Ralf Eickhoff

Learn More
Neural networks are intended to be used in future nano-electronic systems since neural architectures seem to be robust against malfunctioning elements and noise in their weights. In this paper we analyze the fault-tolerance of Radial Basis Function networks to Stuck-At-Faults at the trained weights and at the output of neurons. Moreover, we determine upper(More)
—In this paper, we study beamforming schemes for a novel MIMO transceiver, which performs adaptive signal combining in the radio-frequency (RF) domain. Assuming perfect channel knowledge at the receiver side, we consider the problem of designing the transmit and receive RF beamformers under orthogonal frequency division multiplexing (OFDM) transmissions. In(More)
In this paper, we study transmission schemes for a novel OFDM-based MIMO system which performs adaptive signal combining in radio-frequency (RF). Specifically, we consider the problem of selecting the linear precoder and the transmit and receive RF weights (or beamformers) for minimizing the bit error rate (BER) under the assumption of perfect channel(More)
—In this paper, we study beamforming schemes for a novel MIMO transceiver, which performs adaptive signal combining in the radio-frequency domain. Assuming perfect channel knowledge at both the transmit and receive sides, we consider the problem of selecting the transmit and receive RF beamformers that maximize the capacity (MaxCAP criterion) of the system(More)
Due to continuous advancements in modern technology processes which have resulted in integrated circuits with smaller feature sizes and higher complexity , current system-on-chip designs consist of many different components such as memories, interfaces and microprocessors. To handle this growing number of components, an efficient communication structure(More)
– Neural networks are intended to be used in future nanoelectronics since these architectures seem to be fault-tolerant to malfunctioning elements and robust to noise. In this paper, the robustness to noise of Basis Function networks using tensor product stabilizers is analyzed and upper bounds of the mean square error under noise contaminated weights or(More)