RF rectifiers for EM power harvesting in a deep brain stimulating device

A passive deep brain stimulation (DBS) device can be equipped with a rectenna, consisting of an antenna and a rectifier, to harvest energy from electromagnetic fields for its operation. This paper presents optimization of radio frequency rectifier circuits for wireless energy harvesting in a passive head-mountable DBS device. The aim is to achieve a compact size, high conversion efficiency, and high output voltage rectifier. Four different rectifiers based on the Delon doubler, Greinacher voltage tripler, Delon voltage quadrupler, and 2-stage charge pumped architectures are designed, simulated, fabricated, and evaluated. The design and simulation are conducted using Agilent Genesys at operating frequency of 915 MHz. A dielectric substrate of FR-4 with thickness of 1.6 mm, and surface mount devices (SMD) components are used to fabricate the designed rectifiers. The performance of the fabricated rectifiers is evaluated using a 915 MHz radio frequency (RF) energy source. The maximum measured conversion efficiency of the Delon doubler, Greinacher tripler, Delon quadrupler, and 2-stage charge pumped rectifiers are 78, 75, 73, and 76 % at -5 dBm input power and for load resistances of 5-15 kΩ. The conversion efficiency of the rectifiers decreases significantly with the increase in the input power level. The Delon doubler rectifier provides the highest efficiency at both -5 and 5 dBm input power levels, whereas the Delon quadrupler rectifier gives the lowest efficiency for the same inputs. By considering both efficiency and DC output voltage, the charge pump rectifier outperforms the other three rectifiers. Accordingly, the optimised 2-stage charge pumped rectifier is used together with an antenna to harvest energy in our DBS device.

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the '. curse of dimensionality', is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

It makes you realize, really, just how deep your subtext is : Literature, subjectivity and curriculum change

This paper draws on the notion of discourse to explore complex relationships between teachers and curriculum change. It uses poststructuralist views of discourse to explore ways in which school subjects, such as Literature, are discursively constructed across time, while teachers too are positioned within discourses that shape the ways they understand the subject and themselves as teachers of it. This paper reports on the experience of a small group of teachers of a new literature course in the Australian state of Victoria. Nine teachers were interviewed over 3 years, and the interview transcripts read for traces of discourses formative in shaping their response to the new course. I identified three discourses: Leavisite and New Critical formations of the subject Literature; charismatic pedagogy; and critical theory, which was embodied in the new subject's study design. These 3 discourses, together with the traditions and culture of the school, form the framework for analysis of the interviews. The paper explores ways in which the teachers' positioning within this mix of discourses and settings variously supported or undermined their preparedness to accept new configurations of the subject Literature as well as the implications of curriculum change not just for constructions of the subject but also for teacher subjectivity.

Diaspora, return and migrant architectures

Nearly all discourses on migration (to my knowledge) emphasise that the migrant is not so much a traveller, but a figure oriented towards settlement and a particular destination. Discourses on migration have attended more to the process and site of ‘arrival’, and few studies have focused on the process and site of ‘departure’. However, central to the thesis of this paper would be the testimony of two migrant houses – one in the city of  immigration (Melbourne, Australia), and the other in the village of emigration (Zavoj in Macedonia). The focus will be on the Zavoj house as a significant house, a house that points to a thesis about how architecture makes explicit other processes of migration, namely that of ‘return’. Here there are several intertwined communities and nations, and also different notions of community and nation. It has been noted that ‘diaspora’ is constituted through longer distances, severe separation, and a taboo on return. And yet implicit in many more ‘autobiographical’ accounts is that one only leaves with a promise to return. The conflict and question of ‘return’ is at the centre of the migrant’s imaginary. A study of the two houses of migration implicates a set of networks, forces, relations, circumscribing a much larger global geopolitical and cultural field that questions our understandings of diaspora, the currency of transnationalism, the binary structure of dwelling/travelling, and the fabric and fabrication of community. But the study goes inwards and underneath as well through the figure of the migrant, the figure through which the two migrant houses are deeply associated. The paper will explore the subjective nature of the thesis, the idea of a ‘migrant house’ as an imaginary architecture, a psychic geography, an imaginary community and sense of nationhood.

Fostering deep and elaborative learning and generic(soft) skill development: the strategic use of case studies in accounting education

It is vital that accounting educators take responsibility for the development of students' generic (soft) skills in conjunction with, discipline-specific skills. Research indicates that the typical learning styles of accounting students are not suited to the acquisition of generic skills. In this paper learning theory is used to provide a framework to support the use of case studies as a tool to promote appropriate learning styles and thereby enhance generic skill development. The paper details a number of strategies that may be implemented with case studies to achieve these goals. The implications for accounting educators, which are significant, are discussed.

An ecologically mixed brachiopod fauna from Changhsingian deep-water basin of South China: consequence of end-Permian global warming

The Late Permian Shaiwa Group of the Ziyun area of Guizhou, South China is a deep-water facies succession characterized by deep-water assemblages of pelagic radiolarians, foraminifers, bivalves, ammonoids and brachiopods. Here we report 20 brachiopod species in 18 genera from the uppermost Shaiwa Group. This brachiopod fauna is latest Changhsingian in age and dominated by productides. The palaeoecologic and taphonomic analysis reveals that the brachiopod fauna is preserved in situ. The attachment modes and substratum preference demonstrate that the Shaiwa brachiopod fauna comprises admixed elements of deep-water and shallow-water assemblages. The presence of the shallow-water brachiopods in the Shaiwa faunas indicates the involuntary settlement of shallow-water brachiopods. The stressed ecologic pressure, triggered by warming surface waters, restricted ecospace and short food sources, may have forced some shallow-water elements to move to hospitable deep-water settings and others to modify their habiting behaviours and exploit new ecospace in deep-water environments. We infer that the end-Permian global warming and subsequent transgression event may have accounted for the stressed environmental pressure in the shallow-water communities prior to the end-Permian mass extinction.

Fat content of chips, quality of frying fat and deep-frying practices in New Zealand fast food outlets

Objectives: To collect baseline data on the fat content of hot chips, quality (degradation) of cooking fat, deep-frying practices and related attitudes in fast food outlets in New Zealand. To identify the key determinants of the fat content of chips and quality of cooking fat. Methods: A nationally representative sample of fast food outlets (n=150, response rate 80%) was surveyed between September 1998 and March 1999. Data collected included a questionnaire, observation of cooking practices and analysis of cooked chips and frying fat. Results: Only 8% of independent operators had formal training in deep frying practices compared with 93% of chain operators. There was a wide range of fat content of chips (5%-20%, mean 11.5%). The use of thinner chips, crinkle cut chips and lower fryer fat temperature were associated with higher chip fat content. Eighty-nine per cent of chain outlets used 6–10 mm chips compared with 83% of independent outlets that used chips ≥12 mm. A wide range of frying temperatures was recorded (136–233°C) with 58% of outlets frying outside the reference range (175–190°C). As indices of fat degradation, fat acid and polar compound values above the recommended levels occurred in 54% and 5% of outlets respectively. Operators seemed willing to learn more about best practice techniques, with lack of knowledge being the main barrier to change. Conclusions and implications: Deep frying practices could be improved through operator training and certification options. Even a small decrease in the mean fat content of chips would reduce the obesogenic impact of this popular food.