All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.Comment: 21 Pages, 7 Figure

Product Line Architecture Design of Software-Intensive Physical Protection Systems

A physical protection system (PPS) integrates people, procedures, and equipment for the protection of assets or facilities against theft, sabotage, or other malevolent intruder attacks. Since PPSs are not radically different and share lots of commonalities, there is an important potential for reuse and herewith an opportunity to substantially reduce the cost and development time, and enhance the quality of the developed PPSs. In this paper, we report on the design of a product line architecture for a family of software-intensive PPSs. With this, we adopt a model-based systems engineering (MBSE) approach in which we focus on the architecture design of PPSs. We model the corresponding architecture view models for the PPS product line architecture and discuss the development of specific PPSs

Systems Engineering Architecture Framework for Physical Protection Systems

A physical protection system (PPS) integrates people, procedures, and equipment for the protection of assets or facilities against theft, sabotage, or other malevolent intruder attacks. In this paper we focus on the architecture modeling of PPS to support the communication among stakeholders, analysis and guiding the systems development activities. A common practice for modeling architecture is by using an architecture framework that defines a coherent set of viewpoints. Existing systems engineering modeling approaches appear to be too general and fail to address the domain-specific aspects of PPSs. On the other hand, no dedicated architecture framework approach has been provided yet to address the specific concerns of PPS. In this paper, we present an architecture framework for PPS (PPSAF) that has been developed in a real industrial context focusing on the development of multiple PPSs. The architecture framework consists of six coherent set of viewpoints including facility viewpoint, threats and vulnerabilities viewpoint, deterrence viewpoint, detection viewpoint, delay viewpoint, and response viewpoint. We illustrate the application of the architecture framework for the design of a PPS architecture of a building

Model-Based Development of Design Basis Threat for Physical Protection Systems

Physical protection system (PPS) is developed to protect the assets or facilities against threats. A systematic analysis of the capabilities and intentions of potential threat capabilities is needed resulting in a so-called Design Basis Threat (DBT) document. A proper development of DBT is important to identify the system requirements that are required for adequately protecting a system and to optimize the resources needed for the PPS. In this paper we propose a model-based systems engineering approach for developing a DBT based on feature models. Based on a domain analysis process, we provide a metamodel that defines the key concepts needed for developing DBT. Subsequently, a reusable family feature model for PPS is provided that includes the common and variant properties of the PPS concepts detection, deterrence and response. The configuration processes are modeled to select and analyze the required features for implementing the threat scenarios. Finally, we discuss the integration of the DBT with the PPS design process

Renal Failure after Coronary Bypass Surgery and the Associated Risk Factors

WOS: 000370849200002PubMed ID: 25881215Objective: We aimed to evaluate the risk factors associated with acute renal failure in patients who underwent coronary artery bypass surgery. Methods: One hundred and six patients who developed renal failure after coronary artery bypass grafting (CABG) constituted the study group (RF group), while 110 patients who did not develop renal failure served as a control group (C group). In addition, the RF group was divided into two subgroups: patients that were treated with conservative methods without the need for hemodialysis (NH group) and patients that required hemodialysis (HR group). Risk factors associated with renal failure were investigated. Results: Among the 106 patients that developed renal failure (RF), 80 patients were treated with conservative methods without any need for hemodialysis (NH group); while 26 patients required hemodialysis in the postoperative period (HR group). The multivariate analysis showed that diabetes mellitus and the postoperative use of positive inotropes and adrenaline were significant risk factors associated with development of renal failure. In addition, carotid stenosis and postoperative use of adrenaline were found to be significant risk factors associated with hemodialysis-dependent renal failure (P < .05). The mortality in the RF group was determined as 13.2%, while the mortality rate in patients who did not require hemodialysis and those who required hemodialysis was 6.2% and 34%, respectively. Conclusion: Renal failure requiring hemodialysis after CABG often results in high morbidity and mortality. Factors affecting microcirculation and atherosclerosis, like diabetes mellitus, carotid artery stenosis, and postoperative vasopressor use remain the major risk factors for the development of renal failure