CAREER:Integrated Research and Education on Delta-Sigma Based Digital Signal Processing Circuits for Low-Power Intelligent Sensors

New Mexico State University, Las Cruces NM

Investigators

Abstract

Applications of low-power integrated intelligent sensors have been prolific in recent years, including, for instance, in environmental observation, security surveillance, infrastructure monitoring and communication, and biomedical health care monitoring. Although such sensors usually have wireless data communication capability, transmitting raw sensed data is usually not an option because of limited battery power, since in a wireless sensor the radio communication power is usually much higher than the signal processing power. Therefore, intelligent sensors need to be capable of providing preprocessing of raw data based on signal processing algorithms and sending only the processed results. Another inevitable challenge in biomedical applications is oversampling. This is because the targeted biomedical signals usually have a wide fractional bandwidth in the frequency spectrum, and require time-frequency analysis. Therefore, in these applications, the Analog to Digital Convertor (ADC) have to apply oversampling in order to avoid the signal distortion introduced by anti-aliasing filters due to the trade-off between fast roll-off and flat group delay. The requirements of both oversampling and the higher resolution also exacerbate the power problem. One promising solution to the above challenges is Delta-Sigma technology. The research objective of this CAREER proposal is to apply Delta-Sigma based Integrated Circuit (IC) design in Digital Signal Processing (DSP) to solve circuit power and area problems for the next-generation of low-power intelligent sensors. The proposed research will have a broad impact on next-generation pervasive computing and ubiquitous sensing applications. The educational objectives of this CAREER proposal are promoting active, inquiry-based learning, introducing students to interdisciplinary study and research, and preparing them to meet the future expectations of both academia and industry by providing them a complete skill set including system design, assembly, verification, and optimization, and making cutting-edge research projects more accessible to minority and underrepresented groups of students. The proposed integrated linear signal processing circuits are based on Delta-Sigma re-modulation using digital Delta-Sigma modulators. Delta Sigma linear processing circuits such as adders, coefficient multipliers, and filters will be designed, fabricated, characterized, and compared to conventional signal processing systems. In particular, based on the preliminary results, the research will target a biomedical signal processor applying the Cross-Frequency-Coupling (CFC) algorithm, which includes designing of FIR and IIR Band-pass filters, Hilbert filters, and Coordinate Rotation Digital Computer (CORDIC) circuits. The proposed system with reconfigurable resolution will be investigated in order to evaluate the benefit of introducing adaptive resolution sensors to save sensing and communication power. The proposed research significantly advances the state-of-the-art in integrated intelligent sensors by employing ideas from Delta-Sigma Modulation (DSM), Digital Signal Processing, and Integrated Circuits design in a cross-disciplinary fashion. Specifically, the proposed research will lead to a highly power-efficient circuit and system architecture by addressing the following objectives: 1) studying design and properties of Delta-Sigma based adders and coefficient multipliers for a theoretical understanding of how they can be applied to various circuit architectures, 2) investigating Delta-Sigma based digital filters and neural network systems and exploring the feasibility of applying the proposed circuits in machine learning, and 3) developing an integrated circuit of Cross-Frequency-Coupling algorithm for electroencephalograph (EEG) signal processing using Delta-Sigma digital signal processing circuits, and evaluating the system performance compared with conventional architecture.