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In this paper we propose a QR-decomposition hardware implementation that processes complex calculations in the logarithmic number system. Thus, low complexity numeric format converters are installed, using nonuniform piecewise and multiplier-less function approximation. The proposed algorithm is simulated with several different configurations in a downlink(More)
The use of Orthogonal Frequency Division Multiplex (OFDM) modulation has become increasingly important for actual and future mobile communication systems [1]. Within this scope, Carrier Frequency Offset (CFO) compensation is indispensable [2]. Its underlying algorithm requires the calculation of trigonometric functions, which is difficult to achieve by(More)
As nowadays Direct Digital Frequency Synthesizers (DDFS) are used in a vast area of applications, the demand for simple and efficient hardware design and implementation methods is a highly important aspect. In this paper a new approach is introduced considering Automatic Nonuniform Piecewise linear function Approximation (ANPA). Automatic function(More)
The impact of power efficient wireless sensor networks (WSN) is getting more and more important, as it is built of battery driven sensor nodes (SN). Beside common low power techniques like voltage scaling, variable-rate sampling (VRS) has been exposed as an adequate possibility to minimize the transceiver activity [1]. In this paper a high performance(More)
This paper presents a new approach for hardware-based QR-decomposition using an efficient computation scheme of the Givens-Rotation. In detail, the angle of rotation and its application to the Givens-Matrix are processed in a direct, straightforward manner. High-performance signal processing is achieved by piecewise approximation of the arctangent and sine(More)
In this paper the first low-latency architecture design and hardware implementation for structure-based inpainting to detect and complete isophotes in brain activity recording is presented. This novel mask-based compression and inpainting-based reconstruction methodology for correlated neural signals is especially important for the realization of(More)
This paper presents the first hardware architecture for compressing and reconstructing correlated neural signals using structure-based inpainting. This novel methodology is especially important for the realization of implantable neural measurement systems (NMS), which are subject to strict constraints in terms of area and energy consumption. Such an implant(More)