Design technique for mm-wave IC realization of the load network of switched-mode class-EF power amplifier

Analysis and synthesis of the new Class-EF power amplifier (PA) are presented in this paper. The proposed circuit offers means to alleviate some of the major issues faced by existing Class-EF and Class-EF PAs, such as (1) substantial power losses due to parasitic resistance of the large inductor in the Class-EF load network, (2) unpredictable behaviour of practical lumped inductors and capacitors at harmonic frequencies, and (3) deviation from ideal Class-EF operation mode due to detrimental effects of device output inductance at high frequencies. The transmission-line load network of the Class-EF PA topology elaborated in this paper simultaneously satisfies the Class-EF optimum impedance requirements at fundamental frequency, second, and third harmonics as well as simultaneously providing matching to the circuit optimum load resistance for any prescribed system load resistance. Furthermore, an elegant solution using an open and short-circuit stub arrangement is suggested to overcome the problem encountered in the mm-wave IC realizations of the Class-EF PA load network due to lossy quarter-wave line. © 2010 IEICE Institute of Electronics Informati.

High-efficiency low-voltage-stress Class-EF PA with extended maximum operating frequency

The Class-EF power amplifier (PA) introduced recently has a peak switch voltage much lower than the well-known Class-E PA. However, the value of the transistor output capacitance at high frequencies is typically larger than the required Class-EF optimum shunt capacitance. As a result, softswitching operation that minimizes power dissipation during OFF-to-ON transient cannot be achieved at high frequencies. A novel Class-EF topology with transmission-line load network proposed in this paper allows the PA to operate at much higher frequencies without trading the other figures of merit. Closed-form formulations are derived to simultaneously satisfy the Class-EF impedances requirement at fundamental frequency, all even harmonics, and the first two odd harmonics as well as to provide matching to 50O load. © 2011 Institut fur Mikrowellen.

Kinematic Analysis and Dimensional Synthesis of Exechon Parallel Kinematic Machine for Large Volume Machining

A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

Model categories for orthogonal calculus

We restate the notion of orthogonal calculus in terms of model categories. This provides a cleaner set of results and makes the role of O(n)-equivariance clearer. Thus we develop model structures for the category of n-polynomial and n-homogeneous functors, along with Quillen pairs relating them. We then classify n-homogeneous functors, via a zig-zag of Quillen equivalences, in terms of spectra with an O(n)-action. This improves upon the classification theorem of Weiss. As an application, we develop a variant of orthogonal calculus by replacing topological spaces with orthogonal spectra.

Precise Subnanometer Plasmonic Junctions for SERS within Gold Nanoparticle Assemblies Using Cucurbit[n]uril "Glue"

Cucurbit[n]urils (CB[n]) are macrocyclic host molecules with subnanometer dimensions capable of binding to gold surfaces. Aggregation of gold nanoparticles with CB[n] produces a repeatable, fixed, and rigid interparticle separation of 0.9 nm, and thus such assemblies possess distinct and exquisitely sensitive plasmonics. Understanding the plasmonic evolution is key to their use as powerful SERS substrates. Furthermore, this unique spatial control permits fast nanoscale probing of the plasmonics of the aggregates "glued" together by CBs within different kinetic regimes using simultaneous extinction and SERS measurements. The kinetic rates determine the topology of the aggregates including the constituent structural motifs and allow the identification of discrete plasmon modes which are attributed to disordered chains of increasing lengths by theoretical simulations. The CBs directly report the near-field strength of the nanojunctions they create via their own SERS, allowing calibration of the enhancement. Owing to the unique barrel-shaped geometry of CB[n] and their ability to bind "guest" molecules, the aggregates afford a new type of in situ self-calibrated and reliable SERS substrate where molecules can be selectively trapped by the CB[n] and exposed to the nanojunction plasmonic field. Using this concept, a powerful molecular-recognition-based SERS assay is demonstrated by selective cucurbit[n]uril host-guest complexation.

A theoretical examination of known and hypothetical clathrate hydrate materials

The recent synthesis of a new hydrogen binary hydrate with the sH structure has highlighted the potential storage capabilities of water clathrates [T. A. Strobel, C. A. Koh, and E. D. Sloan, J. Phys. Chem. B 112, 1885 (2008) and A. R. C. Duarte, A. Shariati, L. J. Rovetto, and C. J. Peters, J. Phys. Chem. B 112, 1888 (2008)]. In this work, the absorption of hydrogen and the promoters used in the experimental work are considered using a simplified model for the host-guest interaction, which allows one to understand the stabilizing effects of multiple help molecules. Two further hypothetical clathrates, which are isostructural with known zeolite structures, are also investigated. It is shown that the energy gained by absorbing adamantane into these two frameworks is far greater than that gained upon absorption of adamantane into the sH structure. Hence, a clathrate with the same topology as the DDR (Sigma 1) zeolite may be synthesizable with adamantane and hydrogen as guest molecules as, in the conditions explored here, this phase appears to be more stable than the sH structure. (C) 2009 American Institute of Physics. [DOI: 10.1063/1.3142503]

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Using Mesh-Geometry Relationships to Transfer Analysis Models between CAE Tools

Integrating analysis and design models is a complex task due to differences between the models and the architectures of the toolsets used to create them. This complexity is increased with the use of many different tools for specific tasks during an analysis process. In this work various design and analysis models are linked throughout the design lifecycle, allowing them to be moved between packages in a way not currently available. Three technologies named Cellular Modeling, Virtual Topology and Equivalencing are combined to demonstrate how different finite element meshes generated on abstract analysis geometries can be linked to their original geometry. Establishing the equivalence relationships between models enables analysts to utilize multiple packages for specialist tasks without worrying about compatibility issues or rework.

Chiton phylogeny (Mollusca Polyplacophora) and the placement of the enigmatic species Choriplax grayi (H. Adams & Angas)

Shallow marine chitons (Mollusca:Polyplacophora:Chitonida) are widespread and well described from established morphoanatomical characters, yet key aspects of polyplacophoran phylogeny have remained unresolved. Several species, including Hemiarthrum setulosum Carpenter in Dall, 1876, and especially the rare and enigmatic Choriplax grayi (Adams & Angas, 1864), defy systematic placement. Choriplax is known from only a handful of specimens and its morphology is a mosaic of key taxonomic features from two different clades. Here, new molecular evidence provides robust support for its correct association with a third different clade: Choriplax is placed in the superfamily Mopalioidea. Hemiarthrum is included in Cryptoplacoidea, as predicted from morphological evidence. Our multigene analysis of standard nuclear and mitochondrial markers demonstrates that the topology of the order Chitonida is divided into four clades, which have also been recovered in previous studies: Mopalioidea is sister to Cryptoplacoidea, forming a clade Acanthochitonina. The family Callochitonidae is sister to Acanthochitonina. Chitonoidea is resolved as the earliest diverging group within Chitonida. Consideration of this unexpected result for Choriplax and our well-supported phylogeny has revealed differing patterns of shell reduction separating the two superfamilies within Acanthochitonina. As in many molluscs, shell reduction as well as the de novo development of key shell features has occurred using different mechanisms, in multiple lineages of chitons.

Managing Equivalent Representations of Design and Analysis Models

There is a requirement for better integration between design and analysis tools, which is difficult due to their different objectives, separate data representations and workflows. Currently, substantial effort is required to produce a suitable analysis model from design geometry. Robust links are required between these different representations to enable analysis attributes to be transferred between different design and analysis packages for models at various levels of fidelity.

This paper describes a novel approach for integrating design and analysis models by identifying and managing the relationships between the different representations. Three key technologies, Cellular Modeling, Virtual Topology and Equivalencing, have been employed to achieve effective simulation model management. These technologies and their implementation are discussed in detail. Prototype automated tools are introduced demonstrating how multiple simulation models can be linked and maintained to facilitate seamless integration throughout the design cycle.

The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

Picking up the Pieces: Self-Healing in reconfigurable networks

We consider the problem of self-healing in networks that are reconfigurable in the sense that they can change their topology during an attack. Our goal is to maintain connectivity in these networks, even in the presence of repeated adversarial node deletion, by carefully adding edges after each attack. We present a new algorithm, DASH, that provably ensures that: 1) the network stays connected even if an adversary deletes up to all nodes in the network; and 2) no node ever increases its degree by more than 2 log n, where n is the number of nodes initially in the network. DASH is fully distributed; adds new edges only among neighbors of deleted nodes; and has average latency and bandwidth costs that are at most logarithmic in n. DASH has these properties irrespective of the topology of the initial network, and is thus orthogonal and complementary to traditional topology- based approaches to defending against attack. We also prove lower-bounds showing that DASH is asymptotically optimal in terms of minimizing maximum degree increase over multiple attacks. Finally, we present empirical results on power-law graphs that show that DASH performs well in practice, and that it significantly outperforms naive algorithms in reducing maximum degree increase.

Algorithms for Self-Healing Networks

Many modern networks are \emph{reconfigurable}, in the sense that the topology of the network can be changed by the nodes in the network. For example, peer-to-peer, wireless and ad-hoc networks are reconfigurable. More generally, many social networks, such as a company's organizational chart; infrastructure networks, such as an airline's transportation network; and biological networks, such as the human brain, are also reconfigurable. Modern reconfigurable networks have a complexity unprecedented in the history of engineering, resembling more a dynamic and evolving living animal rather than a structure of steel designed from a blueprint. Unfortunately, our mathematical and algorithmic tools have not yet developed enough to handle this complexity and fully exploit the flexibility of these networks. We believe that it is no longer possible to build networks that are scalable and never have node failures. Instead, these networks should be able to admit small, and maybe, periodic failures and still recover like skin heals from a cut. This process, where the network can recover itself by maintaining key invariants in response to attack by a powerful adversary is what we call \emph{self-healing}. Here, we present several fast and provably good distributed algorithms for self-healing in reconfigurable dynamic networks. Each of these algorithms have different properties, a different set of gaurantees and limitations. We also discuss future directions and theoretical questions we would like to answer. %in the final dissertation that this document is proposed to lead to.