Study on THz Imaging System for Concealed Threats Detection.

PhD ThesisMany research groups have conducted studies on Terahertz technology for various applications in the last decades. THz imaging for personnel screening is one prospective application due in part to its superior performance compared with imaging microwave bands. Because of the demand for the accurate detection, it is desirable to devise a high-performance THz imaging system for concealed threats detection. Therefore, this thesis presents my research on the low-cost THz imaging system for security detection. The key contributions of this research lie in investigating the linear sparse periodic array (SPA) THz imaging system for concealed threats detection, improving the traditional reconstruction algorithm of Generalized Synthetic Aperture Focusing Technique (GSAFT) to suppress the ghost images and applying the compressive sensing technique into the proposed SPA-THz imaging system to reduce the sampling data but maintain the image quality. The first part of the work is to investigate the linear sparse periodic array (SPA) and its configuration with large element spacing in simulation, deriving the design guideline for such a SPA THz imaging system. Meanwhile, the improved GSAFT reconstruction algorithm and multi-pass interferometric synthetic aperture imaging technique have been proposed to suppress the ghost image and improve the image quality, respectively. Secondly, the compressive sensing technique has been investigated to reduce the sampling data. Therefore, we have proposed the corresponding discrete CS SPA-THz reconstruction model and verified it in simulation. Finally, we have devised a simplified experimental set-up to assess the practical imaging performance, verifying the proposed SPA-THz imaging system. The set-up only uses 1 Tx and 1 Rx scanning on two separate tracks to effectively realize the proposed imaging system. The reconstructed images by the GSAFT and CS approaches with the measured data have both shown good consistency with the simulated results, respectively. And the multi-pass interferometric synthetic aperture imaging has been experimentally proved effective in improving image SNR and contras

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Real-Time Magnetic Resonance Imaging

Real‐time magnetic resonance imaging (RT‐MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast‐switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady‐state free precession, and single‐shot rapid acquisition with relaxation enhancement. RT‐MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft‐tissue contrast, as well as flow information. In this review, we discuss the history of RT‐MRI, fundamental tradeoffs, enabling technology, established applications, and current trends

Cardiac magnetic resonance assessment of central and peripheral vascular function in patients undergoing renal sympathetic denervation as predictor for blood pressure response

Background: Most trials regarding catheter-based renal sympathetic denervation (RDN) describe a proportion of patients without blood pressure response. Recently, we were able to show arterial stiffness, measured by invasive pulse wave velocity (IPWV), seems to be an excellent predictor for blood pressure response. However, given the invasiveness, IPWV is less suitable as a selection criterion for patients undergoing RDN. Consequently, we aimed to investigate the value of cardiac magnetic resonance (CMR) based measures of arterial stiffness in predicting the outcome of RDN compared to IPWV as reference. Methods: Patients underwent CMR prior to RDN to assess ascending aortic distensibility (AAD), total arterial compliance (TAC), and systemic vascular resistance (SVR). In a second step, central aortic blood pressure was estimated from ascending aortic area change and flow sequences and used to re-calculate total arterial compliance (cTAC). Additionally, IPWV was acquired. Results: Thirty-two patients (24 responders and 8 non-responders) were available for analysis. AAD, TAC and cTAC were higher in responders, IPWV was higher in non-responders. SVR was not different between the groups. Patients with AAD, cTAC or TAC above median and IPWV below median had significantly better BP response. Receiver operating characteristic (ROC) curves predicting blood pressure response for IPWV, AAD, cTAC and TAC revealed areas under the curve of 0.849, 0.828, 0.776 and 0.753 (p = 0.004, 0.006, 0.021 and 0.035). Conclusions: Beyond IPWV, AAD, cTAC and TAC appear as useful outcome predictors for RDN in patients with hypertension. CMR-derived markers of arterial stiffness might serve as non-invasive selection criteria for RDN

Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks

Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept

Signal acquisition challenges in mobile systems

In recent decades, the advent of mobile computing has changed human lives by providing information that was not available in the past. The mobile computing platform opens a new door to the connected world in which various forms of hand-held and wearable systems are ubiquitous. A single mobile device plays multiple roles and shapes human lives towards a better future. In these systems, sensor-based data acquisition plays an essential role in generating and providing useful information. The increased number of sensors is embedded in a single device in order to process various signal modalities. In practice, more than 30 data converters are required in designing a mobile system in which the data-converting blocks become among the most power-hungry components in battery-operated systems. Due to the increased variety of sensors, mobile systems are meant to face several obstacles. For example, the increased number of sensors increase system power consumption during the system operation. The increased power consumption directly affects operation time because mobile systems are powered by a limited energy source. Moreover, an increased amount of information also gives rise to bandwidth problems in communication due to the increased volume of data transmission. Also, this system design requires a larger area in a silicon die so that multiple signal paths can be placed without cross-channel interference. Therefore, the system design has presented a challenge in terms of trying to resolve the design constraints such as power consumption, bandwidth usage, storage space, and design complexity issues. To overcome these obstacles, in this dissertation, efficient data acquisition and processing methods are investigated. Specifically, this thesis considers the problems of energy-efficient sampling and binary event detection. This dissertation begins by presenting a new signal sampling scheme that enables higher precision signal conversion in compressed-sensing-based signal acquisition. The proposed scheme is based on the popular successive approximation register and employs a modified compressive sensing technique to increase the resolution of successive-approximation-register (SAR) analog-to-digital converter (ADC) architecture. Circuit-level architecture is discussed to implement the proposed scheme using the SAR ADC architecture. A non-uniform quantization scheme is proposed and it improves data quality after data acquisition. The proposed scheme is expected to be used for medium- or high- frequency data conversion. Secondly, the possibility of using fewer ADCs than channels is studied by leveraging sparse-signal representation and blind-source-separation (BSS) techniques. In particular, this dissertation examines the problem of using a single ADC or quantizer system for digitizing multi-channel inputs. Mixing and de-mixing strategies are extensively studied for sampling frequency-sparse signals and the proposed multi-channel architecture can be easily implemented using today's analog/mixed-signal circuits. The third part of this dissertation investigates a binary hypothesis testing problem. In mobile devices such as smartphones and tablet PCs, a major portion of energy is consumed in user interfaces (LCD display and touch input processing). For accurate detection and better user interface, energy-efficient sensing and detection schemes are necessary to manage multiple sensor inputs. A highly efficient detection scheme is presented that can detect binary events reliably with a fraction of the energy consumption required in the conventional energy detection.Electrical and Computer Engineerin

Hardware architectures for compact microwave and millimeter wave cameras

Millimeter wave SAR imaging has shown promise as an inspection tool for human skin for characterizing burns and skin cancers. However, the current state-of-the-art in microwave camera technology is not yet suited for developing a millimeter wave camera for human skin inspection. Consequently, the objective of this dissertation has been to build the necessary foundation of research to achieve such a millimeter wave camera. First, frequency uncertainty in signals generated by a practical microwave source, which is prone to drift in output frequency, was studied to determine its effect on SAR-generated images. A direct relationship was found between the level of image distortions caused by frequency uncertainty and the product of frequency uncertainty and distance between the imaging measurement grid and sample under test. The second investigation involved the development of a millimeter wave imaging system that forms the basic building block for a millimeter wave camera. The imaging system, composed of two system-on-chip transmitters and receivers and an antipodal Vivaldi-style antenna, operated in the 58-64 GHz frequency range and employed the ω-k SAR algorithm. Imaging tests on burnt pigskin showed its potential for imaging and characterizing flaws in skin. The final investigation involved the development of a new microwave imaging methodology, named Chaotic Excitation Synthetic Aperture Radar (CESAR), for designing microwave and millimeter wave cameras at a fraction of the size and hardware complexity of previous systems. CESAR is based on transmitting and receiving from all antennas in a planar array simultaneously. A small microwave camera operating in the 23-25 GHz frequency was designed and fabricated based on CESAR. Imaging results with the camera showed it was capable of basic feature detection for various applications --Abstract, page iv