Spectrum sensing, spectrum monitoring, and security in cognitive radios

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. In this dissertation, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user’s signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). We investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). Next we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving radios. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. We also consider the problem of centralized binary hypothesis testing in a cognitive radio network (CRN) consisting of multiple classes of cognitive radios, where the cognitive radios are classified according to the probability density function (PDF) of their received data (at the FC) under each hypotheses

Innovative Strategies for Healthcare Data Integration: Enhancing Etl Efficiency Through Containerization and Parallel Computing

The healthcare industry is rapidly transforming due to technology adoption, resulting in an explosion of data. Extract, Transform, Load (ETL) processes are crucial for integrating and analyzing this data to support decision-making and enhance patient care. However, ETL processes face significant challenges, including data diversity, quality issues, security and compliance, and scalability. Opportunities exist to optimize ETL processes through advanced technologies like big data analytics, containerization, and parallel computing, improving data quality, and enhancing security. This literature review examines current ETL processes in healthcare, highlighting challenges and opportunities for future improvement, ultimately aiming to enhance healthcare outcomes and patient experiences. In the seccond study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request

The effect of high voltage electric pulse on the coarse particle flotation of sulfur-bearing iron ore samples

In this research, the effect of the high-voltage electric pulse (HVEP) crushing on the flotation of high-sulfur iron ore concentrate in the coarse particle fraction was studied compared to mechanical (conventional) crushing. A jaw crusher, a cone crusher, and a high-voltage electric pulse crushing device with a voltage level of 50 kV were used to investigate the effect of mechanical and electrical crushing. The results showed that a coarser particle product was produced with less slime in primary crushing with electric pulses compared to primary mechanical crushing. It was due to the crushing mechanism, which is based on separating minerals with a different dielectric constant from their connection boundaries and also encompasses a selective separation process. The effect of the mentioned method on coarser fractions led to the creation of cracks/microcracks in particle structures that made grinding easier and faster. In investigating the effect of particle size on pyrite flotation and desulfurization at -300 µm (d80=300µm), the sulfur grade of flotation iron concentrate samples using primary crushing was 0.86% and 0.36%, respectively, and at -150 µm (d80=150µm) fraction, the sulfur grade was found to be 0.33% and 0.19% respectively for mechanical and electrical methods. Also, the sulfur removal (recovery) of the sample with primary electrical crushing was 73.7% at a -300 µm fraction, almost equal to 73.2% at the size range of -150 µm with applying the mechanical method. These results indicated the flotation possibility of coarser particles using electrical crushing and desulfurization similarity to the samples with primary mechanical crushing in finer fractions

Selection of practical bench height in open pit mining using a multi-criteria decision making solution

Determination of practical bench height is an important subject in open pit mining. This subject has always been an issue with different and sometimes conflicting criteria that have to be precisely considered during the mine design process. In this study a multi-expert multi-criteria decision making approach is used to resolve these complexities. In the proposed approach, different bench heights are firstly analyzed considering the variety of criteria such as production scheduling, dilution, costs, practicability, safety, and equipment availability. The practicability analysis is consisted of a primary sequencing method developed to compare total time needed for all bench height alternatives to reach the constant annual production. Once the criteria are weighted according to judgments by expert team, the obtained performance scores are passed to a multi-criteria model called VIKOR (multi-criteria optimization and compromise solution) to introduce the optimum alternative. This approach was utilized for a simple example with two alternatives, where the obtained results confirmed its efficiency

Large melt diversity at a mid-ocean ridge thermal low

Mid-ocean ridges serve as key sites for understanding the composition of the mantle, but extensive melting usually masks its lithological diversity. This study explores how cold mid-ocean ridge segments, such as the eastern Romanche ridge-transform intersection (ERRTI), provide unique insights into mantle heterogeneity. Here, a thick cold lithosphere faces the warm ridge segment efficiently cooling the ridge tip, thus reducing melting and mixing, and allowing distinct short-scale lithologies to be sampled. Our findings reveal a mosaic of mantle components with diverse geochemical and isotopic signatures, reflecting dynamic mantle processes over time. By examining these cold regimes, this research sheds light on the mantle’s compositional complexity and its evolution, offering a fresh perspective on lithospheric dynamics and melt generation in settings independent of hotspot influences

Development of pure poly vinyl chloride (PVC) with excellent 3D printability and macro‐ and micro‐structural properties

Unmodified polyvinyl chloride (PVC) has low thermal stability and high hardness. Therefore, using plasticizers as well as thermal stabilizers is inevitable, while it causes serious environmental and health issues. In this work, for the first time, pure food-grade PVC with potential biomedical applications is processed and 3D printed. Samples are successfully 3D printed using different printing parameters, including velocity, raster angle, nozzle diameter, and layer thickness, and their mechanical properties are investigated in compression, bending, and tension modes. Scanning electron microscopy is also used to evaluate the bonding and microstructure of the printed layers. Among the mentioned printing parameters, raster angle and printing velocity influence the mechanical properties significantly, whereas the layer thickness and nozzle diameter has a little effect. Images from scanning electron microscopy also reveal that printing velocity greatly affects the final part's quality regarding defective voids and rasters’ bonding. The maximum tensile strength of 88.55 MPa is achieved, which implies the superiority of 3D-printed PVC mechanical properties compared to other commercial filaments. This study opens an avenue to additively manufacture PVC that is the second most-consumed polymer with cost-effective and high-strength features

Effects of TPU on the mechanical properties, fracture toughness, morphology, and thermal analysis of 3D‐printed ABS‐TPU blends by FDM

In this paper, blends of ABS-TPU with two different weight percentages of TPU were prepared using fused deposition modeling technology. The effect of adding TPU on the fracture toughness of ABS and mechanical properties was comprehensively studied. Tensile, compression, fracture toughness, and shear tests were conducted on the 3D-printed samples. Thermal and microstructural analyses were performed using dynamic mechanical thermal analysis (DMTA), and scanning electron microscope (SEM). The DMTA results showed that adding TPU decreased the storage modulus and the glass transition temperature of ABS, as well as its peak intensity. The mechanical test results showed that adding TPU decreased the strength but increased the formability and elongation of the samples. Fracture tests showed that the addition of TPU decreased the maximum force needed for a crack to initiate. The force required for crack initiation decreased from 568.4 N for neat ABS to 335.3 N for ABS80 and 123.2 N for ABS60. The ABS60 blend exhibited the highest strength against crack growth, indicating that TPU can change the behavior of ABS from brittle to ductile. Shear test results and SEM images also showed good adhesion strength between the printed samples for all three specimens, indicating their good printability. Adding TPU resulted in a reduction in the size and number of voids and holes between the printed layers

4D printing of polyvinyl chloride (PVC): a detailed analysis of microstructure, programming, and shape memory performance

In this research, polyvinyl chloride (PVC) with excellent shape-memory effects is 4D printed via fused deposition modeling (FDM) technology. An experimental procedure for successful 3D printing of lab-made filament from PVC granules is introduced. Macro- and microstructural features of 3D printed PVC are investigated by means of wide-angle X-ray scattering (WAXS), differential scanning calorimetry (DSC), and dynamic mechanical thermal analysis (DMTA) techniques. A promising shape-memory feature of PVC is hypothesized from the presence of small close imperfect thermodynamically stable crystallites as physical crosslinks, which are further reinforced by mesomorphs and possibly molecular entanglement. A detailed analysis of shape fixity and shape recovery performance of 3D printed PVC is carried out considering three programming scenarios of cold (Tg −45 °C), warm (Tg −15 °C), and hot (Tg +15 °C) and two load holding times of 0 s, and 600 s under three-point bending and compression modes. Extensive insightful discussions are presented, and in conclusion, shape-memory effects are promising, ranging from 83.24% to 100%. Due to the absence of similar results in the specialized literature, this paper is likely to fill a gap in the state-of-the-art shape-memory materials library for 4D printing, and provide pertinent results that are instrumental in the 3D printing of shape-memory PVC-based structures