Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality

ECG Signal Transmissions Performance over Wearable Wireless Sensor Networks

AbstractSudden death is the most common disease of heart diseases and usually caused by cardiac arrest. The electrocardiograph (ECG) system is the most direct way to observe and monitor health status of the heart. In this kind of disease, the patient may need continuous monitoring, and sometimes is kept in Intensive Care Unit (ICU), which needs more utilities and manpower that will eventually leads to increase the cost and demands for qualified medical staff. In this paper we introduce a comprehensive real time monitoring ECG system for continuous monitoring patients inside/outside home. Wearable Wireless Sensor Network with a cluster head is connected to patient body for monitoring. Measured data by WWSS are transmitted via cluster head to an internet connection to the monitoring system located on the hospital. In case of emergency, the measured data is sent to the physician's cell phone for any necessary actions. We discuss the inside/outside system structure. Additionally, we analyze the proposed system in terms of power consumptions and optimum distance between WSN sensors

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios

Mathematical Model Development of Super-Resolution Image Wiener Restoration

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter as a function of the parameters associated with the proposed SR system such as image gathering and display response indices, system average signal-to-noise ratio, and inter-subpixel shifts between the LR images. Simulation and experimental results demonstrate that the derived Wiener filter with the optimal allocation of LR images results in sharper reconstruction. When compared with other SR techniques, our approach outperforms them in both quality and computational time

Correlation between Micronutrient plasma concentration and disease severity in COVID-19 patients

Objectives: Coronavirus Disease 2019 (COVID-19) is caused by a new strain of betacoronavirus called SARS-CoV-2, which leads to mild to severe symptoms. Micronutrients in blood serum, namely, zinc, iron, copper, and selenium, play essential roles in the human body’s various organs. This study investigates the association between micronutrient levels and the severity of symptoms in SARS-CoV-2 infected patients.Methods: A cross-section study was conducted during June–August 2020 in Riyadh city among 80 patients with confirmed SARS-CoV-2 infection. Within 24 hours of hospital admission, patients have been divided into non-severe and severe cases, and blood samples were drawn from each patient to measure the serum levels of copper, iron “in the form of ferritin,” selenium, and zinc.Results: In both study groups, the mean copper and selenium serum levels were within the normal range, while the mean zinc and iron serum levels were elevated. A statistically significant difference was recorded between non-severe and severe cases regarding serum levels of iron and selenium (331.24 vs. 1174.95 ng/ml and 134 vs. 162 mcg/L, respectively, P < 0.0001). On the other hand, no significant difference was detected between both studied groups regarding serum level of zinc and copper (124.57 vs. 116.37 mcq/L and 18.35 vs. 18.2 mcmol/ L, respectively, P > 0.05).Conclusions: There was a significant elevation of selenium and iron serum levels among severe cases compared to non-severe cases of COVID-19. High levels of iron and selenium could be correlated with the disease severity during infection with SARS-CoV-2

Toward Automatic Subpixel Registration of Unmanned Airborne Vehicle Images

Many applications require to register images within subpixel accuracy like computer vision especially super-resolution (SR) where the estimated subpixel shifts are very crucial in the reconstruction and restoration of SR images. In our work we have an optical sensor that is mounted on an unmanned airborne vehicle (UAV) and captures a set of images that contain sufficient overlapped area required to reconstruct a SR image. Due to the wind, The UAV may encounter rotational effects such as yaw, pitch and roll which can distort the acquired as well as processed images with shear, tilt or perspective distortions. In this paper we propose a hybrid algorithm to register these UAV images within subpixel accuracy to feed them in a SR reconstruction step. Our algorithm consists of two steps. The first step uses scale invariant feature transform (SIFT) to correct the distorted images. Because the resultant images are not registered to a subpixel precision, the second step registers the images using a fast Fourier transform (FFT) based method that is both efficient and robust to moderate noise and lens optical blur. Our FFT based method reduces the dimensionality of the Fourier matrix of the cross correlation and uses a forward and backward search in order to obtain an accurate estimation of the subpixel shifts. We discuss the relation between the dimensionality reduction factors and the image shifts as well as propose criteria that can be used to optimally select these factors. Finally, we compare the results of our approach to other subpixel techniques in terms of their efficiency and computational speed

Response of Human Malignant Glioma Cells to Asymmetric Bipolar Electrical Impulses

Electric and electromagnetic pulses have been shown to enhance the endocytosis rate, with all-or-nothing responses beyond a field strength threshold and linear responses as a function of field strength and treatment duration utilizing bipolar symmetrical and monopolar pulses, respectively. Malignant glioma (MG) is resistant to chemotherapy. The present study looked for a new electrical impulse that can aid electrochemotherapy to deliver anticancer drugs while using less electrical energy. Bipolar asymmetric electric pulses were applied to U251MG cells suspended in physiologically conductive media in the presence of molecular probes, including Bleomycin. The delivered electric pulses with a pulse duration range of 180-500 µs and a frequency range of 100-400 Hz had a low field intensity ranging from 1.5 V/cm to 7.3 V/cm. Spectrophotometric and spectrofluorometric measurements were used to investigate the impact of these variables on cancer cell survival and the molecular probe uptake induced by the electric pulses. An all-or-nothing response was observed above a specified threshold of electric field intensity of 4 V/cm. This threshold was unaffected by changes in repetition frequency or pulse duration. It was not a temperature effect that caused the molecular probe uptake to increase. When bipolar asymmetric electric pulses were applied just before electroporation, the effectiveness of the cytotoxic impact of bleomycin was increased from 80%, when employing electroporation pulses alone, to 100%

Fast Stochastic Wiener Filter for Super-Resolution Image Restoration with Information Theoretic Visual Quality Assessment

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochastic Wiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering and image-display processes. We incorporate a new parameter that accounts for LR images registration and fusion errors. Also, we speeded up the performance of the filter by constraining it to work on small patches of the images. Beside this, we introduce two figures of merits: information rate and maximum realizable fidelity, which can be used to assess the visual quality of the resultant images. Simulations and experimental results demonstrate that the derived Wiener filter that can be implemented efficiently in the frequency domain can reduce aliasing, blurring, and noise and result in a sharper reconstructed image. Also, Quantitative assessment using the proposed figures coincides with the visual qualitative assessment. Finally, we evaluate our filter against other SR techniques and its results were very competitive

Hardware Implementation of Chipless RFID Reader and Tags for Moving Targets Identification and Tracking

Radio Frequency Identification (RFID) is a rapidly growing technology with significant security implications for humans and military ambushes. In this paper, the hardware implementation of a chipless RFID system using highly efficient Texas instrumentation components is introduced. We introduced the hardware implementation of both RFID reader and 5-bits chipless tags. The proposed reader system is equipped with an ultra-wideband (UWB) radio frequency (RF) power detector that allows the reader to read different types of tags with different code lengths over the frequency range from  to . The proposed spiral resonators based RFID tags are fabricated using microstrip technology on a cheap FR4 lossy substrate with a dielectric constant of , loss tangent , and thickness . The tags are designed using the computer simulation technology (CST) microwave studio software package. Fortunately, it is found that the experimental measurements of the scattering parameters of the fabricated tags are highly matched to the simulation results