Penetrating 3-D Imaging at 4- and 25-m Range Using a Submillimeter-Wave Radar

We show experimentally that a high-resolution imaging radar operating at 576–605 GHz is capable of detecting weapons concealed by clothing at standoff ranges of 4–25 m. We also demonstrate the critical advantage of 3-D image reconstruction for visualizing hidden objects using active-illumination coherent terahertz imaging. The present system can image a torso with <1 cm resolution at 4 m standoff in about five minutes. Greater standoff distances and much higher frame rates should be achievable by capitalizing on the bandwidth, output power, and compactness of solid state Schottky-diode based terahertz mixers and multiplied sources

3D microwave tomography with huber regularization applied to realistic numerical breast phantoms

Quantitative active microwave imaging for breast cancer screening and therapy monitoring applications requires adequate reconstruction algorithms, in particular with regard to the nonlinearity and ill-posedness of the inverse problem. We employ a fully vectorial three-dimensional nonlinear inversion algorithm for reconstructing complex permittivity profiles from multi-view single-frequency scattered field data, which is based on a Gauss-Newton optimization of a regularized cost function. We tested it before with various types of regularizing functions for piecewise-constant objects from Institut Fresnel and with a quadratic smoothing function for a realistic numerical breast phantom. In the present paper we adopt a cost function that includes a Huber function in its regularization term, relying on a Markov Random Field approach. The Huber function favors spatial smoothing within homogeneous regions while preserving discontinuities between contrasted tissues. We illustrate the technique with 3D reconstructions from synthetic data at 2GHz for realistic numerical breast phantoms from the University of Wisconsin-Madison UWCEM online repository: we compare Huber regularization with a multiplicative smoothing regularization and show reconstructions for various positions of a tumor, for multiple tumors and for different tumor sizes, from a sparse and from a denser data configuration

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion

Signal specific electric potential sensors for operation in noisy environments

Limitations on the performance of electric potential sensors are due to saturation caused by environmental electromagnetic noise. The work described involves tailoring the response of the sensors to reject the main components of the noise, thereby enhancing both the effective dynamic range and signal to noise. We show that by using real-time analogue signal processing it is possible to detect a human heartbeat at a distance of 40 cm from the front of a subject in an unshielded laboratory. This result has significant implications both for security sensing and biometric measurements in addition to the more obvious safety related applications

A Comprehensive Review on Design and Development of Human Breast Phantoms for Ultra-Wide Band Breast Cancer Imaging Systems

Microwave ultra-wide band UWB imaging system is a contemporary biomedical imaging technology for early detection of breast cancers. This imaging system requires the development of breast phantoms for experimental data analysis. In order to obtain realistic results, it is very important that these phantoms mimic the characteristics of real biological breast tissue as close as possible. For this purpose, scientists and engineers make use of the dielectric properties of human breast. This paper takes a survey of mathematical formulations used to determine biological dielectric properties and then takes a review of current breast phantoms being used in UWB imaging systems with reference to the analytical dielectric measurements. At present, breast phantoms are made, both, manually in laboratory utilizing different chemicals and also by using computational electromagnetic algorithms to introduce better heterogeneity in them. They can then easily be tested by doing computer simulations. In this review paper, emphasis is made on the phantoms which are made in laboratory for doing hardware experimentations.Microwave ultra-wide band UWB imaging system is a contemporary biomedical imaging technology for early detection of breast cancers. This imaging system requires the development of breast phantoms for experimental data analysis. In order to obtain realistic results, it is very important that these phantoms mimic the characteristics of real biological breast tissue as close as possible. For this purpose, scientists and engineers make use of the dielectric properties of human breast. This paper takes a survey of mathematical formulations used to determine biological dielectric properties and then takes a review of current breast phantoms being used in UWB imaging systems with reference to the analytical dielectric measurements. At present, breast phantoms are made, both, manually in laboratory utilizing different chemicals and also by using computational electromagnetic algorithms to introduce better heterogeneity in them. They can then easily be tested by doing computer simulations. In this review paper, emphasis is made on the phantoms which are made in laboratory for doing hardware experimentations

Non Contact Heart Monitoring

Electrocardiograms are one of the most widely used methods for evaluating the structure-function relationships of the heart in health and disease. This book is the first of two volumes which reviews recent advancements in electrocardiography. This volume lays the groundwork for understanding the technical aspects of these advancements. The five sections of this volume, Cardiac Anatomy, ECG Technique, ECG Features, Heart Rate Variability and ECG Data Management, provide comprehensive reviews of advancements in the technical and analytical methods for interpreting and evaluating electrocardiograms. This volume is complemented with anatomical diagrams, electrocardiogram recordings, flow diagrams and algorithms which demonstrate the most modern principles of electrocardiography. The chapters which form this volume describe how the technical impediments inherent to instrument-patient interfacing, recording and interpreting variations in electrocardiogram time intervals and morphologies, as well as electrocardiogram data sharing have been effectively overcome. The advent of novel detection, filtering and testing devices are described. Foremost, among these devices are innovative algorithms for automating the evaluation of electrocardiograms. Permanenet links: Full chapter: http://www.intechopen.com/articles/show/title/non-contact-heart-monitoring Book: http://www.intechopen.com/books/show/title/advances-in-electrocardiograms-methods-and-analysi

Challenges in the Design of Microwave Imaging Systems for Breast Cancer Detection

Among the various breast imaging modalities for breast cancer detection, microwave imaging is attractive due to the high contrast in dielectric properties between the cancerous and normal tissue. Due to this reason, this modality has received a significant interest and attention from the microwave community. This paper presents the survey of the ongoing research in the field of microwave imaging of biological tissues, with major focus on the breast tumor detection application. The existing microwave imaging systems are categorized on the basis of the employed measurement concepts. The advantages and disadvantages of the implemented imaging techniques are discussed. The fundamental tradeoffs between the various system requirements are indicated. Some strategies to overcome these limitations are outlined