role. We therefore performed parallel genome-wide association studies of SF in two cohorts.

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.

Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags

BACKGROUND: Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.

DESCRIPTION: This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.

CONCLUSIONS: Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.