Efficient wireless power delivery for biomedical implants

Power delivery for biomedical implants is a major consideration in their design for both measurement and stimulation. When performed by a wireless technique, transmission efficiency is critically important not only because of the costs associated with any losses but also because of the nature of those losses, for example, excessive heat can be uncomfortable for the individual involved. In this study, a method and means of wireless power transmission suitable for biomedical implants are both discussed and experimentally evaluated. The procedure initiated is comparable in size and simplicity to those methods already employed; however, some of Tesla’s fundamental ideas have been incorporated in order to obtain a significant improvement in efficiency. This study contains a theoretical basis for the approach taken; however, the emphasis here is on practical experimental analysis

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Efficient wireless power delivery for biomedical implants

Power delivery for biomedical implants is a major consideration in their design for both measurement and stimulation. When performed by a wireless technique, transmission efficiency is critically important not only because of the costs associated with any losses but also because of the nature of those losses, for example, excessive heat can be uncomfortable for the individual involved. In this study, a method and means of wireless power transmission suitable for biomedical implants are both discussed and experimentally evaluated. The procedure initiated is comparable in size and simplicity to those methods already employed; however, some of Tesla’s fundamental ideas have been incorporated in order to obtain a significant improvement in efficiency. This study contains a theoretical basis for the approach taken; however, the emphasis here is on practical experimental analysis.

Improving transmission reliability of low-power medium access control protocols using average diversity combining

Embedded computer systems equipped with wireless communication transceivers are nowadays used in a vast number of application scenarios. Energy consumption is important in many of these scenarios, as systems are battery operated and long maintenance-free operation is required. To achieve this goal, embedded systems employ low-power communication transceivers and protocols. However, currently used protocols cannot operate efficiently when communication channels are highly erroneous. In this study, we show how average diversity combining (ADC) can be used in state-of-the-art low-power communication protocols. This novel approach improves transmission reliability and in consequence energy consumption and transmission latency in the presence of erroneous channels. Using a testbed, we show that highly erroneous channels are indeed a common occurrence in situations, where low-power systems are used and we demonstrate that ADC improves low-power communication dramatically.

Wireless Sensor Networks: MAC protocols and real time applications

Wireless Senor Networks(WSNs) detect events using one or more sensors, then collect data from detected events using these sensors. This data is aggregated and forwarded to a base station(sink) through wireless communication to provide the required operations. Different kinds of MAC and routing protocols need to be designed for WSN in order to guarantee data delivery from the source nodes to the sink. Some of the proposed MAC protocols for WSN with their techniques, advantages and disadvantages in the terms of their suitability for real time applications are discussed in this paper. We have concluded that most of these protocols can not be applied to real time applications without improvement

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.

Wireless energy behaviour monitoring (Wi-be) for office buildings

This paper presents a study on reduction of energy consumption in buildings through behaviour change informed by wireless monitoring systems for energy, environmental conditions and people positions. A key part to the Wi-Be system is the ability to accurately attribute energy usage behaviour to individuals, so they can be targeted with specific feedback tailored to their preferences. The use of wireless technologies for indoor positioning was investigated to ascertain the difficulties in deployment and potential benefits. The research to date has demonstrated the effectiveness of highly disaggregated personal-level data for developing insights into people’s energy behaviour and identifying significant energy saving opportunities (up to 77% in specific areas). Behavioural research addressed social issues such as privacy, which could affect the deployment of the system. Radio-frequency research into less intrusive technologies indicates that received-signal-strength-indicator-based systems should be able to detect the presence of a human body, though further work would be needed in both social and engineering areas.

New scientific paradigms for probiotics and prebiotics

The inaugural meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) was held May 3 to May 5 2002 in London, Ontario, Canada. A group of 63 academic and industrial scientists from around the world convened to discuss current issues in the science of probiotics and prebiotics. ISAPP is a non-profit organization comprised of international scientists whose intent is to strongly support and improve the levels of scientific integrity and due diligence associated with the study, use, and application of probiotics and prebiotics. In addition, ISAPP values its role in facilitating communication with the public and healthcare providers and among scientists in related fields on all topics pertinent to probiotics and prebiotics. It is anticipated that such efforts will lead to development of approaches and products that are optimally designed for the improvement of human and animal health and well being. This article is a summary of the discussions, conclusions, and recommendations made by 8 working groups convened during the first ISAPP workshop focusing on the topics of: definitions, intestinal flora, extra-intestinal sites, immune function, intestinal disease, cancer, genetics and genomics, and second generation prebiotics. Humans have evolved in symbiosis with an estimated 1014 resident microorganisms. However, as medicine has widely defined and explored the perpetrators of disease, including those of microbial origin, it has paid relatively little attention to the microbial cells that constitute the most abundant life forms associated with our body. Microbial metabolism in humans and animals constitutes an intense biochemical activity in the body, with profound repercussions for health and disease. As understanding of the human genome constantly expands, an important opportunity will arise to better determine the relationship between microbial populations within the body and host factors (including gender, genetic background, and nutrition) and the concomitant implications for health and improved quality of life. Combined human and microbial genetic studies will determine how such interactions can affect human health and longevity, which communication systems are used, and how they can be influenced to benefit the host. Probiotics are defined as live microorganisms which, when administered in adequate amounts confer a health benefit on the host.1 The probiotic concept dates back over 100 years, but only in recent times have the scientific knowledge and tools become available to properly evaluate their effects on normal health and well being, and their potential in preventing and treating disease. A similar situation exists for prebiotics, defined by this group as non-digestible substances that provide a beneficial physiological effect on the host by selectively stimulating the favorable growth or activity of a limited number of indigenous bacteria. Prebiotics function complementary to, and possibly synergistically with, probiotics. Numerous studies are providing insights into the growth and metabolic influence of these microbial nutrients on health. Today, the science behind the function of probiotics and prebiotics still requires more stringent deciphering both scientifically and mechanistically. The explosion of publications and interest in probiotics and prebiotics has resulted in a body of collective research that points toward great promise. However, this research is spread among such a diversity of organisms, delivery vehicles (foods, pills, and supplements), and potential health targets such that general conclusions cannot easily be made. Nevertheless, this situation is rapidly changing on a number of important fronts. With progress over the past decade on the genetics of lactic acid bacteria and the recent, 2,3 and pending, 4 release of complete genome sequences for major probiotic species, the field is now armed with detailed information and sophisticated microbiological and bioinformatic tools. Similarly, advances in biotechnology could yield new probiotics and prebiotics designed for enhanced or expanded functionality. The incorporation of genetic tools within a multidisciplinary scientific platform is expected to reveal the contributions of commensals, probiotics, and prebiotics to general health and well being and explicitly identify the mechanisms and corresponding host responses that provide the basis for their positive roles and associated claims. In terms of human suffering, the need for effective new approaches to prevent and treat disease is paramount. The need exists not only to alleviate the significant mortality and morbidity caused by intestinal diseases worldwide (especially diarrheal diseases in children), but also for infections at non-intestinal sites. This is especially worthy of pursuit in developing nations where mortality is too often the outcome of food and water borne infection. Inasmuch as probiotics and prebiotics are able to influence the populations or activities of commensal microflora, there is evidence that they can also play a role in mitigating some diseases. 5,6 Preliminary support that probiotics and prebiotics may be useful as intervention in conditions including inflammatory bowel disease, irritable bowel syndrome, allergy, cancer (especially colorectal cancer of which 75% are associated with diet), vaginal and urinary tract infections in women, kidney stone disease, mineral absorption, and infections caused by Helicobacter pylori is emerging. Some metabolites of microbes in the gut may also impact systemic conditions ranging from coronary heart disease to cognitive function, suggesting the possibility that exogenously applied microbes in the form of probiotics, or alteration of gut microecology with prebiotics, may be useful interventions even in these apparently disparate conditions. Beyond these direct intervention targets, probiotic cultures can also serve in expanded roles as live vehicles to deliver biologic agents (vaccines, enzymes, and proteins) to targeted locations within the body. The economic impact of these disease conditions in terms of diagnosis, treatment, doctor and hospital visits, and time off work exceeds several hundred billion dollars. The quality of life impact is also of major concern. Probiotics and prebiotics offer plausible opportunities to reduce the morbidity associated with these conditions. The following addresses issues that emerged from 8 workshops (Definitions, Intestinal Flora, Extra-Intestinal Sites, Immune Function, Intestinal Disease, Cancer, Genomics, and Second Generation Prebiotics), reflecting the current scientific state of probiotics and prebiotics. This is not a comprehensive review, however the study emphasizes pivotal knowledge gaps, and recommendations are made as to the underlying scientific and multidisciplinary studies that will be required to advance our understanding of the roles and impact of prebiotics, probiotics, and the commensal microflora upon health and disease management.

RBF equalizer design using directional evolutionary multi-objective optimization

Whilst radial basis function (RBF) equalizers have been employed to combat the linear and nonlinear distortions in modern communication systems, most of them do not take into account the equalizer's generalization capability. In this paper, it is firstly proposed that the. model's generalization capability can be improved by treating the modelling problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets. Then, as a modelling application, a new RBF equalizer learning scheme is introduced based on the directional evolutionary MOO (EMOO). Directional EMOO improves the computational efficiency of conventional EMOO, which has been widely applied in solving MOO problems, by explicitly making use of the directional information. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good performance not only on explaining the training samples but on predicting the unseen samples.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Nonlinear channel equalizer design using directional evolutionary multi-objective optimization

In this paper, a new equalizer learning scheme is introduced based on the algorithm of the directional evolutionary multi-objective optimization (EMOO). Whilst nonlinear channel equalizers such as the radial basis function (RBF) equalizers have been widely studied to combat the linear and nonlinear distortions in the modern communication systems, most of them do not take into account the equalizers' generalization capabilities. In this paper, equalizers are designed aiming at improving their generalization capabilities. It is proposed that this objective can be achieved by treating the equalizer design problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets, followed by deriving equalizers with good capabilities of recovering the signals for all the training sets. Conventional EMOO which is widely applied in the MOO problems suffers from disadvantages such as slow convergence speed. Directional EMOO improves the computational efficiency of the conventional EMOO by explicitly making use of the directional information. The new equalizer learning scheme based on the directional EMOO is applied to the RBF equalizer design. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good generalization capabilities, i.e., good performance on predicting the unseen samples.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.