Speech Processing Front-end in Low-power Hardware

The objective of this work is to develop analog integrated circuits to serve as low-power auditory front-ends in signal processing systems. An analog front-end can be used for feature-extraction to reduce the requirements of the digital back-end, or to detect and call attention to compelling characteristics of a signal while the back-end is in sleep mode. Such a front-end should be advantageous for speech recognition, noise suppression, auditory scene analysis, hearing prostheses, biological modeling, or hardware-based event detection.;This work presents a spectral decomposition system, which consists of a bandpass filter bank with sub-band magnitude detection. The bandpass filter is low-power and each channel can be individually programmed for different quality factors and passband gains. The novel magnitude detector has a 68 decibel dynamic range, excellent tracking capability, and consumes less than a microwatt of power. The system, which was fabricated in a 0.18 micron process, consists of a 16-channel filter bank and a variety of sub-band computational elements

Low-Power Analog Circuits for Sub-Band Speech Processing

The need for efficient electronics has been increasing by the day, as have the constraints on power and size of the devices. Also the increase in use of mobile and wearable electronics has been leading to innovative methods to conserve power and increase functionality. The traditional approach of signal processing heavily relies on the Digital Signal Processing (DSP) hardware to perform most of the tasks, which has lead to power-hungry circuits. Use of analog front-end devices could prove to be efficient, since most of the real-world data is analog and since the DSP could be spared for more application-specific tasks within the system, thereby resulting in more efficient mixed-signal systems.;The focus in this work is to develop an analog front-end for speech-processing applications with inspiration from biology, and trying to mimic human auditory perception techniques. The circuits are designed in 600nm, 350nm and 180nm CMOS processes and are biased in the sub-threshold region to consume low-power. Also, various modules of the system are connected using multiplexing circuits to allow post-fabrication reconfigurability to suit various applications. These circuits are biased using a network of floating-gate transistors which allow reconfigurability and increased bias accuracy. This thesis mainly describes two modules of the analog front-end used for speech processing: derivative circuit and voltage-mode subtractor circuit, which are used for processing spectrally decomposed signals. These circuits could be used for applications like audio analysis or event detection

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Biomimetic Based Applications

The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions