In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

From the Publisher: 
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

数字信号处理 = Understanding digital signal processing

Quality control and authentication of packaged integrated circuits using enhanced-spatial-resolution terahertz time-domain spectroscopy and imaging

This paper presents a comprehensive theory for cohesive mathematical modeling and simulation of THz imaging systems. For mathematical modeling of the point spread function (PSF), system and transmission variables such as spectrum, absorption coefficient, beam divergence, and depth of focus are incorporated into the Gaussian beam distribution. The raster scanning process is mathematically modeled as the convolution of the object function and the PSF. Simulated transmission THz images are achieved as a result. The simulated THz images, compared to the experimental THz images, show great accuracy in terms of the location of the details and structural similarity.

Mathematical Modeling of THz Point Spread Function and Simulation of THz Imaging Systems

Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet . Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.

Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

We compared the possibility of detecting hidden objects covered with various types of clothing by using passive imagers operating in a terahertz (THz) range at 1.2 mm (250 GHz) and a mid-wavelength infrared at 3–6 $\mu \text{m}$ (50–100 THz). We investigated theoretical limitations, performance of imagers, and physical properties of fabrics in both the regions. In order to investigate the time stability of detection, we performed measurements in sessions each lasting 30 min. We present a theoretical comparison of two spectra, as well as the results of experiments. In order to compare the capabilities of passive imaging of hidden objects, we combined the properties of textiles, performance of imagers, and properties of radiation in both spectral ranges. The paper presents the comparison of the original results of measurement sessions for the two spectrums with analysis.

Comparative Studies of Passive Imaging in Terahertz and Mid-Wavelength Infrared Ranges for Object Detection

Terahertz and infrared radiation have unique properties applicable to the field of surveillance and security systems. We investigated the possibility of detecting potentially dangerous objects covered by various types of clothing using passive imagers operating at 1.2 mm (250 GHz) and long-wavelength infrared at 6–15 μm (20–50 THz). We developed a measurement methodology that assumes to investigate theoretical limitations, performance of imagers, and physical properties of fabrics. To evaluate stability of the detection capabilities of imagers, we performed measurement sessions each lasting 30 min. We present a theoretical comparison of the two spectra and results of experiments using state-of-the-art equipment.

Passive imaging of concealed objects in terahertz and long-wavelength infrared

The study presents the comparison of detection and recognition of concealed objects covered with various types of clothing by using passive imagers operating in a terahertz (THz) range at 1.2 mm (250 GHz) and a mid-wavelength infrared (MWIR) at 3–6 μm (50–100 THz). During this study, large dataset of images presenting various items covered with various types of clothing has been collected. The detection and classification algorithms aimed to operate robustly at high processing speed across these two spectrums. Properties of both spectrums, theoretical limitations, performance of imagers and physical properties of fabrics in both spectral domains are described. The paper presents a comparison of two deep learning–based processing methods. The comparison of the original results of various experiments for the two spectrums is presented.

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Hidden Object Detection and Recognition in Passive Terahertz and Mid-wavelength Infrared

We compared terahertz transmission spectra of explosives measured by means of Time Domain Spectroscopy (TDS) and an Optical Parametric Oscillator-based system (OPO). Reflection spectra of pure explosives were measured in reflection configuration by means of TDS in the range 0.3–2.5THz. We also analyzed influence of the surface roughness on reflectance and phase spectra of RDX-plastic based explosives. Next, we present a thermal phantom of human body working in THz range, which was developed for testing of THz cameras. We demonstrate the possibility of improvement of the quality of the image captured by a commercially available passive THz camera.

Marcin Kowalski

Papers

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Comparative Studies of Passive Imaging in Terahertz and Mid-Wavelength Infrared Ranges for Object Detection

Passive imaging of concealed objects in terahertz and long-wavelength infrared

Hidden Object Detection and Recognition in Passive Terahertz and Mid-wavelength Infrared

THz spectroscopy and imaging in security applications