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An ultra-wideband (UWB)-based cognitive radio (CR) is a promising technique to utilize 3.1–10.6-GHz band efficiently for high data-rate short-range wireless connectivity even in overcrowdedness of the frequency spectrum. Frequency synthesizers tailored to the UWB-based CR should provide wide lock range, multiple frequency bands, and low in-band spurs while consuming low power. This paper investigates a low power, wide lock range, integer-N frequency synthesizer approach with 132-MHz frequency step and 57 subbands in the UWB band. Two-stage supply regulated differential ring oscillator provides linear and wide tuning range with voltage controlled oscillator gain of 22.5 GHz/V and low supply-noise sensitivity. A modular true-single-phase-clock programmable divider achieves wide operating frequency range of 0.1–6.5 GHz and power efficiency of 6.25 GHz/mW by compensating leakage current at the dynamic nodes with conditional latches. Replica-based digital calibration reduces the current mismatch of the charge pump to <1% for low in-band reference spurs of <−60 dBc. A frequency synthesizer test chip was fabricated in 65-nm complementary metal–oxide–semiconductor technology. The active area of the frequency synthesizer is 0.12 mm2. It achieves <−30-dBc integrated phase noise and enables the UWB-based CR to get bit error rate of <10−3 while consuming <−10.1 mW. [ABSTRACT FROM AUTHOR]

Focuses on the application of PicoRadio in ultra-low power wireless networking. Power dissipation of wireless equipment; Applications of PicoRadio; Features of PicoRadio networks; Implementations of network organizations.

Presents a study which established a relationship between the basic properties of linear computations and optimizing transformations. Use of transformations in computer-aided design; Three algorithms discussed in the study.

Studies the complementary component of hardware-software codesign, algorithm selection. Process of algorithm selection; Analysis and classification of selection problem; Proposal of relaxation-based heuristic for throughput optimization.

A major hurdle in brain–machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10–100 μm scale, free-floating, independent sensor nodes, or neural dust , that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm) 3 neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions. [ABSTRACT FROM AUTHOR]

Traditional methods for radio frequency (RF) beamforming require significant amounts of power, making them difficult to deploy in some low-power applications. A detailed analysis of RF beamforming utilizing transmission-line transformers (TLTs) and balanced impedance phase shifters (BIPSs) is presented. This technique enables ultralow-power RF beamformers by: 1) leveraging the RF feed lines to implement a low-loss TLT for signal combining and 2) using the balanced impedance tuning of shunt passives to implement simple, low-loss phase shifters. We highlight some of the key advantages of this beamforming approach for low-power systems and show how this idea can be expanded to arrays of more than two antenna elements by introducing a crossover switch into the phase shifters to extend the beam-steering range. In addition, a thorough comparison between the theoretical and measured performances of a simplified prototype system is provided for key system metrics, including loss and bandwidth. The prototype system is a fully passive 2-GHz two-element RF beamformer, utilizing TLTs and BIPS, realized with off-the-shelf components and a standard FR4 PCB. [ABSTRACT FROM AUTHOR]

Surface-mounted parallel-plate capacitors (PPCs) can be used as transducing elements in wireless communication, for example, in human-body-dedicated applications, such as healthcare monitoring or activity tracking, which require robust wireless communications and user-convenient implementation. In this context, PPCs are an attractive element due to their mobility, noninvasive positioning, and small form factors. Moreover, they are relatively easy to analyze with a simple design. This work studies the use of PPCs on dielectric, cylindrical waveguides as a precursor to the relatively complex electromagnetic environment that the human body is. The electromagnetic fields surrounding PPCs placed on a dielectric medium are described theoretically. This theory involves a combination of surface waves, inductive coupling, and quasi-static coupling between PPCs. In dielectric (cylindric) waveguides, the material’s dimensions and frequency of excitation dictate whether propagation of certain modes can occur. This article shows, on one hand, that these modes can be excited by a surface-mounted waveguide coupler such as PPCs, and on the other hand, that the associated propagation is governed by modal characteristics. [ABSTRACT FROM AUTHOR]

Brain-inspired hyperdimensional (HD) computing models cognition by exploiting properties of high dimensional statistics—high-dimensional vectors, instead of working with numeric values used in contemporary processors. A fundamental weakness of existing HD computing algorithms is that they require to use floating point models in order to provide acceptable accuracy on realistic classification problems. However, working with floating point values significantly increases the HD computation cost. To address this issue, we proposed QuantHD, a novel framework for quantization of HD computing model during training. QuantHD enables HD computing to work with a low-cost quantized model (binary or ternary model) while providing a similar accuracy as the floating point model. We accordingly propose an FPGA implementation which accelerates HD computing in both training and inference phases. We evaluate QuantHD accuracy and efficiency on various real-world applications, and observe that QuantHD can achieve on average 17.2% accuracy improvement as compared to the existing binarized HD computing algorithms which provide a similar computation cost. In terms of efficiency, QuantHD FPGA implementation can achieve on average 42.3 × and 4.7 × (34.1 × and 4.1 × ) energy efficiency improvement and speedup during inference (training) as compared to the state-of-the-art HD computing algorithms. [ABSTRACT FROM AUTHOR]

The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as brain–computer interfaces. We describe the use of HD computing to classify electroencephalography (EEG) error-related potentials for noninvasive brain–computer interfaces. Our algorithm naturally encodes neural activity recorded from 64 EEG electrodes to a single temporal–spatial hypervector without requiring any electrode selection process. This hypervector represents the event of interest, can be analyzed to identify the most discriminative electrodes, and is used for recognition of the subject's intentions. Using the full set of training trials, HD computing achieves on average 5% higher single-trial classification accuracy compared to a conventional machine learning method on this task (74.5% vs. 69.5%) and offers further advantages: (1) Our algorithm learns fast: using only 34% of training trials it achieves an average accuracy of 70.5%, surpassing the conventional method. (2) Conventional method requires prior domain expert knowledge, or a separate process, to carefully select a subset of electrodes for a subsequent preprocessor and classifier, whereas our algorithm blindly uses all 64 electrodes, tolerates noises in data, and the resulting hypervector is intrinsically clustered into HD space; in addition, most preprocessing of the electrode signal can be eliminated while maintaining an average accuracy of 71.7%. [ABSTRACT FROM AUTHOR]

Summary The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies. [ABSTRACT FROM AUTHOR]

Brain-machine interface (BMI) technology has tremendous potential to revolutionize healthcare by greatly improving the quality of life of millions of people suffering from a wide variety of neurological conditions. Radio-frequency identification (RFID)-inspired backscattering is a promising approach for wireless powering of miniature neural sensors required in BMI interfaces. We analyze the functionality of millimeter-size loop antennas in the wireless powering of miniature cortical implants through measurements in a human head equivalent liquid phantom and in the head of a postmortem pig. For the first time, we present the design and measurement of a miniature 1\times 1\times 1\ mm^3 backscattering device based on a cubic loop connected with an RFID integrated circuit (IC). Our measurement results show that this very small loop receives sufficient electromagnetic power to activate the IC when the device is implanted in a pig's head. This demonstrates the feasibility of extremely small implant antennas in challenging wireless biomedical systems. [ABSTRACT FROM PUBLISHER]

Sensor data can be wirelessly transmitted from simple, battery-less tags using Radio Frequency Identification (RFID). RFID sensor tags consist of an antenna, a radio frequency integrated circuit chip (RFIC), and at least one sensor. An ideal tag can communicate over a long distance and be seamlessly integrated onto everyday objects. However, miniaturized antenna designs often have lower performance. Here we demonstrate compact, flexible sensor tags with read range comparable to that of conventional rigid tags. We compare fabrication techniques for flexible antennas and demonstrate that screen and stencil printing are both suitable for fabricating antennas; these different techniques are most useful at different points in the design cycle. We characterize two versions of flexible, screen printed folded dipoles and a meandered monopole operating in the 915 MHz band. Finally, we use these antennas to create passive sensor tags and demonstrate over the air communication of sensor data. These tags could be used to form a network of printed, flexible, passive, interactive sensor tags. [ABSTRACT FROM AUTHOR]

While there exists a wide variety of radio frequency (RF) technologies amenable for usage in Wireless Body Area Networks (WBANs), which have been studied separately before, it is currently still unclear how their performance compares in true on-body scenarios. In this paper, a single reference on-body scenario—that is, propagation along the arm—is used to experimentally compare six distinct RF technologies (between 420 MHz and 2.4 GHz) in terms of path loss. To further quantify on-body path loss, measurements for five different on-body scenarios are presented as well. To compensate for the effect of often large path losses, two mitigation strategies to (dynamically) improve on-body links are introduced and experimentally verified: beam steering using a phased array, and usage of on-body RF repeaters. The results of this study can serve as a tool for WBAN designers to aid in the selection of the right RF frequency and technology for their application. [ABSTRACT FROM AUTHOR]