Combination Of Different Sensors Research Articles

Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

A selective feature optimized multi-sensor based e-nose system detecting illegal drugs validated in diverse laboratory conditions

Detecting illegal drugs, such as cannabis and methamphetamine, with high accuracy and speed is a complex problem that requires an innovative solution. To address this challenge, we propose a new method that utilizes a newly developed electronic nose (e-nose) system with an unprecedented total of 56 sensors, including four different types: metal-oxide-semiconductor (MOS), electrochemical (EC), non-dispersive infrared (NDIR), and photoionization detector (PID). Previous studies on gas sensors have typically validated results in a single controlled laboratory condition. In contrast, our study evaluated the performance of our system in different environments from the original training setting. To evaluate the detection performance of our system in unfamiliar environments and its robustness, we diluted the drug gas with normal air from six different laboratory environments. In addition, we evaluated the detection accuracy of our method using forward-feature selection, which allowed us to evaluate the impact of different combinations of sensors. By selectively optimizing sensors based on their ability to capture unique features of different drugs, our proposed method reduced the number of optimal sensors to less than half of 56 (24 selected). The proposed method achieved a detection accuracy of 93.03% and reduced the error rate from 12.23% to 6.97% using 5166 datasets including cannabis, methamphetamine, and tobacco. Our research not only provides rapid and enhanced accuracy, but also has the potential to be an effective tool for detecting illegal drugs in various settings, which could greatly contribute to strengthening national and social security.

Multi-Sensor-Based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression

In the fields of body-worn sensors and computer vision, current research is being done to track and detect falls and activities of daily living using the automatic recognition of human actions. In the area of human–machine communication, different combinations of sensors and communication technologies are often used to capture human action. Many researchers have also worked with artificial intelligent systems to detect actions, understand scenes, and implement systems that are more efficient in human action recognition. Although effective approaches are needed to detect outdoor activities with the combination of human actions, feature extraction can be quite a complicated task in a human activity recognition system development. Thus, this paper proposed a solution to detect human activities via hybrid descriptors based on robust features and accurate results. In this study, complex backgrounds, including multiple humans in video frames, were detected. First, inertial signal and video frames are pre-processed using denoising techniques, after which the frames are used to remove the background by detecting human motions and extracting the silhouettes. Then, these silhouettes are further used to extract the human body key points to make the human skeleton. Then the time and frequency domain features are extracted for inertial signals, and geometric features are extracted for the skeleton body points. Finally, multiple feature sets are combined and fed into a zero order optimization model, after which logistic regression is utilized to recognize each action. The proposed system has been evaluated on three benchmark datasets, including, the UP Fall dataset, the University of Rzeszow Fall dataset, and the SisFall dataset and proved its significance by achieving accuracy of 91.51%, 92.98%, and 90.23%, on the aforementioned datasets respectively.

Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building

This paper uses various machine learning methods which explore the combination of multiple sensors for quality improvement. It is known that a reliable occupancy estimation can help in many different cases and applications. For the containment of the SARS-CoV-2 virus, in particular, room occupancy is a major factor. The estimation can benefit visitor management systems in real time, but can also be predictive of room reservation strategies. By using different terminal and non-terminal sensors in different premises of varying sizes, this paper aims to estimate room occupancy. In the process, the proposed models are trained with different combinations of rooms in training and testing datasets to examine distinctions in the infrastructure of the considered building. The results indicate that the estimation benefits from a combination of different sensors. Additionally, it is found that a model should be trained with data from every room in a building and cannot be transferred to other rooms.

Proximal sensor data fusion for Brazilian soil properties prediction: Exchangeable/available macronutrients, aluminum, and potential acidity

Proximal sensing has achieved widespread popularity recently in soil science and the combination of different sensors and data processing methods is vast. Yet, confusion exists about which sensor (or the combination of sensors) is worthwhile considering the budget, scope, and the goals of the project. Hence, this work aims to test many modeling combinations using pXRF, Vis-NIR, and NixPro™ data and several preprocessing methods to offer a general guideline for exchangeable/available macronutrient (Ca2+, Mg2+, K+, P-rem), exchangeable Al3+, Al3+ saturation and soil potential acidity predictions (H++Al3+). A total of 604 samples were collected across four Brazilian states. Five types of spectra preprocessing, two sample moisture conditions for color, and the addition of extra explanatory variables were tested. The manifold combinations of these factors were modeled as continuous and categorical variables using the random forest algorithm and yielded 9310 models, from which prediction results were validated. The best results were achieved by fusing all sensors, proving the complementary nature of sensor data. However, pXRF data were key to significantly improving the predictions. Exchangeable Ca2+, Mg2+, Al3+, and Al saturation presented the best prediction results (R2 > 0.75), while available K+ and H++Al3+ had poor predictions (R2 < 0.5). Separating models by soil order improved predictions for Ultisols. Binning was the spectra preprocessing method that appeared most frequently in the best-performing models. The dry and moist color showed little effect in predictions. Categorical validation improved the usability of poorer models and maintained the good performance of the best models. Data fusion provided optimal results combining the three sensors, but pXRF provided key data for the good performance of combined sensor datasets.

Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors

Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning.

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML.

Methodological approach for the development of an operator assistance system for the press shop

The productivity of a deep drawing process strongly relies on its robustness as well as the experience of the machine operator. Steadily increasing requirements regarding weight, design and efficiency lead to a production operating increasingly closer to the process limits, making it more challenging to ensure a high robustness of the process. Minimal process fluctuations caused by disturbances such as varying material properties or changing tribological conditions may negatively affect the process due to deteriorated product properties as well as an increased risk of scrap. Thus, a target-oriented adjustment of available parameters by the machine operator becomes more difficult, and an increased knowledge about the causes of defects is more important. In the past, several approaches with different combinations of sensors and actuators have been investigated to enable a stable process window based on a control system. This paper presents a method to address the need for a more robust process by developing an operator assistance system that enables the identification of the component state and provides decision support to the machine operator. The methodological approach includes a thorough process analysis to evaluate the expediency of such a system and to make a reasonable preselection of sensors in order to avoid unnecessary costs.

Daily Gesture Recognition During Human-Robot Interaction Combining Vision and Wearable Systems

The recognition of human gestures is crucial for improving the quality of human-robot cooperation. This article presents a system composed of a Pepper robot that mounts an RGB-D camera and an inertial device called SensHand. The system acquired data from twenty people who performed five daily living activities (i.e., Having Lunch, Personal Hygiene, Working, House Cleaning, Relax). The activities were composed of at least two “basic” gestures for a total of 10 gestures. The data acquisition was performed by two cameras positioned laterally and frontally to mimic the real conditions. The acquired data were off-line classified considering different combinations of sensors to evaluate how the sensor fusion approach improves the recognition abilities. Specifically, the article presents an experimental study that evaluated four algorithms often used in computer vision, i.e., three classical machine learning and one belonging to the field of deep learning, namely Support Vector Machine, Random Forest, K-Nearest Neighbor and Long Short-Term Memory Recurrent Neural Network. The comparative analysis of the results shows a significant improvement of the accuracy when fusing camera and sensors data, i.e., 0.81 for the whole system configuration when the robot is in a frontal position with respect to the user (0.79 if we consider only the index finger sensors) and equal to 0.75 when the robot is in a lateral position. Interestingly, the system performs well in recognising the transitions between gestures when these are presented one after the other, a common event in the real-life that was often neglected in the previous studies.

Variance based sensitivity analysis of deep drawing processes based on neural networks using Sobol indices

Today’s deep drawing of car body parts is operated increasingly closer to the process limits, making it more challenging to ensure a high robustness of the process. Disturbances like varying material properties or changing tribological conditions may negatively affect the process, leading to deteriorated product properties and a loss of productivity. In the past, several approaches using different combinations of sensors and actuators to build up a control system have been investigated to fulfil the need of a more robust process. Hence, a thorough process analysis comes to be beneficial to evaluate the expediency of a control system and to make a reasonable preselection of sensors in order to avoid unnecessary costs. This paper presents a method using variant simulations to evaluate the expediency of a control system, including the necessary sensors. The influence of disturbances in the process is evaluated by a numerical sensitivity analysis under consideration of interaction effects. These higher order effects are calculated by Monte Carlo integration. Potential measurands are determined by the maximum achievable observability of not directly measurable quality criteria of a part. For this purpose, different modelling approaches of the observability are considered and compared with regard to their goodness-of-fit.

Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/2 images

Paddy rice agriculture in Southern China, especially Hunan Province, has been suffered from soil contamination. Several policies including rice fallow and decreasing cropping intensity have been implemented for food safety here. It is thus important to monitor rice planting area and cropping intensity to understand the effectiveness of those land-use policies. However, it is challenging to map rice planting areas due to the complex cropping systems (mixed single- and double-cropping), persistent cloud covers, small crop fields, let alone cropping intensity. Here we used all the available Sentinel-2 and all-weather Sentinel-1 imagery to generate a time series data cube to extract paddy rice planting areas and the rice cropping intensity in the Changsha, Zhuzhou, and Xiangtan areas, which is a traditional rice-growing region with small farms in China. Specifically, we investigated the performances of different features (i.e., spectral, seasonal, polarization backscatter) by comparing five scenarios with different combinations of sensors and features, and identified the most suitable features for certain rice types (early, middle, and late rice). The random forest classifier was used for the classification in the Google Earth Engine (GEE) platform, and a reference map in 2017 based on visual interpretation on the GaoFen-2 images were used for collecting the training and validation samples. The results showed the combined data from Sentinel-1/2 generally outperformed classifications using only a single sensor (Sentinel-1/2), but the contribution of different sensors to certain rice types varied. The early, middle and late rice with the highest accuracies within the five scenarios had the overall accuracies of 85%, 95%, and 95%, respectively (F1 = 0.55, 0.85, and 0.85). The compositing of different types of rice allowed us to generate the rice cropping intensity map with an overall accuracy of 81%, which to our limited knowledge is the first effort to map cropping intensity at 10-m resolution in such a fragmented subtropical region. The result showed the single cropping dominated the rice cropping system in the study area 88%, which used to be a typical area with double cropping of rice. Our study demonstrates the potential of mapping rice cropping intensity in a cloudy and highly fragmented region in South China using all the available Sentinel-1/2 data, which would advance our understanding of regional rice production and mitigation of soil contamination.

Flexible passive LC resonator for wireless measurement during curing of thermosets

Monitoring of fibre composites is an important task to spread the application of fibre composites. Cure monitoring of the polymer matrix of fibre composites during the fabrication can be realized with interdigital sensors. The measured impedance can be directly related to the curing process. However wiring the sensor may disrupt the material, therefore sensing should be done wireless. As many fibre composites are processed under high temperature and high pressure conditions no electronics can be used inside the material. Here we present the design, fabrication, and characterization of a wireless sensory system using a chipless strategy for online cure monitoring. For this purpose, different combinations of sensors and readers are designed, consisting of a planar inductor-capacitor (LC) resonator, where capacitance is an interdigital capacitor, and an inductor as antenna operating wirelessly through inductive coupling. The designs are realized onto flexible polyimide substrate using microfabrication technology. To characterize the coupling effect, the response of the reader antenna has been carried out when sensor is placed in its vicinity. Furthermore, the curing process of adhesive is demonstrated and results are discussed. It is shown that the curing process of a thermoset polymer can be monitored by a flexible chipless LC resonator wirelessly.

Suspended particulate matter in a submarine canyon (Whittard Canyon, Bay of Biscay, NE Atlantic Ocean): Assessment of commonly used instruments to record turbidity

Nepheloid layers with elevated concentrations of suspended particulate matter (SPM) are found throughout the world's oceans. They are generated by both natural processes, involving resuspension of seabed sediment by bottom currents, and anthropogenic sediment resuspension due to bottom trawling, dredging and in the future potentially due to deep-sea mining. These nepheloid layers represent pathways of lateral SPM transport, including lithogenic and biogenic sediment, organic matter, (trace) metals, organic pollutants and plastics. For assessment of the dispersion of these materials, it is essential that SPM mass concentrations can be accurately quantified. However, this is not straightforward as the detected turbidity signal, which is used as a proxy for SPM mass concentration, not only depends on the concentration of particles, but also on physical characteristics of these particles, such as particle size, substance and shape. Here we present a comparative study of turbidity data to assess the potential implications different sensors have on the estimates of SPM mass concentration. Optical backscatter sensors (OBSs), transmissometers and both low- and high-frequency ADCPs were deployed simultaneously in the Whittard Canyon (North Atlantic Ocean), and water samples were collected for quantification of SPM mass concentration and ex-situ particle size analysis. We found that SPM mass concentrations inferred from the transmissometer are easily overestimated in the biologically productive surface layer due to higher light absorption by chlorophyll-bearing phytoplankton, compared to suspended detritic particles. Furthermore, we observed that depending on sensor type some particles are not, or less well, detected. This is due to differences in particle size sensitivities of these sensors towards the diverse range of particle sizes found in the Whittard Canyon, whereby the low-frequency ADCP was most sensitive for coarse-grained material and the high-frequency ADCP and OBSs most sensitive for fine-grained material. In future studies, we suggest to use a combination of different sensors as the use of only one type of sensor could potentially lead to misinterpretation and mis-quantification of particle transport processes and fluxes.

Sensor Based Agility Assessment in Sport

Agility is the ability of a person to change direction quickly. Measuring the basic and sport-specific physical abilities of athletes is important in sport and recreation. To assess agility, various tests are regularly included in physical abilities test batteries. This paper presents a state-of-the-art approach to measuring human movement in agility tests using infrared optical gates and kinematic sensors. The combination of precise time measurement, position detection and motion measurement enables the acquisition of previously inaccessible kinematic and spatiotemporal variables. This makes the assessment of agility more accurate and provides the athletes and coaches with additional information. With the system presented in this paper, the assessment of the athletes’ agility can be recorded more objectively and with greater accuracy than would be possible with conventional equipment and methods. The athletes’ performance is assessed with a combination of different sensors, all of which are synchronized with our sensor-based system. This combination provides the athletes with new information about their movement, which is not possible with traditional professional timing devices used to measure time in agility tests. The presented sensor system can be used in various sports and test types, including the frequently used T-test. We present summarized results from a pilot study conducted in cooperation with the Slovenian youth volleyball team.

Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.

Simultaneous Intrinsic and Extrinsic Calibration of a Visual-Odometric Sensor System

The navigation system of a mobile robot is usually a combination of different sensors. In order to align these measurements, the intrinsic parameters of each sensor and the spatio-temporal extrinsic parameters among them are required to be precisely calibrated. In this paper, a simultaneous intrinsic and extrinsic calibration algorithm of a visual-odometric system is proposed. An initialization process is designed to generate rough estimation of the camera intrinsics, the odometric parameters, and the spatio extrinsic parameters. Then, a joint optimization problem is iteratively solved to obtain a refined calibration result. By taking temporal delay into consideration during the calibration process, the estimation accuracy is highly improved. Experiments are conducted with simulation datasets and also an industrial autonomous guided vehicle (AGV) system. The results are presented to validate the efficiency of our algorithm.

GEOSPATIAL AND TEMPORAL ANALYSES OF RURAL AREA FOR VEGETATION ANALYSIS

The current paper aims at presenting multitemporal monitoring of two crop fields using close-range aerial photogrammetric techniques with a combination of different sensors. A fixed-wing unmanned aerial vehicle was used to map the crops regularly in 2016 and 2017. The goal of the following study is to show the experimental results of an enhanced keypoint number extraction of NIR and TIR images and the importance of radiometric calibration of close-range data.

Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients.

Recovery of the walking function is one of the most common rehabilitation goals of neurological patients. Sufficient and adequate sleep is a prerequisite for recovery or training. To objectively monitor patients’ progress, a combination of different sensors measuring continuously over time is needed. A sensor-based technological platform offers possibilities to monitor gait and sleep. Implementation in clinical practice is of utmost relevance and has scarcely been studied. Therefore, this study examined the feasibility of a sensor-based technological platform within the clinical setting. Participants (12 incomplete spinal cord injury (iSCI), 13 stroke) were asked to wear inertial measurement units (IMUs) around the ankles during daytime and the bed sensor was placed under their mattress for one week. Feasibility was established based on missing data, error cause, and user experience. Percentage of missing measurement days and nights was 14% and 4%, respectively. Main cause of lost measurement days was related to missing IMU sensor data. Participants were not impeded, did not experience any discomfort, and found the sensors easy to use. The sensor-based technological platform is feasible to use within the clinical rehabilitation setting for continuously monitoring gait and sleep of iSCI and stroke patients.

AlphaLogger: detecting motion-based side-channel attack using smartphone keystrokes

Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger- an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer.

Smart hybrid nano composite concrete embedded sensors for structural health monitoring

Smart Hybrid Nano composite sensors are developed to diagnosing their own state of strain in which Nanomaterials used as conducting filler. Nanomaterial like carbon fibre (CF), multiwall carbon nanotube (MWNCT), and graphene have a unique property that they can change applied strain into electric resistance. Hence, they can be used as conducting filler in the cement matrix. The present study mainly concerns with developing smart hybrid Nano composite cement based sensors and implementation of these sensors for structural health monitoring by embedding sensors into structural components. Two different combinations of sensors are developed by inserting Nano materials (0.25 and 0.5% weight of cement of MWCNT, CF, and Graphene) into cement mortar matrix. The insertion of carbon Nano filler into base material makes them into sensitivity to mechanical modification. From the electromechanical test it indicates that both combination sensors show good strain sensing with respect to applied load and also it is observed that is increased in flexural strength about 30.08% for 0.5% combination. A flexural and compression test was conducted by embedding Nano composite cement based sensors into structural components. The test results shows the variation in resistance for both compression and flexural loading which indicates into self-sensing property of structural element by embedding sensors into them, hence it will be helpful in monitoring the structure.