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Temporary cardiac pacemakers used in periods of need during surgical recovery involve percutaneous leads and externalized hardware that carry risks of infection, constrain patient mobility and may damage the heart during lead removal. Here we report a leadless, battery-free, fully implantable cardiac pacemaker for postoperative control of cardiac rate and rhythm that undergoes complete dissolution and clearance by natural biological processes after a defined operating timeframe. We show that these devices provide effective pacing of hearts of various sizes in mouse, rat, rabbit, canine and human cardiac models, with tailored geometries and operation timescales, powered by wireless energy transfer. This approach overcomes key disadvantages of traditional temporary pacing devices and may serve as the basis for the next generation of postoperative temporary pacing technology. A biodegradable pacemaker without external leads improves the safety of temporary cardiac pacing.

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes.Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue.The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor.The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models. Multi-frequency tissue complex electrical impedance measurements are used to provide input data to the data fusion schemes. Our results show that the fusion schemes significantly decrease both the spread of residuals and the mean of the residuals for depth estimation. Thus, data fusion can be a significant tool for use in improving the performance of ML-based monitoring for RFA.

Objective: Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance. Methods: Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression. Results: Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models. The root mean square errors were 2 mm for NN and 0.5 mm for TEs for previous statistical models and the root mean square errors were 0.4 mm for NN and 0.04 mm for TEs for the statistical models presented in this paper. Simulation ablation performance showed a mean difference against physical measurements of $0.5 \pm 0.2$ mm for the NN-based depth estimation method and $0.7 \pm 0.4$ mm for the TE-based depth estimation method. Conclusion: The results show that multi-frequency data significantly improves the depth estimation performance of the statistical models. Significance: The RFA lesion depth estimation methods presented in this work achieve millimeter-resolution accuracy with soft real-time performance on an ARMv7-based embedded system for potential translation to clinical RFA technologies.

The modulation of current through an electrostatically formed nanowire (EFN) is controlled by the voltage on the four input gates. The behavior of this multi-gate transistor can be interpreted as a complex four-input switching process, enabling the computation of multiple-input threshold logic functions using a single device. We have therefore created a novel threshold logic family leveraging these unique capabilities that enables the efficient computation of complex logic functions. Experimental and simulation data are provided to demonstrate feasibility and evaluate behavior. This logic family overcomes the challenge posed by the input-output voltage mismatch inherent to EFNs and produces circuits with one-eighth the number of active logic devices and one-quarter the number of transistors required by CMOS.

An increasingly-popular treatment for ablation of cancerous and non-cancerous masses is thermal ablation by radiofrequency joule heating. Real-time monitoring of the thermal tissue ablation process is essential in order to maintain the reliability of the treatment technique. Common methods for monitoring the extent of ablation have proven to be accurate, though they are time-consuming and often require powerful computers to run on, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the time to calculate the progress of ablation while keeping the accuracy of the conventional methods. Different setups were used to perform the ablation and collect impedance data at the same time and different ML algorithms were tested to predict the ablation depth in three dimensions, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm were able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5.5 s on conventional x86-64 computing hardware.

Background While electromagnetic interference is widely recognized as a cause of noise detected by cardiac implanted electronic devices (CIEDs), we report on two cases of inappropriate activation of magnet response mode due to magnets in continuous positive airway pressure (CPAP) masks. Methods Two patients with St. Jude Medical dual-chamber pacemakers (Assurity DR 2240; St. Jude Medical, St. Paul, MN, USA) were discovered on routine outpatient device interrogation to have magnet response events that correlated with nightly use of their CPAP masks. A magnet response event was reproduced by positioning one CPAP mask over the patient's pulse generator site. The magnetic flux density was measured using a Gauss meter. Results It was found that their CPAP masks had magnetic clips that were used to secure the headgears. These appeared to be transiently positioned within sufficient proximity of the patients' CIEDs to activate magnet response events during sleep. The measured magnetic field density adjacent to a mask magnet was 136 Gauss. Conclusion Clinicians managing CPAPs and CIEDs should be aware of magnetic interference with the potential for inappropriate activation of magnet response mode. CPAP masks that are secured with magnetic clips should be avoided in patients with CIEDs, since this can result in inappropriate asynchronous pacing in pacemakers or potential failure to deliver life-saving tachytherapies in patients with implanted cardioverter-defibrillators.

The cascading of logic gates is a critical challenge for the development of spintronic logic circuits. Here we propose the first logic family exploiting magnetoresistive bipolar spin-transistors to achieve a complete spintronic logic family in which logic gates can be cascaded. This logic family, emitter-coupled spin-transistor logic (ECSTL), is an extension of emitter-coupled logic (ECL) that leverages the advanced features of spintronic devices. The current through the ECL differential amplifier is routed to create a magnetic field that modulates the magnetoamplification of the spin-transistors. This cascading mechanism supplements the voltage cascading available in conventional ECL, providing additional inputs to each logic stage. Each gate therefore has increased logical functionality, leading to logic minimization and compact circuits. No additional current is required to employ this added spintronic switching, resulting in improved speed, area, and power characteristics. This logic family achieves a power-delay product 10–25 times smaller than conventional ECL, inspiring a pathway for high-performance spintronic computing beyond 10 GHz.

This paper presents a platform combining an inverse electromagnetic design computational method with additive manufacturing to design and fabricate all-dielectric metadevices. As opposed to conventional flat metasurface-based devices that are composed of resonant building blocks resulting in narrow band operation, the proposed design approach creates non-resonant, broadband (Δλ/λ up to >50%) metadevices based on low-index dielectric materials. High-efficiency (transmission >60%), thin (≤2λ) metadevices capable of polarization splitting, beam bending, and focusing are proposed. Experimental demonstrations are performed at millimeter-wave frequencies using 3D-printed devices. The proposed platform can be readily applied to the design and fabrication of electromagnetic and photonic metadevices spanning microwave to optical frequencies.

In this paper we present an inverse-designed 3D-printed all-dielectric stretchable millimeter wave metalens with a tunable focal distance. Computational inverse-design method is used to design a flat metalens made of disconnected building polymer blocks with complex shapes, as opposed to conventional monolithic lenses. Proposed metalens provides better performance than a conventional Fresnel lens, using lesser amount of material and enabling larger focal distance tunability. The metalens is fabricated using a commercial 3D-printer and attached to a stretchable platform. Measurements and simulations show that focal distance can be tuned by a factor of 4 with a stretching factor of only 75%, a nearly diffraction-limited focal spot, and with a 70% focusing efficiency. The proposed platform can be extended for design and fabrication of multiple electromagnetic devices working from visible to microwave radiation depending on scaling of the devices., 11 pages, 4 figures

This brief proposes and analyzes a novel logic family composed solely of magnetic tunnel junctions (MTJs) to form complimentary pull-up and pull-down networks. This logic family solves the challenge of direct cascading in spintronic logic circuits while also providing non-volatile data storage. The increased logic density possible with complementary MTJs coupled with a charge pulse switching mechanism driven by ferromagnet polarization permits highly efficient logical computation. Furthermore, the presence of non-volatile memory within the logic structure provides a feasible hardware for non-von Neumann computer architectures.

HighlightsAutomatic, online calibration between a color camera and a depth camera.No special equipment; just a square, checkered target.Calibration occurs very quickly and with minimal user interaction.Online calibration allows user to see results immediately.Rigorous ground truth validation places this algorithm at leading edge. Automatic camera calibration has remained a hard topic in computer vision since its inception due to its reliance on the image correspondence problem. This problem becomes even more pronounced when calibrating a depth image with a color image due to a lack of simple correspondences between the two modalities. In this work, we develop a completely automatic, very fast, online algorithm that demonstrates how a consumer-grade depth camera can be calibrated with a color camera with minimal user interaction.

Remarkable breakthroughs have established the functionality of graphene and carbon nanotube transistors as replacements to silicon in conventional computing structures, and numerous spintronic logic gates have been presented. However, an efficient cascaded logic structure that exploits electron spin has not yet been demonstrated. In this work, we introduce and analyse a cascaded spintronic computing system composed solely of low-dimensional carbon materials. We propose a spintronic switch based on the recent discovery of negative magnetoresistance in graphene nanoribbons, and demonstrate its feasibility through tight-binding calculations of the band structure. Covalently connected carbon nanotubes create magnetic fields through graphene nanoribbons, cascading logic gates through incoherent spintronic switching. The exceptional material properties of carbon materials permit Terahertz operation and two orders of magnitude decrease in power-delay product compared to cutting-edge microprocessors. We hope to inspire the fabrication of these cascaded logic circuits to stimulate a transformative generation of energy-efficient computing., Spintronics, graphene, and carbon nanotubes are potential components of next-generation high performance computers. Here, the authors propose and theoretically evaluate a spintronic logic family composed solely of carbon materials with the potential for a 100 × improvement in energy efficiency.

Using a 94-GHz millimeter-wave interferometer, we are able to calculate the relative displacement of an object. When aimed at the chest of a human subject, we measure the minute motions of the chest due to cardiac activity. After processing the data using a wavelet multiresolution decomposition, we are able to obtain a signal with peaks at heartbeat temporal locations. In order for these heartbeat temporal locations to be accurate, the reflected signal must not be very noisy. Since there is noise in all but the most ideal conditions, we created a statistical algorithm in order to compensate for unconfident temporal locations as computed by the wavelet transform. By analyzing the statistics of the peak locations, we fill in missing heartbeat temporal locations and eliminate superfluous ones. Along with this, we adapt the processing procedure to the current signal, as opposed to using the same method for all signals. With this method, we are able to find the heart rate of ambulatory subjects without any physical contact.

This paper presents a solution to an aiming problem in the remote sensing of vital signs using an integration of two systems. The problem is that to collect meaningful data with a millimeter-wave sensor, the antenna must be pointed very precisely at the subject's chest. Even small movements could make the data unreliable. To solve this problem, we attached a camera to the millimeter-wave antenna, and mounted this combined system on a pan/tilt base. Our algorithm initially finds a subject's face and then tracks him/her through subsequent frames, while calculating the position of the subject's chest. For each frame, the camera sends the location of the chest to the pan/tilt base, which rotates accordingly to make the antenna point at the subject's chest. This paper presents a system for concurrent tracking and data acquisition with results from some sample scenarios.

Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.