Stability analysis of RF amplifiers based on MIMO pole-zero identification

Spurious oscillations are one of the principal issues faced by microwave and RF circuit designers. The rigorous detection of instabilities or the characterization of measured spurious oscillations is still an ongoing challenge. This project aims to create a new stability analysis CAD program that tackles this chal- lenge. Multiple Input Multiple Output (MIMO) pole-zero identification analysis is introduced on the program as a way to create new methods to automate the stability analysis process and to help designers comprehend the obtained results and prevent incorrect interpretations. The MIMO nature of the analysis contributes to eliminate possible controllability and observability losses and helps differentiate mathematical and physical quasi-cancellations, products of overmodeling. The created program reads Single Input Single Output (SISO) or MIMO frequency response data, and determines the corresponding continuous transfer functions with Vector Fitting. Once the transfer function is calculated, the corresponding pole/zero diagram is mapped enabling the designers to analyze the stability of an amplifier. Three data processing methods are introduced, two of which consist of pole/zero elimina- tions and the latter one on determining the critical nodes of an amplifier. The first pole/zero elimination method is based on eliminating non resonant poles, whilst the second method eliminates the poles with small residue by assuming that their effect on the dynamics of a system is small or non-existent. The critical node detection is also based on the residues; the node at which the effect of a pole on the dynamics is highest is defined as the critical node. In order to evaluate and check the efficiency of the created program, it is compared via examples with another existing commercial stability analysis tool (STAN tool). In this report, the newly created tool is proved to be as rigorous as STAN for detecting instabilities. Additionally, it is determined that the MIMO analysis is a very profitable addition to stability analysis, since it helps to eliminate possible problems of loss of controllability, observability and overmodeling.

Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network

In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

Identification of the main allergen sensitizers in an Iran asthmatic population by molecular diagnosis

Background: There has been a significant growth in the prevalence of allergy, mainly associated to IgE-mediated disorders such as asthma and rhinitis. The identification of atopy in asthmatic patients through the measurement of specific IgE can help to identify risk factors that cause asthmatic symptoms in patients. The development and use of individualized allergen-based tests by the Component Resolved Diagnosis has been a crucial advance in the accurate diagnosis and control of allergic patients. The objective of this work was to assess the usefulness of molecular diagnosis to identify environmental allergens as possible factors influencing the development and manifestation of asthma in a group of asthmatic patients from Iran. Methods: Studied population: 202 adult asthmatic patients treated at the Loghman Hakim Hospital and Pasteur Institute of Teheran (Iran) from 2011 to 2012. Specific IgE determined by the ImmunoCAP system were used to both evaluate the patients' atopic condition and the molecules involved in the allergic sensitization. SDS-PAGE IgE-immunoblotting associated with mass spectrometry was carried out to study the cockroach IgE-binding sensitizing proteins. Results: Forty-five percent of all patients could be considered atopic individuals. Eighty-two percent of atopic patients were sensitized to pollen allergens. The Salsola kali (Sal k 1) and the Phleum pratense (rPhl p 1 and/or rPhl p 5) major allergens were the most common sensitizers among pollens (71% and 18%, respectively). Thirty-five percent of the atopic population was sensitized to cockroach. Four different allergens, including a previously unknown alpha-amylase, were identified in the cockroach extract. No significant associations could be demonstrated between the severity of asthma and the specific IgE levels in the atopic population. Statistical analysis identified the Sal k 1 as the main protein allergen influencing the development and expression of asthma in the studied population. Conclusions: Pollen and cockroach were the most relevant allergen sources in the asthmatic population. The Salsola kali major allergen was the main cause for sensitization in the atopic patients suffering asthma. Using the Component Resolved Diagnosis, it was possible to identify a new Blattella germanica cockroach allergen (Blattella alpha amylase 53 kDa) that could sensitize a relevant percentage of this population.