12 research outputs found
A Low-Power BFSK/OOK Transmitter for Wireless Sensors
In recent years, significant improvements in semiconductor technology have allowed consistent development of wireless chipsets in terms of functionality and form factor. This has opened up a broad range of applications for implantable wireless sensors and telemetry devices in multiple categories, such as military, industrial, and medical uses. The nature of these applications often requires the wireless sensors to be low-weight and energy-efficient to achieve long battery life. Among the various functions of these sensors, the communication block, used to transmit the gathered data, is typically the most power-hungry block. In typical wireless sensor networks, transmission range is below 10 meters and required radiated power is below 1 milliwatt. In such cases, power consumption of the frequency-synthesis circuits prior to the power amplifier of the transmitter becomes significant. Reducing this power consumption is currently the focus of various research endeavors. A popular method of achieving this goal is using a direct-modulation transmitter where the generated carrier is directly modulated with baseband data using simple modulation schemes.
Among the different variations of direct-modulation transmitters, transmitters using unlocked digitally-controlled oscillators and transmitters with injection or resonator-locked oscillators are widely investigated because of their simple structure. These transmitters can achieve low-power and stable operation either with the help of recalibration or by sacrificing tuning capability. In contrast, phase-locked-loop-based (PLL) transmitters are less researched. The PLL uses a feedback loop to lock the carrier to a reference frequency with a programmable ratio and thus achieves good frequency stability and convenient tunability.
This work focuses on PLL-based transmitters. The initial goal of this work is to reduce the power consumption of the oscillator and frequency divider, the two most power-consuming blocks in a PLL. Novel topologies for these two blocks are proposed which achieve ultra-low-power operation. Along with measured performance, mathematical analysis to derive rule-of-thumb design approaches are presented. Finally, the full transmitter is implemented using these blocks in a 130 nanometer CMOS process and is successfully tested for low-power operation
Design of high performance frequency synthesizers in communication systems
Frequency synthesizer is a key building block of fully-integrated wireless communication
systems. Design of a frequency synthesizer requires the understanding of
not only the circuit-level but also of the transceiver system-level considerations. This
dissertation presents a full cycle of the synthesizer design procedure starting from the
interpretation of standards to the testing and measurement results.
A new methodology of interpreting communication standards into low level circuit
specifications is developed to clarify how the requirements are calculated. A
detailed procedure to determine important design variables is presented incorporating
the fundamental theory and non-ideal effects such as phase noise and reference
spurs. The design procedure can be easily adopted for different applications.
A BiCMOS frequency synthesizer compliant for both wireless local area network
(WLAN) 802.11a and 802.11b standards is presented as a design example. The two
standards are carefully studied according to the proposed standard interpretation
method. In order to satisfy stringent requirements due to the multi-standard architecture,
an improved adaptive dual-loop phase-locked loop (PLL) architecture is
proposed. The proposed improvements include a new loop filter topology with an
active capacitance multiplier and a tunable dead zone circuit. These improvements
are crucial for monolithic integration of the synthesizer with no off-chip components.
The proposed architecture extends the operation limit of conventional integerN type synthesizers by providing better reference spur rejection and settling time
performance while making it more suitable for monolithic integration. It opens a
new possibility of using an integer-N architecture for various other communication
standards, while maintaining the benefit of the integer-N architecture; an optimal
performance in area and power consumption
Design of sigma-delta modulators for analog-to-digital conversion intensively using passive circuits
This thesis presents the analysis, design implementation and experimental evaluation of passiveactive discrete-time and continuous-time Sigma-Delta (ΣΔ) modulators (ΣΔMs) analog-todigital converters (ADCs).
Two prototype circuits were manufactured. The first one, a discrete-time 2nd-order ΣΔM, was designed in a 130 nm CMOS technology. This prototype confirmed the validity of the ultra incomplete settling (UIS) concept used for implementing the passive integrators. This circuit, clocked at 100 MHz and consuming 298 μW, achieves DR/SNR/SNDR of 78.2/73.9/72.8 dB, respectively, for a signal bandwidth of 300 kHz. This results in a Walden FoMW of 139.3 fJ/conv.-step and Schreier FoMS of 168 dB.
The final prototype circuit is a highly area and power efficient ΣΔM using a combination of a cascaded topology, a continuous-time RC loop filter and switched-capacitor feedback paths. The modulator requires only two low gain stages that are based on differential pairs. A systematic design methodology based on genetic algorithm, was used, which allowed decreasing the circuit’s sensitivity to the circuit components’ variations. This continuous-time, 2-1 MASH ΣΔM has been designed in a 65 nm CMOS technology and it occupies an area of just 0.027 mm2. Measurement results show that this modulator achieves a peak SNR/SNDR of 76/72.2 dB and DR of 77dB for an input signal bandwidth of 10 MHz, while dissipating 1.57 mW from a 1 V power supply voltage. The ΣΔM achieves a Walden FoMW of 23.6 fJ/level and a Schreier FoMS of 175 dB. The innovations proposed in this circuit result, both, in the reduction of the power consumption and of the chip size. To the best of the author’s knowledge the circuit achieves the lowest Walden FOMW for ΣΔMs operating at signal bandwidth from 5 MHz to 50 MHz reported to date
Collective analog bioelectronic computation
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 677-710).In this thesis, I present two examples of fast-and-highly-parallel analog computation inspired by architectures in biology. The first example, an RF cochlea, maps the partial differential equations that describe fluid-membrane-hair-cell wave propagation in the biological cochlea to an equivalent inductor-capacitor-transistor integrated circuit. It allows ultra-broadband spectrum analysis of RF signals to be performed in a rapid low-power fashion, thus enabling applications for universal or software radio. The second example exploits detailed similarities between the equations that describe chemical-reaction dynamics and the equations that describe subthreshold current flow in transistors to create fast-and-highly-parallel integrated-circuit models of protein-protein and gene-protein networks inside a cell. Due to a natural mapping between the Poisson statistics of molecular flows in a chemical reaction and Poisson statistics of electronic current flow in a transistor, stochastic effects are automatically incorporated into the circuit architecture, allowing highly computationally intensive stochastic simulations of large-scale biochemical reaction networks to be performed rapidly. I show that the exponentially tapered transmission-line architecture of the mammalian cochlea performs constant-fractional-bandwidth spectrum analysis with O(N) expenditure of both analysis time and hardware, where N is the number of analyzed frequency bins. This is the best known performance of any spectrum-analysis architecture, including the constant-resolution Fast Fourier Transform (FFT), which scales as O(N logN), or a constant-fractional-bandwidth filterbank, which scales as O (N2).(cont.) The RF cochlea uses this bio-inspired architecture to perform real-time, on-chip spectrum analysis at radio frequencies. I demonstrate two cochlea chips, implemented in standard 0.13m CMOS technology, that decompose the RF spectrum from 600MHz to 8GHz into 50 log-spaced channels, consume < 300mW of power, and possess 70dB of dynamic range. The real-time spectrum analysis capabilities of my chips make them uniquely suitable for ultra-broadband universal or software radio receivers of the future. I show that the protein-protein and gene-protein chips that I have built are particularly suitable for simulation, parameter discovery and sensitivity analysis of interaction networks in cell biology, such as signaling, metabolic, and gene regulation pathways. Importantly, the chips carry out massively parallel computations, resulting in simulation times that are independent of model complexity, i.e., O(1). They also automatically model stochastic effects, which are of importance in many biological systems, but are numerically stiff and simulate slowly on digital computers. Currently, non-fundamental data-acquisition limitations show that my proof-of-concept chips simulate small-scale biochemical reaction networks at least 100 times faster than modern desktop machines. It should be possible to get 103 to 106 simulation speedups of genome-scale and organ-scale intracellular and extracellular biochemical reaction networks with improved versions of my chips. Such chips could be important both as analysis tools in systems biology and design tools in synthetic biology.by Soumyajit Mandal.Ph.D
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Doppler Encoded Excitation Patterning (DEEP) Microscopy
Traditional optical imaging systems rely on lenses and spatially-resolved detection to probe distinct locations on the object. We develop a novel computational approach to 2D and 3D imaging that instead measures the object\u27s spatial Fourier transform using a single-element detector and without requiring precision optics. This wide-field technique can be used to image biological and synthetic structures in fluoresced or scattered light using coherent or broadband illumination. It employs dynamic structured illumination, acousto-optics, RF electronics, and tomographic algorithms to circumvent several trade-offs in conventional imaging, such as the dependence of the optical transfer function on the imaging lenses and the coupling of resolution and depth of field.
We use Fourier optics concepts to derive the dynamic optical transfer function, evaluate different Fourier sampling strategies, and investigate and compare tomographic algorithms for 2D and 3D image synthesis. We also develop conceptual and analytical models to describe imaging of fluorescent as well as amplitude and phase scattering objects, the effects of broadband and spatially-incoherent illumination, and nonlinear wide-field super-resolution imaging. We consider sources of noise, analyze and simulate SNR behavior for several types of noise and Fourier sampling strategies, and compare the sensitivity of the technique to conventional imaging. We describe several experimental proof-of-concept systems and present two-dimensional high-resolution tomographic image reconstructions in both scattered and fluoresced light demonstrating a thousandfold improvement in the depth of field compared to conventional lens-based microscopy. Finally, we explore approaches for high-speed Fourier sampling and propose several related sensing techniques, including wide-field fluorescence imaging in scattering media
Personality Identification from Social Media Using Deep Learning: A Review
Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed