Yehia Massoud

A low-loss rectifier unit for inductive-powering of biomedical implants

Biomedical implants have been developed in the recent years with a focus for continuous and real-time monitoring of physiological parameters. Battery-less operation of the implanted unit requires energy harvesting from an inductive link or from the neighboring environment. For efficient conversion of harvested energy to a usable DC level, a rectifier block is employed. However conventional CMOS full bridge rectifier incurs a significant amount of power loss and lowers the overall efficiency of the powering system. In this work a cross-coupled MOSFET based LC oscillator structure has been presented as a modified rectifier circuit. Cross-coupled structure minimizes the loss of the MOS switches and LC tank circuit boosts up the output DC level. The rectifier unit has been designed and simulated using 0.5-µm standard CMOS process. For simulation purposes, different biomedical frequency bands are used to validate the effectiveness of the proposed circuit. Simulation results show that the proposed rectifier circuit can achieve 75% PCE compared to the conventional full bridge CMOS rectifier of only 3% PCE.

A Time-Varying Approach to Circuit Modeling of Plasmonic Nanospheres Using Radial Vector Wave Functions

Recent research has demonstrated the use of plasmonic nanoparticles (e.g., a silver or a gold nanosphere) as circuit elements. In these metallic nanoparticles, an electromagnetic wave at optical frequencies excites conduction electrons resulting in a plasmon resonance. The derived values of circuit components are based on the observation that the small size of the particle compared to the wavelength leads to lumped-impedance representations under the quasi-static approximation. In this paper, we show that circuit representations based on quasi-static approximations can often result in large errors for typical nanosphere sizes. To remedy this issue, we present a new approach based on time-varying fields, which uses vector wave functions to explicitly derive accurate resonance frequency and impedance expressions for these metallic nanospheres at and around the plasmon resonance. In particular, the proposed approach accurately predicts the dependence of the resonance frequency on the size of the nanoparticle and yields more accurate expressions for the equivalent L and C lumped elements compared to the quasi-static model. The new impedance approach is still compatible with the process of cascading nanoparticles in series and parallel combinations to synthesize more complex nanocircuits. A comparison with Mie and full-wave finite-element simulation results demonstrates that our model provides accurate closed-form expressions, thereby extending the range of the impedance representation to larger radii nanoparticles.

Power-loss reduction of a MOSFET cross-coupled rectifier by employing zero-voltage switching

Ubiquitous monitoring of sensor data and long term reliable operation of sensor units have been studied extensively either for environmental monitoring or for biomedical applications. Long term operation of sensor units requires continuous wireless signal at the output. The proposed rectifier unit is designed and simulated using 0.5-μm standard CMOS process. Simulation results show that power supply from an external source to avoid unwieldy wires or periodic battery replacements. Inductive-power transfer, as a suitable way of driving the sensor electronics, needs a high efficiency rectifier unit to convert the harvested wireless energy into a usable DC level. However, conventional full-wave bridge rectifier with a lower output voltage and a significant power loss lowers the overall efficiency of the inductive-link system. In this paper, a class-E type zero-voltage-switching structure is presented to achieve a high efficiency rectifier circuit. The symmetrical differential class-E switching structures are driven by differential AC signals that result in a low-loss full-wave rectified the proposed rectifier circuit can achieve more than 76% power conversion efficiency for an input AC signal of 7 MHz frequency with signal amplitude of 2 V (peak).

Compressive sensing based classification of intramuscular electromyographic signals

Upper extremity prosthetic limbs have succeeded in providing people affected by disabilities such as amputation or paralysis the ability to perform simple manual tasks. Typically, prosthetic limbs are controlled by electromyography (EMG) signals read from the muscles of the patient. As the capabilities of prosthetic hands improve toward those of the intact human hand, their mechanical complexity increases, making the development of advanced techniques for reading and interpreting these EMG signals, while pushing down the power consumption of the sensing device is becoming more critical. In this work, we investigate the classification EMG signals acquired using the technique of compressive sensing, which provides solutions for reducing sensor power and complexity by relaxing the constraints posed by the Shannon sampling theorem on the rate at which the analog signals, in general, should be sampled for preserving the signals information. We show that using compressive sensing, we can reduce the sampling rate by at least 10 times while maintaining classification accuracy higher than 95%.

A memristor-based random modulator for compressive sensing systems

Memristors promise to allow high levels of compaction in computing systems because these elements combine memory and switching functionality. This can be utilized to overcome some of the hardware challenges in compressive sensing architectures. In this paper, we propose a compressive sensing system architecture that uses a memristor-based random modulator. The gains of using such a memristor-based modulator mainly stem from replacing memory blocks and many of the switching components typically used in compressive sensing. We discuss some of these benefits and the design considerations that need to be addressed in memristor-based compressive sensing architectures.

Performance analysis of random demodulators with M-sequences and Kasami sequences

The theory of compressive sensing has recently been utilized to develop sub-Nyquist communication receivers that can reconstruct the input signal using sub-Nyquist sampling rates. Such samples are acquired randomly by projecting the input signal on random signals. Practically, these random signals can be generated by digital pseudo random signal generators, and the properties of these signals highly affect the reconstruction quality of the receiver. In this paper, we study the performance of the random demodulator, a compressive sampling based receiver, with two types of random sequences that are practical to implement: M-sequences generated by means of a linear feedback shift register, and Kasami sequences. We show that a random demodulator with a Kasami sequence generally outperforms that with an M-sequence in terms of minimum sampling rate and minimum sparsity levels for successful reconstruction.

Parasitic-Aware Design of Integrated DC–DC Converters With Spiral Inductors

Integrated dc-dc converters are widely used for the realization of power converters suitable for energy harvesting and computing systems. In such systems, integrated converters are the ideal candidate due to their small size and low power consumption. Integrated dc-dc converters typically use spiral inductors to achieve high levels of integration and performance. However, under scenarios where energy scarcity is paramount, the integrated converter with spiral inductor requires careful modeling and optimization to achieve maximum efficiency. In this paper, we provide a parasitic aware design technique that takes into account the spiral inductor resistance as well as the switching parasitics, while utilizing numerical methods, to arrive at converter designs with robust performance and optimal efficiency. We translate the various system constraints into design rules and use them to formulate the various design parameters, from switching frequency to duty cycle, in terms of the inductance. We study how these parameters are dependent on each other, giving rise to multiple tradeoffs. We also present a method for minimizing power losses using optimization techniques, which leverage our formulation of the system parameters in terms of inductance.

Compressive sensing with sub-Nyquist clocks using frequency division multiplexed random sequences

Compressive sensing is a newly emerging theory that provides the means to recover a signal from samples obtained at a sub-Nyquist rate. This in turn leads to tremendous reduction in power consumption of receivers because of the direct correlations between the sampling rate and power consumption of analog-to-digital converters, which constitutes a considerable amount of the receivers power consumption. In this paper, we propose an architecture for the random demodulator, which is a compressive sensing based receiver, that requires a sub-Nyquist digital clock rather than a Nyquist rate clock. In a conventional random demodulator, the input signal is mixed with a pseudo random sequence, running at the signals Nyquist rate and then integrated and sampled. In order to overcome this constraint, we propose the use of frequency division multiplexing, in the analog domain, of many digitally generated random sequences running at a sub-Nyquist rate to generate an effective random sequence. The major gain in this approach is that the restricting requirement of having a fast clock is levitated, thus allowing to extend the capabilities of the random demodulator beyond the restrictions imposed by the digital technology.

On the effect of width of metallic armchair graphene nanoribbons in plasmonic waveguide applications

Graphene demonstrates superior electronic properties that make it a potential candidate for future electronic systems. Graphene, additionally, support surface plasmon oscillations, which in turn makes graphene attractive for optoelectronics because of its planar structure and its conductivity properties. When a graphene layer is confined in one dimension, a graphene nanoribbon arises, with proerties differing from the original two dimensional graphene. In this paper we study the main properties of plasmon oscillations on metallic armchair graphene nanoribbons using the dielectric function obtained through the random phase approximation. We mainly study the effect of the graphene nanoribbon width on the plasmon propagation length using numerical techniques to extract the dispersion relation of graphene nanoribbons and the propagation properties of palsmons on graphene nanoribbons.

A random demodulator with a software-based integrator resetting scheme

The random demodulator architecture is a compressive sensing based receiver that allows the reconstruction of frequency-sparse signals from measurements acquired at a rate below the signals Nyquist rate. This in turn results in tremendous power savings in receivers because of the direct correlation between the power consumption of analog-to-digital converters (ADCs) in communication receivers and the sampling rate at which these ADCs operate. In this paper, we propose a random demodulator with a software-based integrator resetting scheme that does not use a switch to reset the integrator as in the conventional random demodulator system, but rather modifies the random signal so that the integrator is reset by zeroing the input. We show that the proposed system is equivalent to the conventional random demodulator, but is more practical to implement because of the many artifacts presented by switches.