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The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR, Entropy and ERGAS values. [ABSTRACT FROM AUTHOR]

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques. [ABSTRACT FROM AUTHOR]

The analysis of Parkinson's disease (PD) is an inspiring task that necessitates the analysis of numerous motor and non-motor indications. During analysis, some abnormalities are considered as important symptoms to analyze the disease. Hence, this research introduced the proposed chronological smart sunflower optimization Algorithm (CSSFOA) for classifying the PD from voice data and voice signal samples. For voice signal, the input signals are pre-processed by the Gaussian filter, and then the significant features are extracted from it. The selection of optimal features is done by the chronological smart flower optimization Algorithm (CSFOA). The proposed CSFOA-based feature selection method considered the features by the Bray–Curtis distance. The PD classification is done by the ZF-Net which is trained by proposed CSSFOA to increase the performance of classification. The experimental result reveals that the proposed CSSFOA_ZF-Net algorithm got a better testing accuracy of 0.945, a sensitivity of 0.919, and a specificity of 0.957. [ABSTRACT FROM AUTHOR]

Around the world, population growth and urbanization are rapidly increasing, resulting in high waste generation. When waste is not properly managed, it creates serious health issues, and methane (CH4) generation and other toxic pollutants will lead to global climatic changes. The technologies that involve converting the waste into electricity, biofuel, etc... are very expensive and amount to 20-50% of municipal budgets. An automatic waste segregating and composting system, which helps to segregate the waste into metal and biodegradable materials by using an Arduino and various sensors, is proposed. After being crushed in the composting process, biodegradable waste is used to make fertilizers and achieve the required level of compost by maintaining and controlling its parameters. By using an ultrasonic sensor, the system determines whether the bin is empty or full. It also gives data about the bin status by using "THINGVIEWFREE" app. [ABSTRACT FROM AUTHOR]

The article describes the case of a 52-year old male patient with swelling in the right mandibular edentulous region and underwent fine needle aspiration cytology (FNAC) which identified a live mite feeding on blood products. The patient's blood smears showed fungi organisms in hyphae and conidial form, hypersegmented neutrophils and macrocytic red blood cells suggestive of nutritional deficient anemia, which predisposes parasitic infection. He received antifungal treatment.

Greenhouse technology is a way to improve crop production. Technology has advanced swiftly in the fields of agriculture and food production and is still paving the way for the optimization and achievement of maximum plant growth. In the current age of Android smartphone applications, a precise mechanism would undoubtedly bring about change. The Internet of Things (IoT) is one of the latest achievements in the field of information and communication technologies, providing global communication and control of sensors, devices, and users with information. This paper proposes a greenhouse monitoring and control system using IoT. The temperature sensor, air quality sensor, soil moisture sensor, Light Dependent Resistors (LDR) sensor, Liquid-Crystal Display (LCD) module, 12-volt Direct Current (DC) fan, electric bulb, water pump motor, and the servo motor with the Internet of Things (IoT) technology make up this greenhouse's monitoring and control system to measure nitrous oxide, temperature, humidity, illumination, CO, and irrigation to create a plant-2 friendly atmosphere. The farmers may use the IoT platform to receive the collected environmental parameter data to their smartphones in online mode so they can make the correct decisions without visiting the field. [ABSTRACT FROM AUTHOR]

Since the pandemic of the novel coronavirus (COVID-19) disease began in 2019, social isolation and quarantining have become universally accepted procedures. Frequent hospital contact visits are avoided as a result of the full acceptance of the aforementioned control methods. However, some patients still need routine monitoring for their physiological needs in order to live healthier lives. However, due to the risk of contracting the illness, doctors and nurses are unable to physically monitor the patients. Many people who are arriving in their respective hometowns from various locations are being confined at home due to the ongoing COVID-19 problem in order to protect other people. Interestingly, contact-based monitoring is now viewed as optional due to recent technological advancements in the fields of Internet of Things (IOT) technology and healthcare systems. In order to accomplish this, a home-based IOT-based health monitoring system for quarantined individuals is suggested. Being an infectious and contagious disease, coronavirus disease (COVID-19) is. As a result, we should regularly assess their state of health. The major purpose of this system is to monitor the health metrics like heartbeat, temperature, and presence of harmful gas in the environment by using Internet of Things (IOT) (IOT). On Embedded C, we create a piece of code that defines the threshold value. By gathering the raw values from the sensors, Arduino UNO processes these characteristics. The sensors that we suggested are the blood pressure, body temperature, and respiration sensors. The patient's current health condition will be continuously tracked and updated on the web server. A buzzer alert will be generated if any of the parameters exceeds the defined threshold levels, and it will use GSM technology to send an SMS to the recommended doctor and emergency contacts. [ABSTRACT FROM AUTHOR]

Since the pandemic of the novel coronavirus (COVID-19) disease began in 2019, social isolation and quarantining have become universally accepted procedures. Frequent hospital contact visits are avoided as a result of the full acceptance of the aforementioned control methods. However, some patients still need routine monitoring for their physiological needs in order to live healthier lives. However, due to the risk of contracting the illness, doctors and nurses are unable to physically monitor the patients. Many people who are arriving in their respective hometowns from various locations are being confined at home due to the ongoing COVID-19 problem in order to protect other people. Interestingly, contactbased monitoring is now viewed as optional due to recent technological advancements in the fields of Internet of Things (IOT) technology and healthcare systems. In order to accomplish this, a home-based IOT-based health monitoring system for quarantined individuals is suggested. Being an infectious and contagious disease, coronavirus disease (COVID-19) is. As a result, we should regularly assess their state of health. The major purpose of this system is to monitor the health metrics like heartbeat, temperature, and presence of harmful gas in the environment by using Internet of Things (IOT) (IOT). On Embedded C, we create a piece of code that defines the threshold value. By gathering the raw values from the sensors, Arduino UNO processes these characteristics. The sensors that we suggested are the blood pressure, body temperature, and respiration sensors. The patient's current health condition will be continuously tracked and updated on the web server. A buzzer alert will be generated if any of the parameters exceeds the defined threshold levels, and it will use GSM technology to send an SMS to the recommended doctor and emergency contacts. [ABSTRACT FROM AUTHOR]

Modern technology has revolutionized automation. Security is at high priority with increasing automation. Today, to help people feel comfortable, video surveillance cameras are installed in public places like schools, hospitals, and other buildings. The main goal of this research work is to automatically collect vehicle images with a camera using a Raspberry Pi and recognising the licence plate of the vehicles. Vehicle number plate recognition is a challenging but crucial system. This is highly helpful for automating toll booths, identifying automated signal violators, and identifying traffic regulation violators. In this work, a Raspberry Pi is used for vehicle license plate recognition, which uses image processing to automatically recognize license plates. Incoming camera footage is continuously processed by the system to look for any signs of number plates. When the camera detects a number plate, Optical Character Recognition (OCR) technique is used to process the image and extract the number from it. The distance to an object is calculated by a sensor utilizing sound waves. The extracted number is then displayed by the system. This can be used for additional authentication. [ABSTRACT FROM AUTHOR]

In remote sensing, the land use and land cover (LULC) play a vital role in analyzing Earth information for human development. These objects of change detections are helpful for finding the increased or decreased areas due to limitations of Landsat panchromatic images (gray scale) are not appearing identification of land object areas. To overcome this problem, multispectral bands are applied to individual channels by using principal component analysis (PCA) and morphological operations. Classification can then be performed for the identification of objects. This study focused on separating forests, land, and the vegetation area, including areas under crops and urban development. Regions of localized change in a multitemporal data set were enhanced using dimensional reduction and edge sharp techniques on a simple data set. This technique is feasible for extracting LULC information through remote sensing applications. The performance of the proposed method is superior to that of conventional methods, such as PCA, the normalized difference vegetation index (NDVI) method, and the band combination method. The NDVI and normalized difference water index are used to monitor the changes in the vegetation and water level, respectively, over an interval of time. After image processing, the changes in the area under forests, agriculture land, urban development, and water were determined using unsupervised change detection. The accuracy of the proposed technique was calculated using k-means clustering. The calculated accuracy indicates that the proposed method provides superior results compared with standard methods. [ABSTRACT FROM AUTHOR]

This study estimates the tipping points of financial development in the finance – growth dynamics in the case of OECD countries as they experience considerably higher levels of financial development. Using a unique balanced panel data set of 31 years from 1983 to 2013 for 27 OECD countries, we provide new evidence on the presence of nonlinearity as there is an inverted U-shaped relationship between finance and growth in the long run. The results show that there exists a tipping point of the finance-growth relationship estimated at 141.6% of GDP. We find that surpassing the threshold level results in deceleration of economic growth as excess finance is undesirable. Based on the panel Granger causality test results, we show that financial development should be associated with optimal growth performance. Our findings for OECD countries provide some useful inferences to the emerging and developing economies in designing their financial development strategies. [ABSTRACT FROM AUTHOR]

Abstract: This study explores the causal link between financial development and economic growth in advanced economies as these countries experience significantly higher levels of financial development. Using a fully balanced panel of 24 economies from 1983 to 2013, the study shows that when imposing a linear relationship, the financial development and economic growth are negatively associated in the long run. The panel granger causality tests establish the bidirectional causality between the financial development variables with growth. Employing an alternate approach of moderated mediation effect framework, we notice that the association of financial development with economic growth is moderated by the negative impacts of inflation, interest rate, and population dependency; and the positive mediation effect of trade openness. The policy implication is that there is a need to rein in inflation and real interest rates and enhance trade openness to optimize the benefits of growing financial development on economic growth. [ABSTRACT FROM AUTHOR]

Globalization leads financial market of one country to become more integrated with other countries. In this environment the knowledge of dependency between the stock market helps the investors and portfolio managers for international investment and diversification. This paper investigates the linkage among BRIC market indices, i.e. Brazil, Russia, India and China, and their relation with US market and oil price. Restricted Vector Auto Regression (VAR) model is employed to gauge the interdependencies among the selected stock market and the short-term relationship is tested with the help of Granger causality test by using the daily data from 2nd January 1998 to 30th September 2014. Based on the result of Final prediction error (FPE) and Akaike information criterion (AIC) six lag lengths is selected as the optimal lag for the VAR model. The results of VAR model indicate that the return of Brazil, Russia and India having co-movement with US market and oil price. However, China does not exhibit the co-movement with US stock market but it does with oil price. Brazil market causes more other market than is caused by them, China and Russia Granger-cause each other. However, to the lesser extent, India Granger-cause China and Russian market. Us market has high significant positive influence than the oil price on Brazil, Russia and India. On the other hand, Oil price has significantly positive influence on China where as US market not influences the Chinese market. [ABSTRACT FROM AUTHOR]

Studies have reported opposing effects of high-fat (HF) diet and mechanical stimulation on lineage commitment of the bone marrow stem cells. Yet, how bone marrow modulates its gene expression in response to the combined effects of mechanical loading and a HF diet has not been addressed. We investigated whether early-life (before onset of sexual maturity at 6 weeks of age) voluntary physical activity can modulate the effects of a HF diet on male Sprague Dawley rats. In the bone marrow, early-life HF diet resulted in adipocyte hypertrophy and a pro-inflammatory and pro-adipogenic gene expression profile. The bone marrow of the rats that undertook wheel exercise while on a HF diet retained a memory of the early-life exercise. This memory lasted at least 60 days after the cessation of the voluntary exercise. Our results are consistent with the marrow adipose tissue having a unique response to HF feeding in the presence or absence of exercise. [ABSTRACT FROM AUTHOR]

Mechanical loading is essential to maintain optimal skeletal health. Despite the fact that early-life exercise has positive, long-lasting effects on the musculo-skeletal system, the response of the musculo-skeletal system to spontaneous low-impact exercise has been poorly studied. Previously, we identified subtle morphological changes in the femoral diaphysis of exercised animals compared to nonexercised controls. We hypothesized that significant changes in gene expression of cells should precede significant measurable phenotypic changes in the tissues of which they are part. Here, we employed RNA-Seq to analyse the transcriptome of the cortical bone from the femoral mid-diaphysis of prepubertal male Sprague-Dawley rats that were assigned to control ( CON); bipedal stance ( BPS); or wheel exercise ( WEX) groups for 15 days. We identified 808 and 324 differentially expressed transcripts in the BPS and WEX animals respectively. While a number of transcripts change their levels in an exercise-specific manner, we identified 191 transcripts that were differentially expressed in both BPS and WEX. Importantly, we observed that the exercise mode had diametrically opposite effects on transcripts for multiple genes within the integrin-linked kinase ( ILK) and Ca2+ signaling pathways such that they were up-regulated in BPS and down-regulated in WEX. The findings are important for our understanding of possible ways in which different exercise regimens might affect bone when normal activities apply mechanical stimuli during postnatal growth and development. [ABSTRACT FROM AUTHOR]

Following our recent synthesis and characterization of three new cationic polyelectrolytes with subtle hydrophobic variability, this paper reports their physical and chemical properties in aqueous media in relation to their chemical structure. Aryl substituted cationic polyelectrolytes varying with their charge density are reported for the first time. Viscosity studies show that these polymers display typical polyelectrolytic behavior. The flocculation efficiency of the polyelectrolytes was investigated with different counter ions. The zeta potential of the polyelectrolytes indicates the charge of the mono and diquaternary ammonium salts which is supported by chloride analysis. The morphology of polymer before and after flocculation was investigated. The introduction of methylene group and quaternary nitrogen play an important role in the flocculation process. It was shown that increasing the hydrophobicity and charge density of the aryl substituted polymer affects the flocculation in the industrial tannery effluent and bentonite suspension. [ABSTRACT FROM AUTHOR]

We examine the relationship between spot and futures prices for selected five agricultural commodities, namely, Chilli, Coriander, Jeera, Pepper and Turmeric. We considered all the contracts of the above commodities over a period of 36 months, from Jan 2008 to Dec 2010. The study examined the existence of unit root in the data series by employing Augmented Dickey-Fuller (ADF) test and we found the existence of long run relationship between selected spot and futures market using Johansen cointegration test and the presence of disequilibrium between markets in short run by employing Vector Error Correction Model (VECM). [ABSTRACT FROM AUTHOR]