Search results for: seed sensor

Authors: John E. Foley, Martin Miele, Raul Fonda, Jon Jacobson

Abstract:

This paper presents development and implementation of new and innovative data collection and analysis methodologies based on deployment of total field magnetometer arrays. Our research has focused on the development of a vertically-integrated suite of platforms all utilizing common data acquisition, data processing and analysis tools. These survey platforms include low-altitude helicopters and ground-based vehicles, including robots, for terrestrial mapping applications. For marine settings the sensor arrays are deployed from either a hydrodynamic bottom-following wing towed from a surface vessel or from a towed floating platform for shallow-water settings. Additionally, sensor arrays are deployed from tethered remotely operated vehicles (ROVs) for underwater settings where high maneuverability is required. While the primary application of these systems is the detection and mapping of unexploded ordnance (UXO), these system are also used for various infrastructure mapping and geologic investigations. For each application, success is driven by the integration of magnetometer arrays, accurate geo-positioning, system noise mitigation, and stable deployment of the system in appropriate proximity of expected targets or features. Each of the systems collects geo-registered data compatible with a web-enabled data management system providing immediate access of data and meta-data for remote processing, analysis and delivery of results. This approach allows highly sophisticated magnetic processing methods, including classification based on dipole modeling and remanent magnetization, to be efficiently applied to many projects. This paper also briefly describes the initial development of magnetometer-based detection systems deployed from low-altitude helicopter platforms and the subsequent successful transition of this technology to the marine environment. Additionally, we present examples from a range of terrestrial and marine settings as well as ongoing research efforts related to sensor miniaturization for unmanned aerial vehicle (UAV) magnetic field mapping applications.

Keywords: dipole modeling, magnetometer mapping systems, sub-surface infrastructure mapping, unexploded ordnance detection

Procedia PDF Downloads 461

Authors: M. Mirmohammadi, S. Taghdisi, F. Anali

Abstract:

Gossypol is a yellow anti-nutritional compound found in the cotton plant. This substance exists in the cottonseed and other parts of the cotton plant, such as bark, leaves, and stems. Chemically, gossypol is a very active polyphenolic aldehyde compound, and due to this polyphenolic structure, it has antioxidant and therapeutic properties. On the other hand, this compound, especially in free form, has many toxic effects, that its excessive consumption can be very dangerous for humans and animals. In this study, gossypol was extracted as a derivative compound of gossypol acetic acid from cottonseed using the n-hexane solvent with an efficiency of 0.84 ± 0.04, which compared to the Gossypol extracted from cottonseed oil with the same method (cold press) showed a significant difference with its efficiency of 1.14 ± 0.06. Therefore, it can be suggested to use cottonseed oil to extract this valuable compound. In the other part of this research, cottonseed extracts and cotton bolls extracts were obtained by two methods of soaking and Soxhlet with hydroalcoholic solvent taken with a ratio of (25:75), then by using extracts and corn starch powder, four herbal medicine code was created and after receiving the code of ethics (IR.SSU.REC.1398.136) the therapeutic effect of each one on the Cutaneous leishmaniasis resistant to drugs (caused by the leishmaniasis parasite) was investigated in real patients and its results was compared with the common drug glucantime (local ampoule) (n = 36). Statistical studies showed that the use of herbal medicines prepared with cottonseed extract and cotton bolls extract has a significant positive effect on the treatment of the disease’s wounds (p-value > 0.05) compared to the control group (only ethanol). Also, by comparing the average diameter of the wounds after a two-month treatment period, no significant difference was found between the use of ointment containing extracts and local glucantime ampoules (p-value < 0.05). Bolls extract extracted with the Soxhlet method showed the best therapeutic effects, although there was no significant difference between them (p-value < 0.05). Therefore, there is acceptable reliability to recommend this medicine for the treatment of Cutaneous leishmaniasis resistant to drugs without the side effects of the chemical drug glucantime and the pain of injecting the ampoule.

Keywords: cottonseed oil, gossypol, cotton boll, cutaneous leishmaniasis

Procedia PDF Downloads 90

Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

Abstract:

Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

Procedia PDF Downloads 266

Authors: N. Chougui, R. Larbat

Abstract:

The first economic importance of Opuntia ficus-indica relies on the production of edible fruits. This food transformation generates a large amount of by-products (seeds and peels) in addition to cladodes produced by the plant. Several studies showed the richness of these products with bioactive substances like phenolics that have potential applications. Indeed, phenolics have been associated with protection against oxidation and several biological activities responsible of different pathologies. Consequently, there has been a growing interest in identifying natural antioxidants from plants. This study falls within the framework of the industrial exploitation of by-products of the plant. The study aims to investigate the metabolic profile of three by-products (cladodes, peel seeds) regarding total phenolic content by liquid chromatography coupled to mass spectrometry approach (LC-MSn). The byproducts were first washed, crushed and stored at negative temperature. The total phenolic compounds were then extracted by aqueous-ethanolic solvent in order to be quantified and characterized by LC-MS. According to the results obtained, the peel extract was the richest in phenolic compounds (1512.58 mg GAE/100 g DM) followed by the cladode extract (629.23 GAE/100 g DM) and finally by the seed extract (88.82 GAE/100 g DM) which is mainly used for its oil. The LC-MS analysis revealed diversity in phenolics in the three extracts and allowed the identification of hydroxybenzoic acids, hydroxycinnamic acids and flavonoids. The highest complexity was observed in the seed phenolic composition; more than twenty compounds were detected that belong to acids esters among which three feruloyl sucrose isomers. Sixteen compounds belonging to hydroxybenzoic acids, hydroxycinnamic acids and flavonoids were identified in the peel extract, whereas, only nine compounds were found in the cladode extract. It is interesting to highlight that the phenolic composition of the cladode extract was closer to that of the peel exact. However, from a quantitative viewpoint, the peel extract presented the highest amounts. Piscidic and eucomic acids were the two most concentrated molecules, corresponding to 271.3 and 121.6 mg GAE/ 100g DM respectively. The identified compounds were known to have high antioxidant and antiradical potential with the ability to inhibit lipid peroxidation and to exhibit a wide range of biological and therapeutic properties. The findings highlight the importance of using the Opuntia ficus-indica by-products.

Keywords: characterization, LC-MSn analysis, Opuntia ficus-indica, phenolics

Procedia PDF Downloads 223

Authors: A. Rinaldi, A. Proietti, C. Aquarelli, F. Marra, A. Tamburrano, M. Ciminello, M. S. Sarto

Abstract:

A new type of high sensitive piezoresistive sensors based on graphene was developed within the SARISTU project for application on Structural Health Monitoring (SHM). The new sensor consists of a graphene-based film, obtained through the spray deposition of a colloidal suspension of Multi-Layer Graphene (MLGs) nano platelets over a substrate. MLGs are produced by liquid exfoliation of thermally expanded Graphite Intercalation Compound. An array of 8 sensors is produced by spray deposition over an aeronautical CFRC plate of dimensions 550 mm (length) × 550 mm (width) × 3 mm (thickness). Electromechanical tests were performed in order to assess the sensitivity of the new piezoresistive sensors, which are characterized by an isotropic response. In the quasi-static characterizations, the CFRC plate was clamped on one side and loaded on the opposite one. The local strain map of the plate was then obtained from displacement measurements and numerical analysis. The dynamic tests were performed lying the plate over an anti-vibration table and actuating a piezoelectric element located in the middle of the sensing array. The obtained experimental results demonstrated that the sensors possess a good repeatability and a high constant gauge factor (~200) in the applied strain range 0.001%-0.02%. Moreover, they can follow dynamics up to 400 kHz and for this reason they are good candidates for Lamb-wave analysis.

Keywords: graphene, strain sensor, spray deposition, lamb-wave analysis

Procedia PDF Downloads 426

Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

Abstract:

Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

Procedia PDF Downloads 335

Authors: Kirandeep Kaur, Rohit Mehra, Pargin Bangotra

Abstract:

Radon and thoron are pervasive radioactive gases and so are their progenies. The progenies of radon and thoron are present in the indoor atmosphere as attached/unattached fractions. In the present work, seasonal variation of concentration of attached and total (attached + unattached) nanosized decay products of indoor radon and thoron has been studied in the dwellings of Bathinda District of Punjab using Deposition based progeny sensors over long integrated times, which are independent of air turbulence. The preliminary results of these measurements are reported particularly regarding DTPS (Direct Thoron Progeny Sensor) and DRPS (Direct Radon Progeny Sensor) for the first time in Bathinda. It has been observed that there is a strong linear relationship in total EERC (Equilibrium Equivalent Radon Concentration) and EETC (Equilibrium Equivalent Thoron Concentration) in rainy season (R2 = 0.83). Further a strong linear relation between total indoor radon concentration and attached fraction has also been observed for the same rainy season (R2= 0.91). The concentration of attached progeny of radon (EERCatt) is 76.3 % of the total Equilibrium Equivalent Radon Concentration (EERC).

Keywords: radon, thoron, progeny, DTPS/DRPS, EERC, EETC, seasonal variation

Procedia PDF Downloads 412

Authors: Vishal Kumar, Soumitra Satapathi

Abstract:

Föster Resonance Energy Transfer (FRET) is a powerful technique used to probe close-range molecular interactions. Physically, the FRET phenomenon manifests as a dipole–dipole interaction between closely juxtaposed fluorescent molecules (10–100 Å). Our effort is to employ this FRET technique to make a prototype device for highly sensitive detection of environment pollutant. Among the most common environmental pollutants, nitroaromatic compounds (NACs) are of particular interest because of their durability and toxicity. That’s why, sensitive and selective detection of small amounts of nitroaromatic explosives, in particular, 2,4,6-trinitrophenol (TNP), 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT) has been a critical challenge due to the increasing threat of explosive-based terrorism and the need of environmental monitoring of drinking and waste water. In addition, the excessive utilization of TNP in several other areas such as burn ointment, pesticides, glass and the leather industry resulted in environmental accumulation, and is eventually contaminating the soil and aquatic systems. To the date, high number of elegant methods, including fluorimetry, gas chromatography, mass, ion-mobility and Raman spectrometry have been successfully applied for explosive detection. Among these efforts, fluorescence-quenching methods based on the mechanism of FRET show good assembly flexibility, high selectivity and sensitivity. Here, we report a FRET-based sensor system for the highly selective detection of NACs, such as TNP, DNT and TNT. The sensor system is composed of a copolymer Poly [(N,N-dimethylacrylamide)-co-(Boc-Trp-EMA)] (RP) bearing tryptophan derivative in the side chain as donor and dansyl tagged copolymer P(MMA-co-Dansyl-Ala-HEMA) (DCP) as an acceptor. Initially, the inherent fluorescence of RP copolymer is quenched by non-radiative energy transfer to DCP which only happens once the two molecules are within Förster critical distance (R0). The excellent spectral overlap (Jλ= 6.08×10¹⁴ nm⁴M⁻¹cm⁻¹) between donors’ (RP) emission profile and acceptors’ (DCP) absorption profile makes them an exciting and efficient FRET pair i.e. further confirmed by the high rate of energy transfer from RP to DCP i.e. 0.87 ns⁻¹ and lifetime measurement by time correlated single photon counting (TCSPC) to validate the 64% FRET efficiency. This FRET pair exhibited a specific fluorescence response to NACs such as DNT, TNT and TNP with 5.4, 2.3 and 0.4 µM LODs, respectively. The detection of NACs occurs with high sensitivity by photoluminescence quenching of FRET signal induced by photo-induced electron transfer (PET) from electron-rich FRET pair to electron-deficient NAC molecules. The estimated stern-volmer constant (KSV) values for DNT, TNT and TNP are 6.9 × 10³, 7.0 × 10³ and 1.6 × 104 M⁻¹, respectively. The mechanistic details of molecular interactions are established by time-resolved fluorescence, steady-state fluorescence and absorption spectroscopy confirmed that the sensing process is of mixed type, i.e. both dynamic and static quenching as lifetime of FRET system (0.73 ns) is reduced to 0.55, 0.57 and 0.61 ns DNT, TNT and TNP, respectively. In summary, the simplicity and sensitivity of this novel FRET sensor opens up the possibility of designing optical sensor of various NACs in one single platform for developing multimodal sensor for environmental monitoring and future field based study.

Keywords: FRET, nitroaromatic, stern-Volmer constant, tryptophan and dansyl tagged copolymer

Procedia PDF Downloads 127

Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

Procedia PDF Downloads 111

Authors: Anusha Jain, Santosh Vishwanathan, Praveen K. Gupta, Shwetha S., Kavitha S. N.

Abstract:

Agriculture has a major impact on the Indian economy, with the highest employment ratio than any sector of the country. Currently, most of the traditional agricultural practices and farming methods are manual, which results in farmers not realizing their maximum productivity often due to increasing in labour cost, inefficient use of water sources leading to wastage of water, inadequate soil moisture content, subsequently leading to food insecurity of the country. This research paper aims to solve this problem by developing a full-fledged web application-based platform that has the capacity to associate itself with a Microcontroller-based Automated Irrigation System which schedules the irrigation of crops based on real-time soil moisture content employing soil moisture sensors centric to the crop’s requirements using WSN (Wireless Sensor Networks) and M2M (Machine To Machine Communication) concepts, thus optimizing the use of the available limited water resource, thereby maximizing the crop yield. This robust automated irrigation system provides end-to-end automation of Irrigation of crops at any circumstances such as droughts, irregular rainfall patterns, extreme weather conditions, etc. This platform will also be capable of achieving a nationwide united farming community and ensuring the welfare of farmers. This platform is designed to equip farmers with prerequisite knowledge on tech and the latest farming practices in general. In order to achieve this, the MailChimp mailing service is used through which interested farmers/individuals' email id will be recorded and curated articles on innovations in the world of agriculture will be provided to the farmers via e-mail. In this proposed system, service is enabled on the platform where nearby crop vendors will be able to enter their pickup locations, accepted prices and other relevant information. This will enable farmers to choose their vendors wisely. Along with this, we have created a blogging service that will enable farmers and agricultural enthusiasts to share experiences, helpful knowledge, hardships, etc., with the entire farming community. These are some of the many features that the platform has to offer.

Keywords: WSN (wireless sensor networks), M2M (M/C to M/C communication), automation, irrigation system, sustainability, SAAS (software as a service), soil moisture sensor

Procedia PDF Downloads 122

Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

Abstract:

The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

Procedia PDF Downloads 377

Authors: Gopalasingam Daisan

Abstract:

Landmines and ammunitions have been creating a significant threat to the people and animals, after the war, the landmines remain in the land and it plays a vital role in civilian’s security. Especially the Children are at the highest risk because they are curious. After all, an unexploded bomb can look like a tempting toy to an inquisitive child. The initial step of designing the UAS (Unmanned Aircraft Systems) for landmine detection is to choose an appropriate and effective sensor to locate the landmines and other unexploded ammunitions. The sensor weight and other components related to the sensor supporting device’s weight are taken as a payload weight. The mission requirement is to find the landmines in a particular area by making a proper path that will cover all the vicinity in the desired area. The weight estimation of the UAV (Unmanned Aerial Vehicle) can be estimated by various techniques discovered previously with good accuracy at the first phase of the design. The next crucial part of the design is to calculate the power requirement and the wing loading calculations. The matching plot techniques are used to determine the thrust-to-weight ratio, and this technique makes this process not only easiest but also precisely. The wing loading can be calculated easily from the stall equation. After these calculations, the wing area is determined from the wing loading equation and the required power is calculated from the thrust to weight ratio calculations. According to the power requirement, an appropriate engine can be selected from the available engine from the market. And the wing geometric parameter is chosen based on the conceptual sketch. The important steps in the wing design to choose proper aerofoil and which will ensure to create sufficient lift coefficient to satisfy the requirements. The next component is the tail; the tail area and other related parameters can be estimated or calculated to counteract the effect of the wing pitching moment. As the vertical tail design depends on many parameters, the initial sizing only can be done in this phase. The fuselage is another major component, which is selected based on the slenderness ratio, and also the shape is determined on the sensor size to fit it under the fuselage. The landing gear is one of the important components which is selected based on the controllability and stability requirements. The minimum and maximum wheel track and wheelbase can be determined based on the crosswind and overturn angle requirements. The minor components of the landing gear design and estimation are not the focus of this project. Another important task is to calculate the weight of the major components and it is going to be estimated using empirical relations and also the mass is added to each such component. The CG and moment of inertia are also determined to each component separately. The sensitivity of the weight calculation is taken into consideration to avoid extra material requirements and also reduce the cost of the design. Finally, the aircraft performance is calculated, especially the V-n (velocity and load factor) diagram for different flight conditions such as not disturbed and with gust velocity.

Keywords: landmine, UAS, matching plot, optimization

Procedia PDF Downloads 168

Authors: Gabriel Vusanimuzi Nkomo

Abstract:

The majority of soils in communal areas of Zimbabwe are sandy and inherently infertile and sustainable cultivation is not feasible without addition of plant nutrients. Most farmers find it difficult to raise the capital required for investments in mineral fertilizer and find it cheaper to use low nutrition animal manure. An experiment was conducted to determine the effects of nitrokara biofertiliser on early growth, development and maize yield while also comparing nitrokara biofertiliser on availability of nitrogen and phosphorous in soil. The experiment was conducted at Africa University Farm. The experiment had six treatments (nitrokara +300kg/ha Compound D, nitrokara+ 300kg/ha Compound D(7N;14P;7K) + 75kg/ha Ammonium Nitrate(AN), nitrokara +300kg/ha Compound D +150kg AN, nitrokara +300kg/ha Compound D +225kg/ha AN, nitrokara +300kg/ha Compound D + 300 kg/ha AN and 0 nitrokara+300kg/ha Compound D +0 AN). Early maturing SC 403 maize (Zea mays) was inoculated with nitrokara and a compound mineral fertilizer at 300 kg/ha at planting while ammonium nitrate was applied at 45 days after planting. There were no significant differences (P > 0.05) on emergence % from 5days up to 10 days after planting using maize seed inoculated with nitrokara. Emergence percentage varied with the number of days. At 5 days the emergence % was 62% to a high of 97 % at 10 days after emergence among treatments. There were no significant differences (P > 0.05) on plant biomass on treatments 1 to 6 at 4 weeks after planting as well as at 8 weeks after planting. There were no significant differences among the treatments on the availability of nitrogen after 6 weeks (P > 0.05). However at 8 and 10 weeks after planting there were significant differences among treatments on nitrogen availability (P < 0.05). There were no significant differences among the treatments at week 6 after planting on soil pH (p > 0.05). However there were significant differences among treatments pH at weeks 9 and 12 (p < 0.05). There were significant differences among treatments on phosphorous availability at 6, 8 and 10 weeks after planting (p < 0.05). There were no significant differences among treatments on stem diameter at 3 and 6 weeks after planting (p > 0.05).However at 9 and 12 weeks after planting there were significant differences among treatments on stem diameter (p < 0.05).There were no significant differences among treatments on plant height from week 3 up to week 6 on plant height (P > 0.05).However there were significant differences among treatments at week 9 and 12 (p < 0.05). There were significant differences among treatments on days to early, 50% and 100% anthesis (P < 0.05). There were significant differences during early, 50% and 100% days to silking among the treatments (P < 0.05).Also there were significant differences during early, 50% and 100% days to silking among the treatments (P < 0.05).The study revealed that inoculation of nitrokara biofertiliser at planting with subsequent addition of ammonium nitrate has a positive effect on maize crop development and yield.

Keywords: nitrokara, biofertiliser, symbiotic, plant biomass, inoculated

Procedia PDF Downloads 544

Authors: Raz Flieshman, Adam Michael Altenbuchner, Jörg Krüger

Abstract:

This paper presents an approach to optimizing the Fused Deposition Modeling (FDM) 3D printing process through a preprocess calibration routine of printing parameters. The core of this method involves the use of a low-cost thermal sensor capable of measuring tempera-tures within the range of -20 to 500 degrees Celsius for detailed process observation. The calibration process is conducted by printing a predetermined path while varying the process parameters through machine instructions (g-code). This enables the extraction of critical thermal, dimensional, and surface properties along the printed path. The calibration routine utilizes computer vision models to extract features and metrics from the thermal images, in-cluding temperature distribution, layer adhesion quality, surface roughness, and dimension-al accuracy and consistency. These extracted properties are then analyzed to optimize the process parameters to achieve the desired qualities of the printed material. A significant benefit of this calibration method is its potential to create printing parameter profiles for new polymer and composite materials, thereby enhancing the versatility and application range of FDM 3D printing. The proposed method demonstrates significant potential in enhancing the precision and reliability of FDM 3D printing, making it a valuable contribution to the field of additive manufacturing.

Keywords: FDM 3D printing, preprocess calibration, thermal sensor, process optimization, additive manufacturing, computer vision, material profiles

Procedia PDF Downloads 28

Authors: Hamed Ahari, Behrouz Akbari Adreghani, Vadood Razavilar, Amirali Anvar, Sima Moradi, Hourieh Shalchi

Abstract:

Considering the ever increasing population and industrialization of the developmental trend of humankind's life, we are no longer able to detect the toxins produced in food products using the traditional techniques. This is due to the fact that the isolation time for food products is not cost-effective and even in most of the cases, the precision in the practical techniques like the bacterial cultivation and other techniques suffer from operator errors or the errors of the mixtures used. Hence with the advent of nanotechnology, the design of selective and smart sensors is one of the greatest industrial revelations of the quality control of food products that in few minutes time, and with a very high precision can identify the volume and toxicity of the bacteria. Methods and Materials: In this technique, based on the bacterial antibody connection to nanoparticle, a sensor was used. In this part of the research, as the basis for absorption for the recognition of bacterial toxin, medium sized silica nanoparticles of 10 nanometer in form of solid powder were utilized with Notrino brand. Then the suspension produced from agent-linked nanosilica which was connected to bacterial antibody was positioned near the samples of distilled water, which were contaminated with Staphylococcus aureus bacterial toxin with the density of 10-3, so that in case any toxin exists in the sample, a connection between toxin antigen and antibody would be formed. Finally, the light absorption related to the connection of antigen to the particle attached antibody was measured using spectrophotometry. The gene of 23S rRNA that is conserved in all Staphylococcus spp., also used as control. The accuracy of the test was monitored by using serial dilution (l0-6) of overnight cell culture of Staphylococcus spp., bacteria (OD600: 0.02 = 107 cell). It showed that the sensitivity of PCR is 10 bacteria per ml of cells within few hours. Result: The results indicate that the sensor detects up to 10-4 density. Additionally, the sensitivity of the sensors was examined after 60 days, the sensor by the 56 days had confirmatory results and started to decrease after those time periods. Conclusions: Comparing practical nano biosensory to conventional methods like that culture and biotechnology methods(such as polymerase chain reaction) is accuracy, sensitiveness and being unique. In the other way, they reduce the time from the hours to the 30 minutes.

Keywords: exotoxin, nanobiosensor, recognition, Staphylococcus aureus

Procedia PDF Downloads 380

Authors: Hui Zhang, Sherif Beskhyroun

Abstract:

In this study, a highly flexible LoRa-Based wireless sensing system was used to assess the strain state performance of building structures. The system was developed to address the local damage limitation of structural health monitoring (SHM) systems. The system is part of an intelligent SHM system designed to monitor, collect and transmit strain changes in key structural components. The main purpose of the wireless sensor system is to reduce the development and installation costs, and reduce the power consumption of the system, so as to achieve long-time monitoring. The highly stretchable flexible strain gauge is mounted on the surface of the structure and is waterproof, heat resistant, and low temperature resistant, greatly reducing the installation and maintenance costs of the sensor. The system was also developed with the aim of using LoRa wireless communication technology to achieve both low power consumption and long-distance transmission, therefore solving the problem of large-scale deployment of sensors to cover more areas in large structures. In the long-term monitoring of the building structure, the system shows very high performance, very low actual power consumption, and wireless transmission stability. The results show that the developed system has a high resolution, sensitivity, and high possibility of long-term monitoring.

Keywords: LoRa, SHM system, strain measurement, civil structures, flexible sensing system

Procedia PDF Downloads 99

Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

Abstract:

Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

Procedia PDF Downloads 139

Authors: Olakanmi Oladayo Olufemi, Bamifewe Olusegun James, Badmus Yaya Opeyemi, Adegoke Kayode

Abstract:

The wireless sensor network (WSN) has made a significant contribution to the emergence of various intelligent services or cloud-based applications. Most of the time, these data are stored on a cloud platform for efficient management and sharing among different services or users. However, the sensitivity of the data makes them prone to various confidentiality and performance-related attacks during and after harvesting. Various security schemes have been developed to ensure the integrity and confidentiality of the WSNs' data. However, their specificity towards particular attacks and the resource constraint and heterogeneity of WSNs make most of these schemes imperfect. In this paper, we propose a secure variance-aware routing and authentication scheme with two-tier verification to collect, share, and manage WSN data. The scheme is capable of classifying WSN into different subnets, detecting any attempt of wormhole and black hole attack during harvesting, and enforcing access control on the harvested data stored in the cloud. The results of the analysis showed that the proposed scheme has more security functionalities than other related schemes, solves most of the WSNs and cloud security issues, prevents wormhole and black hole attacks, identifies the attackers during data harvesting, and enforces access control on the harvested data stored in the cloud at low computational, storage, and communication overheads.

Keywords: data block, heterogeneous IoT network, data harvesting, wormhole attack, blackhole attack access control

Procedia PDF Downloads 68

Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

Abstract:

Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

Procedia PDF Downloads 297

Authors: B. Padmaja, V. V. Rama Prasad, K. V. N. Sunitha, E. Krishna Rao Patro

Abstract:

Stress is a psychological condition that reduces the quality of sleep and affects every facet of life. Constant exposure to stress is detrimental not only for mind but also body. Nevertheless, to cope with stress, one should first identify it. This paper provides an effective method for the cognitive stress level detection by using data provided from a physical activity tracker device Fitbit. This device gathers people’s daily activities of food, weight, sleep, heart rate, and physical activities. In this paper, four major stressors like physical activities, sleep patterns, working hours and change in heart rate are used to assess the stress levels of individuals. The main motive of this system is to use machine learning approach in stress detection with the help of Smartphone sensor technology. Individually, the effect of each stressor is evaluated using logistic regression and then combined model is built and assessed using variants of ordinal logistic regression models like logit, probit and complementary log-log. Then the quality of each model is evaluated using Akaike Information Criterion (AIC) and probit is assessed as the more suitable model for our dataset. This system is experimented and evaluated in a real time environment by taking data from adults working in IT and other sectors in India. The novelty of this work lies in the fact that stress detection system should be less invasive as possible for the users.

Keywords: physical activity tracker, sleep pattern, working hours, heart rate, smartphone sensor

Procedia PDF Downloads 249

Authors: M’lik Randa, Lakhdari Wassima, Dahliz Abderrahmène, Soud Adila, Hammi Hamida

Abstract:

In cereal culture, as in the most the vegetal productions the seeds play an important role in the development of the future plant. The healthy seeds are very important for the quality and quantity production. This study on a media (P.D.A) shows that an important mycoflora exists in the crops. Among the identified fungical, we notice the presence of Helminthosporium sp, Alternaria sp, Botrytis and Macrosporium. The use of the illness causing facies, especially for Helminthosporium, Alternaria and Botrytis emphasizes the relation between the seminicole inoculums and the appearance of symptoms on young plants noted by authors.

Keywords: seeds, barley, wheat, fungical flora

Procedia PDF Downloads 411

Authors: Ayman El Sabagh, SobhySorour, AbdElhamid Omar, Adel Ragab, Mohammad Sohidul Islam, Celaleddin Barutçular, Akihiro Ueda, Hirofumi Saneoka

Abstract:

Salinity is one of the major factors limiting crop production in an arid environment. What adds to the concern is that all the legume crops are sensitive to increasing soil salinity. So it is implacable to either search for salinity enhancement of legume plants. The exogenous of osmoprotectants has been found effective in reducing the adverse effects of salinity stress on plant growth. Despite its global importance soybean production suffer the problems of salinity stress causing damages at plant development. Therefore, in the current study we try to clarify the mechanism that might be involved in the ameliorating effects of osmo-protectants such as proline and glycine betaine and compost application on soybean plants grown under salinity stress. Experiments were carried out in the greenhouse of the experimental station, plant nutritional physiology, Hiroshima University, Japan in 2011- 2012. The experiment was arranged in a factorial design with 4 replications at NaCl concentrations (0 and 15 mM). The exogenous, proline and glycine betaine concentrations (0 mM and 25 mM) for each. Compost treatments (0 and 24 t ha-1). Results indicated that salinity stress induced reduction in all growth and physiological parameters (dry weights plant-1, chlorophyll content, N and K+ content) likewise, seed and quality traits of soybean plant compared with those of the unstressed plants. In contrast, salinity stress led to increases in the electrolyte leakage ratio, Na and proline contents. Thus tolerance against salt stress was observed, the improvement of salt tolerance resulted from proline, glycine betaine and compost were accompanied with improved membrane stability, K+, and proline accumulation on contrary, decreased Na+ content. These results clearly demonstrate that could be used to reduce the harmful effect of salinity on both physiological aspects and growth parameters of soybean. They are capable of restoring yield potential and quality of seed and may be useful in agronomic situations where saline conditions are diagnosed as a problem. Consequently, exogenous osmo-protectants combine with compost will effectively solve seasonal salinity stress problem and are a good strategy to increase salinity resistance in the drylands.

Keywords: compost, glycine betaine, proline, salinity tolerance, soybean

Procedia PDF Downloads 364

Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

Abstract:

Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

Procedia PDF Downloads 253

Authors: Mehwish Shahzadi

Abstract:

Sunflower is cultivated all over the world not only as an ornament plant but also for the purpose of getting oil. It is the third most cultivated plant in the history because its oil considered best for health. The present study deals with the preparation of sunflower oil from commercial seed sample which was obtained from local market. The physicochemical properties of the oil were determined which included saponification value, acid value and ester value. Results showed that saponification value of the oil was 191.675, acid value was 0.64 and ester value to be 191.035 for the sample under observation. GC-MS analysis of sunflower oil was carried out to check its composition. Oleic acid was determined with linoleic acid and isopropyl palmitate. It represents the presence of three major components of sunflower oil. Other compounds detected were, p-toluylic acid, butylated hydroxytoluene, 1,2-benzenedicarboxylic acid, benzoic acid, 2,4,6-trimethyl-, 2,4,6-trimethylphenyl ester and 2,4-decadienal, (E,E).

Keywords: GC-MS, oleic acid, saponification value, sunflower oil

Procedia PDF Downloads 307

Authors: Fayiz Saifurahman, Noori Fida Mohammad

Abstract:

Afghanistan produces the highest rate of wheat, it is the first source of food, and food security in Afghanistan is dependent on the availability of wheat. Although Afghanistan is the main producer of wheat, on the other hand, Afghanistan is the largest importers of flour. The objective of this study is to assess the structure and dynamics of the wheat market in Afghanistan, can compute with foreign markets, and increase the level of production. To complete this, a broad series of secondary data was complied with, group discussions and interviews with farmers, agricultural and market experts. The research findings propose that; the government should adopt different policies to support the local market. The government should distribute the seed, support financially and technically to increase wheat production.

Keywords: Afghanistan, wheat, production , import

Procedia PDF Downloads 156

Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

Abstract:

In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

Procedia PDF Downloads 57

Authors: Sangwoo Oh, Jaewoo Kim, Dongmin Seo, Jaewon Park, Yongha Hwang, Sungkyu Seo

Abstract:

Plasmonics has been regarded one of the most powerful bio-sensing modalities to evaluate bio-molecular interactions in real-time. However, most of the plasmonic sensing methods are based on labeling metallic nanoparticles, e.g. gold or silver, as optical modulation markers, which are non-recyclable and expensive. This plasmonic modulation can be usually achieved through various nano structures, e.g., nano-hole arrays. Among those structures, plasmonic lens has been regarded as a unique plasmonic structure due to its light focusing characteristics. In this study, we introduce a custom designed plasmonic lens array for bio-sensing, which was simulated by finite-difference-time-domain (FDTD) approach and fabricated by top-down approach. In our work, we performed the FDTD simulations of various plasmonic lens designs for bacteria sensor, i.e., Samonella and Hominis. We optimized the design parameters, i.e., radius, shape, and material, of the plasmonic lens. The simulation results showed the change in the peak intensity value with the introduction of each bacteria and antigen i.e., peak intensity 1.8711 a.u. with the introduction of antibody layer of thickness of 15nm. For Salmonella, the peak intensity changed from 1.8711 a.u. to 2.3654 a.u. and for Hominis, the peak intensity changed from 1.8711 a.u. to 3.2355 a.u. This significant shift in the intensity due to the interaction between bacteria and antigen showed a promising sensing capability of the plasmonic lens. With the batch processing and bulk production of this nano scale design, the cost of biological sensing can be significantly reduced, holding great promise in the fields of clinical diagnostics and bio-defense.

Keywords: plasmonic lens, FDTD, fabrication, bacteria sensor, salmonella, hominis

Procedia PDF Downloads 266

Authors: Behzad Sani, Vida Jodaeian

Abstract:

In order to determine the impact of thermo priming on germination of soybean seeds, an experiment was conducted as a completely randomized design with three replications. The factors of studied included different time thermo priming (control, 5 and 10 minutes) through the placing seeds were exposed to oven. The results showed that the effect of thermo priming was significant on germination percentage, seedling dry weight and seedling vigour in P ≤ 0.05. Mean comparison showed that the highest germination percentage (77 %), seedling dry weight (1.39 g) and seedling vigour (107.03) were achieved by 10 minutes thermo priming.

Keywords: thermo priming, seedling, seedling production, seedling growth, soybean

Procedia PDF Downloads 322

Authors: Babatunde Olumide Olawale, Oyebode Olumide Oyediran

Abstract:

The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe.

Keywords: access control, multimodal biometrics, pattern recognition, security safe

Procedia PDF Downloads 324

Authors: P. Senthilraja, C. Rukumani Khandhan, M. Palaniappan, S. L. Rama, P. Sai Sushimitha, R. Madhan, J. Vinumathi, N. Vijayarangan

Abstract:

Nowadays, the logistics achieve a vast improvement in efficient delivery of goods. The technology improvement also helps to improve its development, but still the owners of transport vehicles face problems, i.e., fuel theft in vehicles by the drivers or by an unknown person. There is no proper solution to overcome the problems. This scheme is to determine the amount of fuel that has been stolen and also to determine the position of the vehicle at a particular time using the technologies like GPS, GSM, ultrasonic fuel level sensor and numeric lock system. The ultrasonic sensor uses the ultrasonic waves to calculate the height of the tank up to which the fuel is available. Based on height it is possible to calculate the amount of fuel. The Global Positioning System (GPS) is a satellite-based navigation system. The scientific community uses GPS for its precision timing capability and position information. The GSM provides the periodic information about the fuel level. A numeric lock system has been provided for fuel tank opening lever. A password is provided to access the fuel tank lever and this is authenticated only by the driver and the owner. Once the fuel tank is opened an alert is sent to owner through a SMS including the timing details. Third party monitoring software is a user interface that updates the information automatically into the database which helps to retrieve the data as and when required. Third party monitoring software provides vehicle’s information to the owner and also shows the status of the vehicle. The techniques that are to be proposed will provide an efficient output. This project helps to overcome the theft and hence to put forth fuel economy.

Keywords: fuel theft, third party monitoring software, bioinformatics, biomedicine

Procedia PDF Downloads 387