Search results for: Remote sensing

Authors: Yu Zhang, Jie Wu, Li Dong

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Rural tourism is the service industry which regards the agricultural production, rural life, rural nature and cultural landscape as the tourist attraction. It aims to meet the needs of the city tourists such as country sightseeing, vacation, and leisure. According to the difference of the tourist resources, the rural settlements can be divided into different types: The type of tourism resources, scenic spot, and peri-urban. In the past ten years, the rural tourism has promoted the industrial transformation and economic growth in rural areas of China. And it is conducive to the coordinated development of urban and rural areas and has greatly improved the ecological environment and the standard of living for farmers in rural areas. At the same time, a large number of buildings and sites are built in the countryside in order to enhance the tourist attraction and the ability of tourist reception and also to increase the travel comfort and convenience, which has significant influence on the spatial evolution of the village settlement. This article takes the XiangYing Subdistrict, which is in JinPu District of Dalian in China as the exemplification and uses the technology of Remote Sensing (RS), Geographic Information System (GIS) and the technology of Landscape Spatial Analysis to study the influence of the rural tourism development in the rural settlement spaces in four steps. First, acquiring the remote sensing image data at different times of 8 administrative villages in the XiangYing Subdistrict, by using the remote sensing application EDRAS8.6; second, vectoring basic maps of XiangYing Subdistrict including its land-use map with the application of ArcGIS 9.3, associating with social and economic attribute data of rural settlements and analyzing on the rural evolution visually; third, quantifying the comparison of these patches in rural settlements by using the landscape spatial calculation application Fragstats 3.3 and analyzing on the evolution of the spatial structure of settlement in macro and medium scale; finally, summarizing the evolution characteristics and internal reasons of tourism-oriented rural settlements. The main findings of this article include: first of all, there is difference in the evolution of the spatial structure between the developing rural settlements and undeveloped rural settlements among the eight administrative villages; secondly, the villages relying on the surrounding tourist attractions, the villages developing agricultural ecological garden and the villages with natural or historical and cultural resources have different laws of development; then, the rural settlements whose tourism development in germination period, development period and mature period have different characteristics of spatial evolution; finally, the different evolution modes of the tourism-oriented rural settlement space have different influences on the protection and inheritance of the village scene. The development of tourism has a significant impact on the spatial evolution of rural settlement. The intensive use of rural land and natural resources is the fundamental principle to protect the rural cultural landscape and ecological environment as well as the critical way to improve the attraction of rural tourism and promote the sustainable development of countryside.

Keywords: landscape pattern, rural settlement, spatial evolution, tourism-oriented, Xiangying Subdistrict

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Authors: Carolina Morales, Ricardo Castro-Huerta, Enrique A. Mundaca

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The edaphic fauna is the main factor involved in the transformation of nutrients and soil decomposition processes. Edaphic organisms are highly sensitive to soil disturbances, which normally causes changes in the composition and abundance of such organisms. Fire is known to be a disturbing factor since it affects the physical, chemical and biological properties of the soil and the whole ecosystem. During the summer (December-March) of 2017, Chile suffered the major fire events recorded in its modern history, which affected a vast area and a number of ecosystem types. The objective of this study was first to use remote sensing satellite images and GIS (Geographic Information Systems) to assess and identify levels of fire severity in disturbed areas and to compare the responses of the soil mesofauna diversity among such areas. We identified four areas (treatments) with an ascending level of severity, namely: mild, medium, high severity, and free of fire. A non-affected patch of forest was established as a control. Three samples from each treatment were collected in the form of a soil cube (10x10x10 cm). Edaphic mesofauna was obtained from each sample through the Berlese-Tullgren funnel method. Collected specimens were quantified and identified, using the RTU (Recognisable Taxonomic Unit) criterion. Diversity was analysed using inferential statistics to compare Simpson and Shannon-Wiener indexes across treatments. As predicted, the unburned forest patch (control) exhibited higher diversity values than the treatments. Significantly higher diversity values were recorded in those treatments subjected to lower fire severity. We conclude that remote sensing zoning is an adequate tool to identify different levels of fire severity and that an edaphic mesofauna is a group of organisms that qualify as good bioindicators for monitoring soil recovery after fire events.

Keywords: bioindicator, Chile, fire severity level, soil

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Authors: Arpan Kumar Nayak, Debabrata Pradhan

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A simple, single-step and one-pot hydrothermal method was employed to synthesize WO₃-SnO₂ mixed nanostructured metal oxides at 200°C in 12h. The SnO₂ nanoparticles were found to be uniformly decorated on the WO₃ nanoplates. Though it is widely known that noble metals such as Pt, Pd doping or decoration on metal oxides improve the sensing response and sensitivity, we varied the SnO₂ concentration in the WO₃-SnO₂ mixed oxide and demonstrated their performance in ammonia, ethanol and acetone sensing. The sensing performance of WO₃-(x)SnO₂ [x = 0.27, 0.54, 1.08] mixed nanostructured oxides was found to be not only superior to that of pristine oxides but also higher/better than that of reported noble metal-based sensors. The sensing properties (selectivity, limit of detection, response and recovery times) are measured as a function of operating temperature (150-350°C). In particular, the gas selectivity is found to be highly temperature-dependent with optimum performance obtained at 200°C, 300°C and 350°C for ammonia, ethanol, and acetone, respectively. The present results on cost effective WO₃-SnO₂ sensors can find potential application in human breath analysis by noninvasive detection.

Keywords: gas sensing, mixed oxides, nanoplates, ammonia, ethanol, acetone

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Authors: Basit Aftab, Zhichao Wang, Feng Zhongke

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Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies.

Keywords: land use land cover, Markov chain model, multi-layer perceptron, random forest, sustainable land, remote sensing.

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Authors: S. Yelubayev, V. Ten, B. Albazarov, E. Sarsenbayev, К. Аlipbayev, A. Shamro, Т. Bopeyev, А. Sukhenko

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Currently in Kazakhstan much attention is paid to the development of space branch. Successful launch of two Earth remote sensing satellite is carried out, projects on development of components for satellite are being carried out. In particular, the project on development of star tracker experimental model is completed. In the future it is planned to use this experimental model for development of star tracker prototype. Main stages of star tracker experimental model development are considered in this article.

Keywords: development, prototype, satellite, star tracker

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Authors: N. Tochukwu, M. Mukhopadhyay, A. Mohamed

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It is no new that Nigeria faces a continual power shortage problem due to its vast population power demand and heavy reliance on nonrenewable forms of energy such as thermal power or fossil fuel. Many researchers have recommended using geothermal energy as an alternative; however, Past studies focus on the geophysical & geochemical investigation of this energy in the sedimentary and basement complex; only a few studies incorporated the remote sensing methods. Therefore, in this study, the preliminary examination of geothermal resources in the Middle Benue was carried out using satellite imagery in ArcMap. Landsat 8 scene (TIR, NIR, Red spectral bands) was used to estimate the Land Surface Temperature (LST). The Maximum Likelihood Classification (MLC) technique was used to classify sites with very low, low, moderate, and high LST. The intermediate and high classification happens to be possible geothermal zones, and they occupy 49% of the study area (38077km2). Riverline were superimposed on the LST layer, and the identification tool was used to locate high temperate sites. Streams that overlap on the selected sites were regarded as geothermal springs as. Surprisingly, the LST results show lower temperatures (<36°C) at the famous thermal springs (Awe & Wukari) than some unknown rivers/streams found in Kwande (38°C), Ussa, (38°C), Gwer East (37°C), Yola Cross & Ogoja (36°C). Studies have revealed that temperature increases with depth. However, this result shows excellent geothermal resources potential as it is expected to exceed the minimum geothermal gradient of 25.47 with an increase in depth. Therefore, further investigation is required to estimate the depth of the causative body, geothermal gradients, and the sustainability of the reservoirs by geophysical and field exploration. This method has proven to be cost-effective in locating geothermal resources in the study area. Consequently, the same procedure is recommended to be applied in other regions of the Precambrian basement complex and the sedimentary basins in Nigeria to save a preliminary field survey cost.

Keywords: ArcMap, geothermal resources, Landsat 8, LST, thermal springs, MLC

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Chyuan Haur Kao, Hsiang Chen, Yu Sheng Tsai, Chen Hao Hung, Yu Shan Lee

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Dielectric electrolyte-insulator-semiconductor sensing membranes-based biosensors have been intensively investigated because of their simple fabrication, low cost, and fast response. However, to enhance their sensing performance, it is worthwhile to explore alternative materials, distinct processes, and novel treatments. An ISFET can be viewed as a variation of MOSFET with the dielectric oxide layer as the sensing membrane. Then, modulation on the work function of the gate caused by electrolytes in various ion concentrations could be used to calculate the ion concentrations. Recently, owing to the advancement of CMOS technology, some high dielectric materials substrates as the sensing membranes of electrolyte-insulator-semiconductor (EIS) structures. The EIS with a stacked-layer of SiO₂ layer between the sensing membrane and the silicon substrate exhibited a high pH sensitivity and good long-term stability. IGZO is a wide-bandgap (~3.15eV) semiconductor of the III-VI semiconductor group with several preferable properties, including good transparency, high electron mobility, wide band gap, and comparable with CMOS technology. IGZO was sputtered by reactive radio frequency (RF) on a p-type silicon wafer with various gas ratios of Ar:O₂ and was treated with rapid thermal annealing in O₂ ambient. The sensing performance, including sensitivity, hysteresis, and drift rate was measured and XRD, XPS, and AFM analyses were also used to study the material properties of the IGZO membrane. Moreover, IGZO was used as a sensing membrane in dielectric EIS bio-sensor structures. In addition to traditional pH sensing capability, detection for concentrations of Na+, K+, urea, glucose, and creatinine was performed. Moreover, post rapid thermal annealing (RTA) treatment was confirmed to improve the material properties and enhance the multi-analyte sensing capability for various ions or chemicals in solutions. In this study, the IGZO sensing membrane with annealing in O₂ ambient exhibited a higher sensitivity, higher linearity, higher H+ selectivity, lower hysteresis voltage and lower drift rate. Results indicate that the IGZO dielectric sensing membrane on the EIS structure is promising for future bio-medical device applications.

Keywords: dielectric sensing membrane, IGZO, hydrogen ion, plasma, rapid thermal annealing

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Authors: Israel Ropo Orimoloye, Johanes A. Belle, Olusola Adeyemi, Olusola O. Ololade

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With more than 100,000 orbits during the past 20 years, Terra has significantly improved our knowledge of the Earth's climate and its implications on societies and ecosystems of human activity and natural disasters, including drought events. With Terra instrument's performance and the free distribution of its products, this study utilised Terra MOD13Q1 satellite data to assess drought disaster events and its spatiotemporal patterns over the Free State Province of South Africa between 2001 and 2019 for summer, autumn, winter, and spring seasons. The study also used high-resolution downscaled climate change projections under three representative concentration pathways (RCP). Three future periods comprising the short (the 2030s), medium (2040s), and long term (2050s) compared to the current period are analysed to understand the potential magnitude of projected climate change-related drought. The study revealed that the year 2001 and 2016 witnessed extreme drought conditions where the drought index is between 0 and 20% across the entire province during summer, while the year 2003, 2004, 2007, and 2015 observed severe drought conditions across the region with variation from one part to the another. The result shows that from -24.5 to -25.5 latitude, the area witnessed a decrease in precipitation (80 to 120mm) across the time slice and an increase in the latitude -26° to -28° S for summer seasons, which is more prominent in the year 2041 to 2050. This study emphasizes the strong spatio-environmental impacts within the province and highlights the associated factors that characterise high drought stress risk, especially on the environment and ecosystems. This study contributes to a disaster risk framework to identify areas for specific research and adaptation activities on drought disaster risk and for environmental planning in the study area, which is characterised by both rural and urban contexts, to address climate change-related drought impacts.

Keywords: remote sensing, drought disaster, climate scenario, assessment

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Tridipa Biswas, Kamal Pandey

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A Digital Elevation Model (DEM) is a simple representation of a surface in 3 dimensional space with elevation as the third dimension along with X (horizontal coordinates) and Y (vertical coordinates) in rectangular coordinates. DEM has wide applications in various fields like disaster management, hydrology and watershed management, geomorphology, urban development, map creation and resource management etc. Cartosat-1 or IRS P5 (Indian Remote Sensing Satellite) is a state-of-the-art remote sensing satellite built by ISRO (May 5, 2005) which is mainly intended for cartographic applications.Cartosat-1 is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution. One camera is looking at +26 degrees forward while another looks at –5 degrees backward to acquire stereoscopic imagery with base to height ratio of 0.62. The time difference between acquiring of the stereopair images is approximately 52 seconds. The high resolution stereo data have great potential to produce high-quality DEM. The high-resolution Cartosat-1 stereo image data is expected to have significant impact in topographic mapping and watershed applications. The objective of the present study is to generate high-resolution DEM, quality evaluation in different elevation strata, generation of ortho-rectified image and associated accuracy assessment from CARTOSAT-1 data based Ground Control Points (GCPs) for Aglar watershed (Tehri-Garhwal and Dehradun district, Uttarakhand, India). The present study reveals that generated DEMs (10m and 30m) derived from the CARTOSAT-1 stereo pair is much better and accurate when compared with existing DEMs (ASTER and CARTO DEM) also for different terrain parameters like slope, aspect, drainage, watershed boundaries etc., which are derived from the generated DEMs, have better accuracy and results when compared with the other two (ASTER and CARTO) DEMs derived terrain parameters.

Keywords: ASTER-DEM, CARTO-DEM, CARTOSAT-1, digital elevation model (DEM), ortho-rectified image, photogrammetry, RPC, stereo pair, terrain parameters

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Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

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Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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Authors: Ashok Tejankar, Rohan K. Pathrikar

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Maharashtra is covered by heterogeneous flows of Deccan basaltic terrains of upper cretaceous to lower Eocene age. It consist mainly different types of basalt flow, having heterogeneous Geohydrological characters. The study area Aurangabad dist. lies in the central part of Maharashtra. The study area is typically covered by Deccan traps formation mainly basalt type of igneous volcanic rock. The area is located in the survey of India toposheet No. 47M and laying between 19° to 20° north latitudes and 74° to 76° east longitudes. Groundwater is the primary source for fresh water in the study area. There has been a growing demand for fresh water in domestic & agriculture sectors. Due to over exploitation and rainfall failure has been created an irrecoverable stress on groundwater in study area. In an effort to maintain the water table condition in balance, artificial recharge is being implemented. The selection of site for artificial recharge is a very important task in recharge basalt. The present study aims at sitting artificial recharge structure at village Pachod in basaltic terrain of the Godavari-Purna river basin in Aurangabad district of Maharashtra, India. where the average annual rainfall is 650mm. In this investigation, integrated remote sensing and GIS techniques were used and various parameters like lithology, structure, etc. aspect of drainage basins, landforms and other parameters were extracted from visual interpretation of IRS P6 Satellite data and Survey of India (SIO) topographical sheets, aided by field checks by carrying well inventory survey. The depth of weathered material, water table conditions, and rainfall data were been considered. All the thematic information layers were digitized and analyzed in Arc-GIS environment and the composite maps produced show suitable site, depth of bed rock flows for successful artificial recharge in village Pachod to increase groundwater potential of low laying area.

Keywords: hard rock, artificial recharge, remote sensing, GIS

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Authors: Sanu Muhammed C., Nihal Vadakkan, Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards AR/VR applications, the user experience on these devices should be considered, and we are trying to improve user experience in VR. This paper proposes a survey-based solution to improve user experience in VR. By creating a VR environment where users can move a ball from one position to another using a remote controller and another VR environment where users can move a ball from one place to another using hand gestures/ By allowing a set of audience to use these two environments, we can get their feedback. There are two steps in this comparison, 1) Using Hand Gestures To Move Ball In VR Environment: Here, we create a VR environment where two baskets are there, and one ball will be there in a basket. Here users can transfer the ball to another basket using hand gestures. They will be able to move the ball using hand gestures. 2) Using Remote Control To Move Ball In VR Environment: Here, we create a VR environment where two baskets are there, and one ball will be there in a basket. Here users can transfer the ball to another basket using a remote control. They will be able to move the ball using a remote controller. The above two environments are given to users to experience, and their responses will be recorded to compare the user experience in the above two environments.

Keywords: virtual reality, user experience, hand gestures, remote control

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Authors: Gaurav Malik, Satyendra Mourya, Jyoti Jaiswal, Ramesh Chandra

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The attractive and multifunctional properties of ZnO make it a promising material for the fabrication of highly sensitive and selective efficient gas sensors at room temperature. This presented article focuses on the development of highly selective and sensitive H₂ gas sensor based on the Pd-Pt decorated ZnO framework and its sensing mechanisms. The gas sensing performance of sputter made Pd-Pt/ZnO electrode on anodized porous silicon (PSi) substrate toward H₂ gas is studied under low detection limit (2–500 ppm) of H₂ in the air. The chemiresistive sensor demonstrated sublimate selectivity, good sensing response, and fast response/recovery time with excellent stability towards H₂ at low temperature operation under ambient environment. The elaborate selective measurement of Pd-Pt/ZnO/PSi structure was performed towards different oxidizing and reducing gases. This structure exhibited advance and reversible response to H₂ gas, which revealed that the acquired architecture with ZnO framework is a promising candidate for H₂ gas sensor.

Keywords: sputtering, porous silicon, ZnO framework, XPS spectra, gas sensor

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Authors: Gyoutae Park, Woosuk Kim, Sangguk Ahn, Seungmo Kim, Minjun Kim, Jinhan Lee, Youngdo Jo, Jongsam Moon, Hiesik Kim

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In this research, we designed a remote system to test parameters of gas detectors such as gas concentration and initial response time. This testing system is available to measure two gas instruments simultaneously. First of all, we assembled an experimental jig with a square structure. Those parts are included with a glass flask, two high-quality cameras, and two Ethernet modems for transmitting data. This remote gas detector testing system extracts numerals from videos with continually various gas concentrations while LCDs show photographs from cameras. Extracted numeral data are received to a laptop computer through Ethernet modem. And then, the numerical data with gas concentrations and the measured initial response speeds are recorded and graphed. Our remote testing system will be diversely applied on gas detector’s test and will be certificated in domestic and international countries.

Keywords: gas leak detector, inspection instrument, extracting numerals, concentration

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Authors: Ling Dai

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Surface-sensing devices such as atomic force microscope have been widely used to characterize the surface structure and properties of nanoscale polymer films. However, using molecular dynamics simulations, we show that there is intrinsic and unavoidable inelastic deformation at polymer surfaces induced by the sensing tip. For linear chain polymers like perfluoropolyether, such tip-induced deformation derives from the differences in the atomic interactions which are atomic specie-based Van der Waals interactions, and resulting in atomic shuffling and causing inelastic alternation in both molecular structures and mechanical properties at the regions of the polymer surface. For those aromatic chain polymers like epoxy, the intrinsic deformation is depicted as the intra-chain rotation of aromatic rings and kinking of linear atomic connections. The present work highlights the need to reinterpret the data obtained from surface-sensing tests by considering this intrinsic inelastic deformation occurring at polymer surfaces.

Keywords: polymer, surface, nano, molecular dynamics

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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Cognitive Radio is a turning out technology that empowers viable usage of the spectrum. Energy Detector-based Sensing is the most broadly utilized spectrum sensing strategy. Besides, it is a lot of generic as receivers does not like any information on the primary user's signals, channel data, of even the sort of modulation. This paper puts forth the execution of energy detection sensing for AM (Amplitude Modulated) signal at 710 KHz, FM (Frequency Modulated) signal at 103.45 MHz (local station frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz. The OFDM/OFDMA based WiMAX physical layer with convolutional channel coding is actualized utilizing USRP N210 (Universal Software Radio Peripheral) and GNU Radio based Software Defined Radio (SDR). Test outcomes demonstrated the BER (Bit Error Rate) augmentation with channel noise and BER execution is dissected for different Eb/N0 (the energy per bit to noise power spectral density ratio) values.

Keywords: BER, Cognitive Radio, GNU Radio, OFDM, SDR, WiMAX

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Authors: Chad A. Roberts

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In the digitally connected world, global virtual teams are increasingly becoming the norm at large, multinational companies. Marketing managers see the positives of virtual teams. They also see the negatives. Employees who work from home may feel isolated, unorganized, and distracted by homelife. These complexities create a phenomenon that leaves virtual team members feeling burnout, a significant issue for marketing leaders and their team members. This paper examines remote worker burnout in global virtual marketing team settings. It provides an overview of the benefits and downsides to remote working marketing teams. The paper presents the literature on remote work stress and burnout, discusses ways marketing leaders can help prevent virtual employee burnout and suggests future research studies.

Keywords: burnout, COVID-19 pandemic, leadership, marketing, remote work, virtual team

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Authors: Arafan Traore, Teiji Watanabe

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Conakry, the capital city of the Republic of Guinea, has experienced a rapid urban expansion and population increased in the last two decades, which has resulted in remarkable local weather and climate change, raise energy demand and pollution and treating social, economic and environmental development. In this study, the spatiotemporal variation of the land surface temperature (LST) is retrieved to characterize the effect of urban growth on the thermal environment and quantify its relationship with biophysical indices, a normalized difference vegetation index (NDVI) and a normalized difference built up Index (NDBI). Landsat data TM and OLI/TIRS acquired respectively in 1986, 2000 and 2016 were used for LST retrieval and Land use/cover change analysis. A quantitative analysis based on the integration of a remote sensing and a geography information system (GIS) has revealed an important increased in the LST pattern in the average from 25.21°C in 1986 to 27.06°C in 2000 and 29.34°C in 2016, which was quite eminent with an average gain in surface temperature of 4.13°C over 30 years study period. Additionally, an analysis using a Pearson correlation (r) between (LST) and the biophysical indices, normalized difference vegetation index (NDVI) and a normalized difference built-up Index (NDBI) has revealed a negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. Which implies that an increase in the NDVI value can reduce the LST intensity; conversely increase in NDBI value may strengthen LST intensity in the study area. Although Landsat data were found efficient in assessing the thermal environment in Conakry, however, the method needs to be refined with in situ measurements of LST in the future studies. The results of this study may assist urban planners, scientists and policies makers concerned about climate variability to make decisions that will enhance sustainable environmental practices in Conakry.

Keywords: Conakry, land surface temperature, urban heat island, geography information system, remote sensing, land use/cover change

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Authors: S. S. Chong, A. R. Abdul Aziz, S. W. Harun, H. Arof

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A miniaturized non-adiabatic silica microfiber is proposed for biological oxygen demand (BOD) ratio chemical oxygen demand (COD) sensing for the first time. BOD and COD are two main parameters to justify quality of wastewater. A ratio, BOD:COD can usually be established between the two analytical methods once COD and BOD value has been gathered. This ratio plays a vital role to determine appropriate strategy in wastewater treatment. A non-adiabatic microfiber sensor was formed by tapering the SMF to generate evanescent field where sensitive to perturbation of sensing medium. Because difference ratio BOD and COD contain in solution, this may induced changes of effective refractive index between microfiber and sensing medium. Attenuation wavelength shift to right with 0.5 nm and 3.5 nm while BOD:COD equal to 0.09 and 0.18 respectively. Significance difference wavelength shift may relate with the biodegradability of analyte. This proposed sensor is compact, reliable and feasible to determine the BOD:COD. Further research and investigation should be proceeded to enhance sensitivity and precision of the sensor for several of wastewater online monitoring.

Keywords: non-adiabatic fiber sensor, environmental sensing, biodegradability, evanescent field

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Authors: Krishan P. Sharma, T. P. Sharma

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Wireless sensor nodes are resource constrained battery powered devices usually deployed in hostile and ill-disposed areas to cooperatively monitor physical or environmental conditions. Due to their limited power supply, the major challenge for researchers is to utilize their battery power for enhancing the lifetime of whole network. Communication and sensing are two major sources of energy consumption in sensor networks. In this paper, we propose a deployment strategy for enhancing the average lifetime of a sensor network by effectively utilizing communication and sensing energy to provide full coverage. The proposed scheme is based on the fact that due to heavy relaying load, sensor nodes near to the sink drain energy at much faster rate than other nodes in the network and consequently die much earlier. To cover this imbalance, proposed scheme finds optimal communication and sensing ranges according to effective load at each node and uses a non-uniform deployment strategy where there is a comparatively high density of nodes near to the sink. Probable relaying load factor at particular node is calculated and accordingly optimal communication distance and sensing range for each sensor node is adjusted. Thus, sensor nodes are placed at locations that optimize energy during network operation. Formal mathematical analysis for calculating optimized locations is reported in present work.

Keywords: load factor, network lifetime, non-uniform deployment, sensing range

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Dauda A. Oladosu, Kamorudeen A Olaiya, Abdurahman Bello

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The deployment of smart meters by utility providers to gather fine grained spatiotemporal consumption data has grossly influenced the consumers’ emotion and behavior towards energy utilization. The quest for reduction in power consumption is now a subject of concern and one the methods adopted by the consumers to achieve this is Non-intrusive Load (appliance) Monitoring. Hence, this work presents performance Analysis of Domotics System as a tool for load monitoring when integrated with Consumer Control Unit of residential building. The system was developed with basic elements which enhance remote sensing, DTMF (Dual Tone Multi-frequency) recognition and cryptic messaging when specific task was performed. To demonstrate its applicability and suitability, this prototype was used consistently for six months at different load demands and the utilities consumed were documented. The results obtained shows good response when phone dialed, and the packet delivery of feedback SMS was quite satisfactory, making the implemented system to be of good quality with affordable cost and performs the desired functions. Besides, comparative analysis showed notable reduction in energy consumption and invariably lessened electrical bill of the consumer.

Keywords: automation, domotics, energy, load, remote, schedule

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Jyoti Jaiswal, Satyendra Mourya, Gaurav Malik, Ramesh Chandra

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In the present work, we have fabricated and studied a resistive H₂ gas sensor based on Pd-Pt decorated room temperature sputter grown nanocrystalline porous titanium dioxide (p-TiO₂) thin film on porous silicon (p-Si) substrate for fast H₂ detection. The gas sensing performance of Pd-Pt/p-TiO₂/p-Si sensing electrode towards H₂ gas under low (10-500 ppm) detection limit and operating temperature regime (25-200 °C) was discussed. The sensor is highly sensitive even at room temperature, with response (Ra/Rg) reaching ~102 for 500 ppm H₂ in dry air and its capability of sensing H₂ concentrations as low as ~10 ppm was demonstrated. At elevated temperature of 200 ℃, the response reached more than ~103 for 500 ppm H₂. Overall the fabricated resistive gas sensor exhibited high selectivity, good sensing response, and fast response/recovery time with good stability towards H₂.

Keywords: sputtering, porous silicon (p-Si), TiO₂ thin film, hydrogen gas sensor

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Ahmad Umar, Sheikh Akbar, Ahmed A. Ibrahim, Mohsen A. Alhamami

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The development of advanced materials for energy storage and gas sensing applications has gained significant attention in recent years. In this study, we synthesized and characterized PANI@MnO2@rGO ternary nanocomposites (NCs) to explore their potential in supercapacitors and gas sensing devices. The ternary NCs were synthesized through a multi-step process involving the hydrothermal synthesis of MnO2 nanoparticles, preparation of PANI@rGO composites and the assembly to the ternary PANI@MnO2@rGO ternary NCs. The structural, morphological, and compositional characteristics of the materials were thoroughly analyzed using techniques such as XRD, FESEM, TEM, FTIR, and Raman spectroscopy. In the realm of gas sensing, the ternary NCs exhibited excellent performance as NH3 gas sensors. The optimized operating temperature of 100 °C yielded a peak response of 15.56 towards 50 ppm NH3. The nanocomposites demonstrated fast response and recovery times of 6 s and 10 s, respectively, and displayed remarkable selectivity for NH3 gas over other tested gases. For supercapacitor applications, the electrochemical performance of the ternary NCs was evaluated using cyclic voltammetry and galvanostatic charge-discharge techniques. The composites exhibited pseudocapacitive behavior, with the capacitance reaching up to 185 F/g at 1 A/g and excellent capacitance retention of approximately 88.54% over 4000 charge-discharge cycles. The unique combination of rGO, PANI, and MnO2 nanoparticles in these ternary NCs offer synergistic advantages, showcasing their potential to address challenges in energy storage and gas sensing technologies.

Keywords: paniI@mnO2@rGO ternary NCs, synergistic effects, supercapacitors, gas sensing, energy storage

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Authors: Purba Biswas, Priyanka Dey

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Worldwide rapid urbanisation has accelerated city expansion in both developed and developing nations. This unprecedented urbanisation trend due to the increasing population and economic growth has caused challenges for the decision-makers in city planning and urban management. Metropolitan cities, class-I towns, and major urban centres undergo a continuous process of evolution due to interaction between socio-cultural and economic attributes. This constant evolution leads to urban expansion in all directions. Understanding the patterns and dynamics of urban built-up growth is crucial for policymakers, urban planners, and researchers, as it aids in resource management, decision-making, and the development of sustainable strategies to address the complexities associated with rapid urbanisation. Identifying spatio-temporal patterns of urban growth has emerged as a crucial challenge in monitoring and assessing present and future trends in urban development. Analysing urban growth patterns and tracking changes in land use is an important aspect of urban studies. This study analyses spatio-temporal urban transformations and land-use and land cover changes using remote sensing and GIS techniques. Built-up growth analysis has been done for the city of Surat as a case example, using the GIS tools of NDBI and GIS models of the Built-up Urban Density Index and Shannon Entropy Index to identify trends and the geographical direction of transformation from 2005 to 2020. Surat is one of the fastest-growing urban centres in both the state and the nation, ranking as the 4th fastest-growing city globally. This study analyses the dynamics of urban built-up area transformations both zone-wise and geographical direction-wise, in which their trend, rate, and magnitude were calculated for the period of 15 years. This study also highlights the need for analysing and monitoring the urban growth pattern of class-I cities in India using spatio-temporal and quantitative techniques like GIS for improved urban management.

Keywords: urban expansion, built-up, geographic information system, remote sensing, Shannon’s entropy

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