Peter Russer

Bio: Peter Russer is an academic researcher from Technische Universität München. The author has contributed to research in topics: Time domain & Electromagnetic interference. The author has an hindex of 38, co-authored 708 publications receiving 7743 citations. Previous affiliations of Peter Russer include Ferdinand-Braun-Institut & Leibniz Association.

An efficient method for computer aided noise analysis of linear amplifier networks

Abstract: A method for computer aided noise analysis is presented which is based on a description of noise by means of correlation matrices. The method is a two-port analysis and it is, therefore, applicable to circuits which are composed of simple two-ports with known noise performance. The correlation matrix concept holds two main advantages over other methods of noise analysis. Partially correlated noise sources can be treated without any loss of efficiency and information concerning minimum noise figure and noise matching conditions is obtained.

Influence of Microwave Radiation on Current‐Voltage Characteristic of Superconducting Weak Links

Abstract: The current‐voltage characteristics of microwave‐irradiated superconducting weak links have been calculated on the analog computer. Experimental results of several authors can be interpreted correctly by taking into consideration the resistive feedback.

A field theoretical derivation of TLM

Abstract: A field theoretical foundation of the TLM method is presented in this paper. In the derivation of the condensed symmetric TLM node, the Method of Moments is applied to Maxwell's equations to obtain discretized field equations. It is shown that the traditional mapping between wave amplitudes and electric and magnetic field components incorporates serious problems. Therefore, a new mapping between the wave amplitudes and the electric and magnetic field components is introduced. Applying the new mapping to the discretized field equations, the fundamental equations of the three-dimensional TLM method with condensed symmetric node are derived from Maxwell's equations. >

Si and SiGe millimeter-wave integrated circuits

Abstract: Monolithic integrated millimeter-wave circuits based on silicon and SiGe are emerging as an attractive option in the field of millimeter-wave communications and millimeter-wave sensors. The combination of active devices with passive planar structures, including also antenna elements, allows single-chip realizations of complete millimeter-wave front-ends. This paper reviews the state-of-the-art silicon- and SiGe-based monolithic integrated millimeter-wave circuits. The technological background as well as active and nonlinear devices and passive circuit structures suitable for silicon- and SiGe-based monolithic integrated millimeter-wave circuits are discussed. Examples of such integrated circuits and first systems applications are also presented.

Minimizing the Noise Penalty Due to Mutual Coupling for a Receiving Array

Abstract: For phased array receivers, mutual coupling leads to beam-dependent active impedances which must be taken into account when matching the array ports to front end amplifiers for optimal noise performance. We study the noise penalty for several noise matching conditions and develop a matching condition that minimizes the average beam equivalent receiver noise temperature over multiple beams. For non-beamforming applications such as multiple input multiple output communications, we show that noise performance for coupled arrays can be quantified using the spectrum of an equivalent receiver noise temperature correlation matrix.

Object Detection with Discriminatively Trained Part-Based Models

Abstract: We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

A discriminatively trained, multiscale, deformable part model

Abstract: This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Abstract: Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

On the theory of superconductivity

Abstract: IN two previous notes1, Prof. Max Born and I have shown that one can obtain a theory of superconductivity by taking account of the fact that the interaction of the electrons with the ionic lattice is appreciable only near the boundaries of Brillouin zones, and particularly strong near the corners of these. This leads to the criterion that the metal should be superconductive if a set of corners of a Brillouin zone is lying very near the Fermi surface, considered as a sphere, which limits the region in the momentum space completely filled with electrons.