Solid-state laser resonators with diffractive optic thermalaberration correction

A solid-state laser cavity is studied where the laser crystal aberrations are corrected by a diffractive optic element. The type of aberration and the location of the correction plate are found to significantly influence the modal performance of the cavity. The largest modal discrimination is obtained for axicon-like crystal aberrations and when the correction is performed close to the end mirror. An experiment is performed with a laser diode-pumped Nd:YVO₄ laser. With no correction, a thermal aberration of approximately two waves is measured across the crystal. By using a diffractive corrector plate, the single-mode slope efficiency is increased by a factor of 4, and the total single-mode output power is increased by a factor of 3     

Lossy Communicating Finite State Machines

A network of communicating finite state machines (CFSM) consists of a set of finite state machines that communicate asynchronously with each other over (potentially) unbounded FIFO channels by sending and receiving typed messages. As a concurrency model, CFSMs has been widely used to specify and validate communications protocols. In this paper we propose to extend the classical CFSM model by introducing a new type of actions – the deletion action. The resulted model is called lossy communicating finite state machines (LCFSMs). The LCFSM model remedies two weaknesses in classical CFSM model. We show that the LCFSM model allows specification and verification of unreliable communication channels with no need of extra CFSMs. The LCFSM model enables more succinct specification and verification of communication protocols that use unreliable communication channels. LCFSM paradigm can also be used to concisely model communication errors such as dropping datagrams in UDP due to lack of local buffers.     

An epistemic event‐based correlation approach for managing pervasive networks

Existing pervasive applications are based on time series data that possess the form of time‐ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on‐the‐fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. In this paper we analyze complex‐event semantic correlation that examines epistemic uncertainty in computer networks by using Dempster–Shafer theory to support a high‐volume, event‐based, in‐network and non‐deterministic pervasive network management. We consider imprecision and uncertainty when an event is detected and associate a belief parameter with the semantics and the detection of composite events. The approach taps into in‐network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. In the end, we establish that a lightweight, distributed, large‐volume, event‐based technique which exploits epistemic uncertainty to correlate events along contextual dimensions provides a successful technique for enabling management of large‐scale and pervasive contemporary and future computer networks. Copyright © 2011 John Wiley & Sons, Ltd.     

Battery-and wire-less tire pressure measurement systems (TPMS) sensor

This paper focuses on the design and development of a tire pressure measurement systems (TPMS) sensor powered by on-the-go piezoelectric energy harvesting. The prime research motivation was to achieve replacement of a limited capacity power source such as a battery with an on-the-go power generation method in order to enhance TPMS lifespan and simplify the design, with the former having greater priority. Very low cost and highly flexible piezoceramic (PZT) bender elements are used to generate power inside the automobile wheel assembly thereby replacing the battery and eliminating the need for a motion sensing mechanism and/or circuitry since PZT harvesters only produce power during wheel rotation. A fully operational sensor configuration which proves the feasibility of PZT based energy harvesting as a substitute for a permanent power source is discussed in detail.     

An evolving neural network to perform dynamic principal component analysis

Nonlinear principal component analysis is one of the best dimension reduction techniques developed during the recent years which have been applied in different signal-processing applications. In this paper, an evolving category of auto-associative neural network is presented which is applied to perform dynamic nonlinear principal component analysis. Training strategy of the network implements both constructive and destructive algorithms to extract dynamic principal components of speech database. In addition, the proposed network makes it possible to eliminate some dimensions of sequences that do not play important role in the quality of speech processing. Finally, the network is successfully applied to solve missing data problem.     

Chemical and nutritional assessment of sweetpotato proteins

The solubility of sweetpotato nitrogen increased outside the pH range 3-6 for the pulp, and 2-5 for the peel of the two varieties namely, "Abees" and "Giza 69". The minimum nitrogen extractability occurred between the pH range 3-4, and 4-5 for the peel and pulp, respectively. The sweetpotato protein isolates were prepared separately from both peel and pulp of the two varieties. The former had the highest values of carbohydrates and ash while the latter had the highest values of protein and fat. The in-vitro digestibility of sweetpotato proteins and casein by pepsin-pancreatin was studied. Casein was more easily digested than the proteins of sweetpotatoes, whose digestibilities were higher in the pulp than in the peel of the two varieties. No trypsin inhibitor activity was detected in peel and pulp of both varieties under test. The effect of the variety of sweetpotatoes on the protein patterns was studied using the polyacrylamide gel electrophoresis and varietal specific patterns were obtained.     

Game theoretic modeling of security and trust relationship in cyberspace

Today, online network services have evolved as the highest‐emergent medium, enabling various online activities to be lucrative. However, these lucrative activities also bring new forms of privacy threats to the community. In a reliable e‐business service, users should be able to trust the providers of the service to protect their customers' privacy. The service providers should not risk the personal and private information about their customers in cyberspace. There is an economic gain for a business provider when users trust the service provider. Despite those benefits, cyber security concern is the main reason some large organization may go bankrupted. Unfortunately, attackers may attempt to breach a provider's database and expose customers' private information. Therefore, in this paper, we propose a game theoretic framework for security and trust relationship in cyberspace for users, service providers, and attackers. Mathematical proofs and evaluations support our model. Service providers may use the model to see how important and dissuasive against attackers is when investing in cybersecurity. Copyright © 2016 John Wiley & Sons, Ltd.     

Some refinements of the standard autoassociative neural network

Improving the training algorithm, determining near-optimal number of nonlinear principal components (NLPCs), extracting meaningful NLPCs, and increasing the nonlinear, dynamic, and selective processing capability of the standard autoassociative neural network are the objectives of this article that are achieved independently by some new refinements of the network structure and the training algorithm. In addition, three different topologies of the network are presented, which make it possible to perform local nonlinear principal component analysis. Performances of all methods are evaluated by a stock price database that demonstrates their efficiency in different situations. Finally, as it will be illustrated in the last section, the proposed structures can be easily combined together, which introduce them as efficient tools in a wide range of signal processing applications.     

Tubular precipitation structures: materials synthesis under non-equilibrium conditions

Inorganic precipitation reactions are known to self-organize a variety of macroscopic structures, including hollow tubes. We discuss recent advances in this field with an emphasis on experiments similar to 'silica gardens'. These reactions involve metal salts and sodium silicate solution. Reactions triggered from reagent-loaded microbeads can produce tubes with inner radii of down to 3 μm. Distinct wall morphologies are reported. For pump-driven injection, three qualitatively different growth regimes exist. In one of these regimes, tubes assemble around a buoyant jet of reactant solution, which allows the quantitative prediction of the tube radius. Additional topics include relaxation oscillations and the templating of tube growth with pinned gas bubble and mechanical devices. The tube materials and their nano-to-micro architectures are discussed for the cases of silica/Cu(OH)(2) and silica/Zn(OH)(2)/ZnO tubes. The latter case shows photocatalytic activity and photoluminescence.