748 research outputs found

    A Blind Interference Canceling Technique for Two-Stage Harmonic Rejection in Down-mixers

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    This paper presents practical experiments on a harmonic rejection down-mixer, which offers up to 75 dB of harmonic rejection, without an RF filter. The down-mixer uses a two-stage approach; the first stage is an analog multi-path/multiphase harmonic rejection mixer followed by a second stage providing additional harmonic rejection based on blind adaptive interference canceling in the discrete-time domain. The aim is to show its functional operation. The canceler cannot cope with DC offsets. The DC offsets are removed by highpass filters. The signal paths used to obtain an estimate of the interference must be designed to provide as much attenuation of the desired signal as possible. Front-end nonlinearities and DC offsets are discussed

    Hardware Learning in Analogue VLSI Neural Networks

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    Reconfigurable Receiver Front-Ends for Advanced Telecommunication Technologies

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    The exponential growth of converging technologies, including augmented reality, autonomous vehicles, machine-to-machine and machine-to-human interactions, biomedical and environmental sensory systems, and artificial intelligence, is driving the need for robust infrastructural systems capable of handling vast data volumes between end users and service providers. This demand has prompted a significant evolution in wireless communication, with 5G and subsequent generations requiring exponentially improved spectral and energy efficiency compared to their predecessors. Achieving this entails intricate strategies such as advanced digital modulations, broader channel bandwidths, complex spectrum sharing, and carrier aggregation scenarios. A particularly challenging aspect arises in the form of non-contiguous aggregation of up to six carrier components across the frequency range 1 (FR1). This necessitates receiver front-ends to effectively reject out-of-band (OOB) interferences while maintaining high-performance in-band (IB) operation. Reconfigurability becomes pivotal in such dynamic environments, where frequency resource allocation, signal strength, and interference levels continuously change. Software-defined radios (SDRs) and cognitive radios (CRs) emerge as solutions, with direct RF-sampling receivers offering a suitable architecture in which the frequency translation is entirely performed in digital domain to avoid analog mixing issues. Moreover, direct RF- sampling receivers facilitate spectrum observation, which is crucial to identify free zones, and detect interferences. Acoustic and distributed filters offer impressive dynamic range and sharp roll off characteristics, but their bulkiness and lack of electronic adjustment capabilities limit their practicality. Active filters, on the other hand, present opportunities for integration in advanced CMOS technology, addressing size constraints and providing versatile programmability. However, concerns about power consumption, noise generation, and linearity in active filters require careful consideration.This thesis primarily focuses on the design and implementation of a low-voltage, low-power RFFE tailored for direct sampling receivers in 5G FR1 applications. The RFFE consists of a balun low-noise amplifier (LNA), a Q-enhanced filter, and a programmable gain amplifier (PGA). The balun-LNA employs noise cancellation, current reuse, and gm boosting for wideband gain and input impedance matching. Leveraging FD-SOI technology allows for programmable gain and linearity via body biasing. The LNA's operational state ranges between high-performance and high-tolerance modes, which are apt for sensitivityand blocking tests, respectively. The Q-enhanced filter adopts noise-cancelling, current-reuse, and programmable Gm-cells to realize a fourth-order response using two resonators. The fourth-order filter response is achieved by subtracting the individual response of these resonators. Compared to cascaded and magnetically coupled fourth-order filters, this technique maintains the large dynamic range of second-order resonators. Fabricated in 22-nm FD-SOI technology, the RFFE achieves 1%-40% fractional bandwidth (FBW) adjustability from 1.7 GHz to 6.4 GHz, 4.6 dB noise figure (NF) and an OOB third-order intermodulation intercept point (IIP3) of 22 dBm. Furthermore, concerning the implementation uncertainties and potential variations of temperature and supply voltage, design margins have been considered and a hybrid calibration scheme is introduced. A combination of on-chip and off-chip calibration based on noise response is employed to effectively adjust the quality factors, Gm-cells, and resonance frequencies, ensuring desired bandpass response. To optimize and accelerate the calibration process, a reinforcement learning (RL) agent is used.Anticipating future trends, the concept of the Q-enhanced filter extends to a multiple-mode filter for 6G upper mid-band applications. Covering the frequency range from 8 to 20 GHz, this RFFE can be configured as a fourth-order dual-band filter, two bandpass filters (BPFs) with an OOB notch, or a BPF with an IB notch. In cognitive radios, the filter’s transmission zeros can be positioned with respect to the carrier frequencies of interfering signals to yield over 50 dB blocker rejection

    Fingerprinting Smart Devices Through Embedded Acoustic Components

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    The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

    Energy-Efficient Wireless Circuits and Systems for Internet of Things

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    As the demand of ultra-low power (ULP) systems for internet of thing (IoT) applications has been increasing, large efforts on evolving a new computing class is actively ongoing. The evolution of the new computing class, however, faced challenges due to hard constraints on the RF systems. Significant efforts on reducing power of power-hungry wireless radios have been done. The ULP radios, however, are mostly not standard compliant which poses a challenge to wide spread adoption. Being compliant with the WiFi network protocol can maximize an ULP radio’s potential of utilization, however, this standard demands excessive power consumption of over 10mW, that is hardly compatible with in ULP systems even with heavy duty-cycling. Also, lots of efforts to minimize off-chip components in ULP IoT device have been done, however, still not enough for practical usage without a clean external reference, therefore, this limits scaling on cost and form-factor of the new computer class of IoT applications. This research is motivated by those challenges on the RF systems, and each work focuses on radio designs for IoT applications in various aspects. First, the research covers several endeavors for relieving energy constraints on RF systems by utilizing existing network protocols that eventually meets both low-active power, and widespread adoption. This includes novel approaches on 802.11 communication with articulate iterations on low-power RF systems. The research presents three prototypes as power-efficient WiFi wake-up receivers, which bridges the gap between industry standard radios and ULP IoT radios. The proposed WiFi wake-up receivers operate with low power consumption and remain compatible with the WiFi protocol by using back-channel communication. Back-channel communication embeds a signal into a WiFi compliant transmission changing the firmware in the access point, or more specifically just the data in the payload of the WiFi packet. With a specific sequence of data in the packet, the transmitter can output a signal that mimics a modulation that is more conducive for ULP receivers, such as OOK and FSK. In this work, low power mixer-first receivers, and the first fully integrated ultra-low voltage receiver are presented, that are compatible with WiFi through back-channel communication. Another main contribution of this work is in relieving the integration challenge of IoT devices by removing the need for external, or off-chip crystals and antennas. This enables a small form-factor on the order of mm3-scale, useful for medical research and ubiquitous sensing applications. A crystal-less small form factor fully integrated 60GHz transceiver with on-chip 12-channel frequency reference, and good peak gain dual-mode on-chip antenna is presented.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162975/1/jaeim_1.pd

    The Human Keyboard

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    Most products that implement features for user interactions utilize buttons or switches for the user to command to select actions to perform. Such designs are typically controlled with direct motions, such as touch or voice and are seldom designed in consideration of those unable to utilize direct control. In this project, we designed technology that reads naturally occuring biosignals from the body, which then can be apply those signals with any interface. For our specific application in this project, we decided to implement a keyboard. Instead of teaching the fingers how to type on a mechanical keyboard, the body can designate an action with a more native motion. We aim to take ‘body language’ to the next level. By making the human body the centerpiece, and building the interconnects between people (reading and comprehending EMG signals), we strive to create a more interconnected world. Using our custom implementation of an analog to digital converter, the amplitude of EMG signals at carefully placed muscle probes are collected and translated into a digital signal. The resulting signal values are sent to a remote server where key characteristics are calculated. The backend of the system consists of a mathematical model that continuously uses these calculated characteristics to re-parameterize itself for recognition. After the signals are recognized, they are assigned an appropriate output at the user’s request. This document includes the requirements, design, use cases, risk tables, workflow and the architecture for the device we developed

    Study of substrate noise and techniques for minimization

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 155-158).This thesis presents a study of the effects of substrate noise on analog circuits in mixed-signal chips and techniques for minimizing these harmful effects on sensitive analog circuits. A microchip built in a 0.25um CMOS epitaxial process was designed, fabricated, and tested for this research. Through the use of an on-chip sampling scope, the effect of substrate noise generated by digital inverters with coupling capacitors to the substrate on analog circuits was characterized. Substrate noise coupled into a representative analog circuit, a switched capacitor delta-sigma modulator primarily through the asymmetrical parasitics of the input sampling circuit. Furthermore, since some of the parasitics are nonlinear with input voltage, substrate noise couples into the analog circuits producing an input signal dependent component and an input signal independent component. The substrate noise, with decay time constants of a few nanoseconds and ringing frequencies of few hundred megahertz, can decrease analog circuit performance. In the case of a delta-sigma modulator, substrate noise caused the signal to noise power ratio to decrease by more than 18dB, 3 bits in terms of analog-to-digital converter metrics. In addition, two techniques of minimizing the substrate noise and its effects were explored. The first used a replica delta-sigma modulator on the same chip to subtract the effects of substrate noise from the original delta-sigma modulator. This method proved useful for removing input signal independent substrate noise, but not input signal dependent substrate noise which dominates in-band noise for large input signal magnitudes. The second technique involved an active substrate noise cancellation system.(cont.) A discrete time feedback loop senses the substrate noise, processes it through a filter, and uses an array of digital inverters to cancel the substrate noise. The principal advantages of this technique are the shaping of substrate noise through a designed filter without a significant power penalty and design independence from the analog and digital components. Measured data shows that this technique is capable of over 20dB reduction in substrate noise on the substrate voltage itself. Measured data also shows over 10dB improvement in SNDR of the delta-sigma modulator in certain cases.by Mark Shane Peng.Ph.D
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