Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods

Multiscale materials design of hard coatings for improved fracture resistance and thermal stability

Physical vapor deposited hard coatings comprised of cubic (c) transition metal (TM)-Al-N, and (TM)-Si-N are the current work horse materials for a large number of metal cutting and wear resistant applicatíons to light against the extreme conditions of temperature and stress simultaneously. In spite of a high degree of sophisticatíon in terms of material choice and microstructural design, a lower fracture resistance and limited thermal stability of the coatings remains a technological challenge in the field. The lower fracture resistance ofthe coating is an inherent material property. Limited thermal stability in the TM-Al-N system is associated with the transformation of metastable c -AIN to its stable wurtzite (w)-AIN phase ata temperature above 900 oC resulting an undesirable hardness drop. The current work shows how to overcome these challenges by manipulaling the coating material at different length scales, i.e. microstructure, crystal and interface structure, and alloy design. The endeavor of multiscale materials design is achieved by converging a deeper material and process knowledge to result specific structural modification over multiple length scales by alloying transition metal nitrides with AIN and SiNxs following. Microstructure variation is achieved in ZrN coating by alloying it with SiNx, where the surface segregated SiNx breaks down the columnar structure and evolves a self-organized nanocomposite structure with a hardness variation from 37 ±2 GPa to 26 ±1 GPa. The indentation induced fracture studies reveal crack deflection for the colum nar coating, likely a long the coiumn boundaries. The crack deflection olfers additional energy dissipative mechanisms that make the columnar structured coating more fracture resistant, which is not the case fur the nanocomposite coating in spite of its lower hardness. Crystal structure of AIN is variad between stable wurtzite structure to metastable cubic structure in the ZrAIN alloy by adapting a mullilayer structure and tuning the layerthickness. The multilayer consisting c-AIN layer shows a hardness of 34 ±1 GPa anda twofold enhancement in the critica! force to cause an indentation induced surface crack compared to the multilayer containing w-AIN in spite of a lower hardness for the later case. The higher fracture resistance is discovered to be ca u sed by stress- induced transformation of /IJN from its metastable cubic structure to its thermodynamically stable wurtzite structure associated with a molar volume expansion of20% that builds up local compressive stress zones delay;ng the onset and propagation of the cracks. This is in fact the first experím en tal data point for the stress-induced transfurmation toughening in a hard coatíng. The current work also demonstrates a concept of im proving the thermal stabilíty ofTM-Al-N by m odifying the interface structure between w-AIN and c-TMN. A popular belief in the field is that AIN in lis stable wurtzite structure is detrimental to coating hardness, and hence the curren! material design strategy Is to force AIN in metas table cubic phase that confines the application temperature (- 900 oC). In contrast, here it is shown that the w-AIN offers a high hardness provided if it is grown (semi-)coherent to c-TMN. This is experimentally shown for lhe multilayer system ofTiN/ZrAIN. The interface structure between the c-TiN, c-ZrN and w-AIN is transformed from incoherent to (semi-)coherent structure bytuning the growth conditions under a favorable crystallographic template. Furthennore, the low energy(semi-) coherent interface structure between w-AIN and c- TiN, c- ZrN display a high thermal stability, causing a high and more stable hardness up to an annealing temperature of 1150 oC with a value of34± 1.5 GPa. This value is 50 % higher comparad to the state-of-the-art monolithic and multilayered Ti-/IJ -N and Zr-Al-N coating containing incoherent w-AIN. Finally, an entropy based alloy design concept is explorad to form a thermodynamicLos recubrimientos duros formados por metales de transición (TM) cúbicos -AlN, y -SiN depositados mediante fase de vapor (CVD) son materiales extensamente utilizados en gran número de aplicaciones de corte y de desgaste bajo condiciones extremas de temperatura y solicitaciones mecánicas. A pesar de un alto grado de sofisticación en cuanto a la selección del material y el diseño microestructural, la baja resistencia a la fractura y la limitada estabilidad térmica sigue siendo un importante reto tecnológico. La variación microestructural en los recubrimientos de ZrN se controla mediante la aleación con SiNx, ya que la segregación superficial de SiNx rompe la estructura columnar y evoluciona a un nanocompuesto autoorganizado con una dureza de entre 37 ±2 GPa y 26 ±1 GPa. Las grietas producidas por indentación muestran la existencia de deflexión de grieta, lo que proporciona un mecanismo de disipación de energía adicional, haciendo de este material más resistente a la generación de grieta.La estructura cristalina del recubrimiento de AlN se varía entre la fase estable wurtzita y la fase cúbica estable ZrAlN mediante el control de la estructura y el espesor de la arquitectura multicapa. El recubrimiento multicapa formado por la fase c-AlN presenta una dureza de 34 ±1 GPa y una resistencia a la generación de grietas por indentación dos veces mayor comparado con el recubrimiento multicapa formado por w-AlN, aunque éste presente una dureza menor. La mayor resistencia a fractura está causada por la transformación inducida por tensión de AlN desde la fase cúbica metaestable a la fase wurtzita termodinámicamente estable acompañada de una expansión molar del 20%, resultando en una generación de tensiones compresivas que retarda la generación y propagación de grietas. Esta es la primera vez que se reporta la existencia de transformación catalizada por tensión en recubrimientos duros. En esta tesis también se demuestra el concepto de mejorar la estabilidad térmica de los recubrimientos basados en TM-Al-N mediante la modificación de la estructura interfacial entre las fases w-AlN y c-TMN. En general la existencia de AlN en su fase estable wurtzita puede ser detrimental para la dureza, y por lo tanto se suele depositar el material en la fase cúbica, lo que limita la temperatura de utilización (~ 900 oC). Esta dureza es un 50%mayor de la dureza reportada para recubrimientos monolíticos y multicapas de Ti-Al-N y Zr-Al-N que contengan fase incoherente de w-AlN. Finalmente, el concepto de aleaciones de alta entropía se utiliza para depositar una solución sólida termodinámicamente estable del sistema TM-Al-N que presenta una entalpía de mezcla positiva. Elementos de aleación multi-principales de (AlTiVCrNb)N se utilizan para formar una solución sólida cúbica . La alta entropía configuracional en la mezcla es mayor que la entalpía, por lo que se espera una formación de solución sólida estabilizada a temperaturas mayores de 1000K. Sin embargo, a temperaturas elevadas, la optimización entre la minimización de la energía de interacción y la maximización del desorden configuracional causa la precipitación de AlN en su estructura wurtzita estable, y la solución sólida cúbica está únicamente confinada entre TiN, CrN , VN y NbN que tienen baja entalpía de mezcla. En resumen, esta tesis presenta soluciones tecnológica a dos retos importantes en el campo. Se consigue una mejora significativa en la resistencia a fractura en los recubrimientos mediante la selección de materiales y el diseño microestructural mediante mecanismos de deflexión de grieta y transformación de fase asistida por tensión. Así mismo, se aumenta la estabilidad térmica de recubrimientos TM-Al-N mediante una nueva microestructura consistente en c-TMN y w-AlN termodinámicamente estable con una estructura interfacial (semi-)coherente de baja energía

Growth and thermal stability of TiN/ZrAlN: Effect of internal interfaces

Wear resistant hard films comprised of cubic transition metal nitride (c-TMN) and metastable c-AlN with coherent interfaces have a confined operating envelope governed by the limited thermal stability of metastable phases. However, equilibrium phases (c-TMN and wurtzite(w)-AlN) forming semicoherent interfaces during film growth offer higher thermal stability. We demonstrate this concept for a model multilayer system with TiN and ZrAlN layers where the latter is a nanocomposite of ZrN- and AlN- rich domains. The interfaces between the domains are tuned by changing the AlN crystal structure by varying the multilayer architecture and growth temperature. The interface energy minimization at higher growth temperature leads to formation of semicoherent interfaces between w-AlN and c-TMN during growth of 15 nm thin layers. Ab initio calculations predict higher thermodynamic stability of semicoherent interfaces between c-TMN and w-AlN than isostructural coherent interfaces between c-TMN and c-AlN. The combination of a stable interface structure and confinement of w-AlN to nm-sized domains by its low solubility in c-TMN in a multilayer, results in films with a stable hardness of 34 GPa even after annealing at 1150 °C.Peer ReviewedPostprint (author's final draft

Time-Distributed Attention-Layered Convolution Neural Network with Ensemble Learning using Random Forest Classifier for Speech Emotion Recognition

Speech Emotion Detection (SER) is a field of identifying human emotions from human speech utterances. Human speech utterances are a combination of linguistic and non-linguistic information. Nonlinguistic SER provides a generalized solution in human–computer interaction applications as it overcomes the language barrier. Machine learning and deep learning techniques were previously proposed for classifying emotions using handpicked features. To achieve effective and generalized SER, feature extraction can be performed using deep neural networks and ensemble learning for classification. The proposed model employed a time-distributed attention-layered convolution neural network (TDACNN) for extracting spatiotemporal features at the first stage and a random forest (RF) classifier, which is an ensemble classifier for efficient and generalized classification of emotions, at the second stage. The proposed model was implemented on the RAVDESS and IEMOCAP data corpora and compared with the CNN-SVM and CNN-RF models for SER. The TDACNN-RF model exhibited test classification accuracies of 92.19 percent and 90.27 percent on the RAVDESS and IEMOCAP data corpora, respectively. The experimental results proved that the proposed model is efficient in extracting spatiotemporal features from time-series speech signals and can classify emotions with good accuracy. The class confusion among the emotions was reduced for both data corpora, proving that the model achieved generalization

Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model

Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications, a semi-empirical model (viz., Water Cloud Model (WCM)) is often used to estimate vegetation descriptors individually. However, a simultaneous estimation of these vegetation descriptors would be logical given their inherent correlation, which is seldom preserved in the estimation of individual descriptors by separate inversion models. This functional relationship between biophysical parameters is essential for crop yield models, given that their variations often follow different distribution throughout crop development stages. However, estimating individual parameters with independent inversion models presume a simple relationship (potentially linear) between the biophysical parameters. Alternatively, a multi-target inversion approach would be more effective for this aspect of model inversion compared to an individual estimation approach. In the present research, the multi-output support vector regression (MSVR) technique is used for inversion of the WCM from C-band dual-pol Sentinel-1 SAR data. Plant Area Index (PAI, m2 m−2) and wet biomass (W, kg m−2) are used as the vegetation descriptors in the WCM. The performance of the inversion approach is evaluated with in-situ measurements collected over the test site in Manitoba (Canada), which is a super-site in the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiment network. The validation results indicate a good correlation with acceptable error estimates (normalized root mean square error–nRMSE and mean absolute error–MAE) for both PAI and wet biomass for the MSVR approach and a better estimation with MSVR than single-target models (support vector regression–SVR). Furthermore, the correlation between PAI and wet biomass is assessed using the MSVR and SVR model. Contrary to the single output SVR, the correlation between biophysical parameters is adequately taken into account in MSVR based simultaneous inversion technique. Finally, the spatio-temporal maps for PAI and W at different growth stages indicate their variability with crop development over the test site.This research was supported in part by Shastri Indo-Candian Institute, New Delhi, India and the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), in part by the European Funds for Regional Development under project TEC2017-85244-C2-1-P

Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.This work was supported by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council

ZrN based Nanostructured Hard Coatings : Structure-Property Relationship

Ever since the hard coatings have been introduced, there has been a constant push for better mechanical properties, which motivates for deeper understanding of the microstructure-mechanical properties correlation. The aim of this thesis is to extend the knowledge on how microstructural variation influences the deformation, fracture and wear behavior of ZrN based nanostructured coatings. Few microns thick, monolithic Zr-Si-N and multilayered Zr-Al-N coatings were deposited by reactive arc deposition and unbalanced reactive magnetron sputtering techniques respectively. The microstructures of the coatings were studied using xray diffraction, transmission electron microscopy and scanning electron microscopy. Indentation induced plastic deformation and fracture behavior was visualized by extracting the lamellae under the indent using focused ion beam milling technique combined with transmission electron microscopy. Wear behavior of the coatings were characterized by reciprocating sliding wear test following microscopic observations of the wear track. Monolithic Zr-Si-N coating shows a systematic variation of microstructure, hardness and fracture resistance as a function of Si content. Si forms a substitutional solid solution in the cubic ZrN lattice up to 1.8 at. % exhibiting a fine columnar structure. Further Si additions result in precipitation of an amorphous SiNX phase in the form of a nanocomposite structure (nc ZrN- a SiNX) that is fully developed at 6.3 at. % Si. Dislocation based homogeneous deformation is the dominating plastic deformation mode in the columnar structure, while grain boundary sliding mediated plastic deformation causing localized heterogeneous shear bands dominates in the nanocomposite structure. Indentation induced cracking shows the higher fracture resistance for columnar structure compared to the nanocomposite coatings. Crack branching and deflection were observed to be the key toughening mechanisms operating in the columnar structured coating. Reciprocating wear tests on these coatings show a bi-layer wear mode dominated by tribo-oxidation. Nanocomposite coatings offer superior resistance to both static and tribo-oxidation, resulting in higher wear resistance even though they are soft and brittle. Monolithic and multilayers of Zr0.63Al0.37N coatings were grown at a deposition temperature of 700 °C. Monolithic Zr0.63Al0.37N coating shows a chemically segregated nanostructure consisting of wurtzite-AlN and cubic-ZrN rich domains with incoherent interfaces. When the same composition is sandwiched between ZrN nanolaminates, Zr0.63Al0.37N shows a layer thickness dependent structure, which results in systematic variation of hardness and fracture resistance of the coatings. Maximum hardness is achieved when the Zr0.63Al0.37N layer shows semicoherent wurtzite-AlN rich domains. While the maximum toughness is achieved when AlN- rich domains are pseudomorphically stabilized into cubic phase. Stress induced transformation of metastable cubic-AlN to thermodynamically stable wurtzite-AlN was suggested to be the likely toughening mechanism

Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data

Rice growth monitoring using Synthetic Aperture Radar (SAR) is recognized as a promising approach for tracking the development of this important crop. Accurate spatio-temporal information of rice inventories is required for water resource management, production risk occurrence, and yield forecasting. This research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index (GRVI) for monitoring rice growth at different phenological stages. The GRVI is derived using the concept of a geodesic distance (GD) between Kennaugh matrices projected on a unit sphere. We utilized this concept of GD to quantify a similarity measure between the observed Kennaugh matrix (representation of observed Polarimetric SAR information) and the Kennaugh matrix of a generalized volume scattering model (a realization of scattering media). The similarity measure is then modulated with a factor estimated from the ratio of the minimum to the maximum GD between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. In this work, we utilize a time series of C-band quad-pol RADARSAT-2 observations over a semi-arid region in Vijayawada, India. Among the several rice cultivation practices adopted in this region, we analyze the growth stages of direct seeded rice (DSR) and conventional tansplanted rice (TR) with the GRVI and crop biophysical parameters viz., Plant Area Index – PAI. The GRVI is compared for both rice types against the Radar Vegetation Index (RVI) proposed by Kim and van Zyl. A temporal analysis of the GRVI with crop biophysical parameters at different phenological stages confirms its trend with the plant growth stages. Also, the linear regression analysis confirms that the GRVI outperforms RVI with significant correlations with PAI (r ≥ 0.83 for both DSR and TR). In addition, PAI estimations from GRVI show promising retrieval accuracy with Root Mean Square Error (RMSE) <1.05m2 m−2 and Mean Absolute Error (MAE) <0.85m2 m−2.This work was partially supported by the Spanish Ministry of Science, Innovation and Universities, the State Research Agency (AEI) and the European Fund for Regional Development (EFRD) under project TEC2017-85244-C2-1-P