Perform Data Acquisition Research Articles

Detection of Hand Poses with a Single-Channel Optical Fiber Force Myography Sensor: A Proof-of-Concept Study

Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer.

FPGA: Field programmable gate array-based four-channel embedded system for ultrasonic imaging of under-water concrete structures.

Non-destructive testing is needed for the evaluation of quality and safety of concrete structures in the field of civil engineering. The imaging of concrete/reinforced cement concrete structures (RCC) is a challenging task due to the non-homogeneous properties of the concrete material. To address this challenge, a novel real-time, re-configurable, four-channel embedded system has been designed and developed to image the internal details of the concrete samples using the water immersion pulse-echo (PE) mode under automation, which needs access from one side of the structure. The system performs data acquisition (DAQ) of the amplified echo signals under the control of the computer via a universal serial bus interface. A graphical user interface (GUI) has been developed using C# in a Visual environment, for image acquisition and control of the DAQ parameters. The performance of the system has been evaluated by acquiring B-Scan images of three types of concrete test blocks having side drilled holes (SDHs) and simulated inclusions embedded in concrete blocks of M20 grade using a linear array of 92kHz water immersion transducers operating in under-water PE mode. The acquired B-Scan images revealed the internal details of the concrete test blocks with sizing of the SDHs and inclusions. Therefore, the developed four-channel ultrasonic imaging system can visualize the internal details of under-water concrete structures, such as bridges and sea links, with the help of corresponding 2-D cross-sectional images, acquired using the developed system.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Ultrasonic Imaging of Concrete/ RCC Structures using Immersion Technique

There has been a rapid increase in the construction of under-water concrete structures all over the world and the maintenance and rehabilitation of these concrete structures pose a major concern for estimating the quality and the useful remaining life of these structures. To cater to such needs, the detection of defects, porosities and rebar locations inside the Concrete/Reinforced Cement Concrete (RCC) structures have been carried out using the in-house developed single-channel ultrasonic imaging system at Electronics Division, BARC, India. Commercially, the inspection of under-water concrete structures is being carried out by utilizing Ultrasonic Pulse Velocity (UPV) method in Transmit-Receive (T-R) mode to indirectly estimate the density and strength of the concrete structure under test. UPV method requires a skill to interpret the data provided by the UPV instrument. The in-house developed single-channel system can perform imaging of Concrete/ RCC structures both in Pulse Echo (PE) as well as in T-R mode and can function in UPV mode. The imaging system performs data acquisition and temporal averaging of the RF bipolar data to achieve high Signal-to-Noise Ratio (SNR) ( > 20 dB). In this paper, different types of concrete blocks with simulated defects and RCC blocks with steel rebars have been utilized for the inspection using the single-channel imaging system in water-immersion mode. The B-Scan images have been acquired for the concrete and RCC sample test blocks, using 54 KHz water immersion transducer in PE mode. The acquired B-Scan images have revealed the internal details of the Concrete and RCC sample test blocks, resembling the defects generated in the test blocks. The A-Scan waveform was also acquired on the 1 metre thick concrete block in contact PE mode, which shows the capability of the single channel ultrasonic imaging system for the inspection of such thick concrete blocks.

Dynamic assessment of self-regulated learning in preschool

The assessment of self-regulated learning is a relevant research topic in early childhood development. However, there are few ecologic measures to assess self-regulated learning in preschool as a dynamic and multidimensional process. This study aims to fill this gap by presenting the development and validation of the Dynamic Assessment of Self-regulated learning in Preschool (DASP) method. A dynamic assessment of the construct may constitute an important contribution as it enables the acquisition of cross observational, verbal, and performance data. The DASP method was developed within a theoretical framework of self-regulation, including all cyclical phases, namely, forethought, performance, and self-reflection. Specifically, this method requires children to be questioned in the forethought and self-reflection phases, and observed in the performance phase, as the researcher notes their strategies. This method is used while children engage in authentic preschool tasks. To achieve the study's aim, 214 preschool children were asked to participate. In this research, children performed the Clown task (cognitive task) and the Head-Toes-Knees-Shoulders task (motor task). Item Response Theory analyses provided good item fit for the DASP method (forethought: .99, performance: 1.00, self-reflection: .99), good values of the tasks' reliability (Clown: .92; HTKS: .85), and evidence of the participants' difficulty level in completing the tasks. Results indicated that the children experienced more difficulty in the performance phase, as opposed to the other phases. The potentialities, constraints, and practical implications of the DASP method will be discussed in terms of contributions for theory and practice.

Fast impedance matrix calculation for real-time application

Computational effort, complexity and storage may constitute limitations for performing data acquisition and real-time simulation to support the operation of electrical power systems. Some techniques and simulations necessary to obtain the power system state depend on calculations for obtaining the inverse of the admittance matrix. Matrix inversion of large power networks in real-time requires big computational effort and storage, and sometimes become unfeasible for real-time applications. Considering power systems with busbars splitting and coupling possibilities, the admittance matrix dimension changes, becoming bigger the computational effort and memory. In this paper a modification in the Woodbury Formula is proposed, resulting in just one storage in the inverse admittance matrix; being updated to obtain the new inverse matrix of the network even with different dimensions. Results show that the proposed technique reduce the computational cost, complexity, and memory storage necessity to obtain the matrix inversion, proving to be an alternative for real-time simulations.

Design of an architecture for storing a time connected model of a digital twin

Recently, in connection with the development of information technology, digital twin technology has become widespread, which allows replacing physical and analog resources with digital ones. The digital twin is also a relevant and effective method of real-time interaction. However, with a wealth of real-time data and information from the digital twin, it is difficult to present them intuitively to users and operators. A digital twin for the study of an arbitrary object can formally be represented as mathematical abstractions designed for efficient processing of temporal multimodal da-ta of the object under study. Based on the above abstractions and their application for the formal specification of the digital twin, the so-called multiimage is built, which ac-tually provides a formal description of the sequences of multimodal data about the ob-ject under study, obtained taking into account time as a result of measurement, genera-tion and other related data acquisition operations. As a result of the study, a sequence of discrete states of the object under study is formed, each of which can be characterized by a set of temporal multimodal data associated with a specific moment in time, which actually serves as a temporal key for a certain state of the object. The purpose of the study is to design the architecture of a software system for stor-ing and then reproducing a temporal connected model using a database designed to store time series data. TimescaleDB, a relational database for time series data, is pro-posed as the main data storage engine. The main challenges for a database that must process a large amount of temporary data is its scalability - to process and store a large amount of data without performance degradation. The proposed method for building a temporal connected model of a digital twin based on TimescaleDB using the concept of hypertables will achieve efficient use of re-sources by parallelizing the load when searching for data, and a three-level architec-ture designed to distribute the responsibilities of a software system can greatly simplify the implementation and subsequent processing of a temporal model of a digital twin.

Use of a Raspberry-Pi Video Camera for Coastal Flooding Vulnerability Assessment: The Case of Riccione (Italy)

Coastal monitoring is strategic for the correct assessment of nearshore morphodynamics, to verify the effects of anthropogenic interventions for the purpose of coastal protection and for the rapid assessment of flooding vulnerability due to severe events. Remote sensing and field surveys are among the main approaches that have been developed to meet these necessities. Key parameters in the assessment and prevision of coastal flooding extensions, beside meteomarine characteristics, are the topography and slope of beaches, which can be extremely dynamic. The use of continuous monitoring through orthorectified video images allows for the rapid detection of the intertidal bathymetry and flooding threshold during severe events. The aim of this work was to present a comparison of different monitoring strategies and methodologies that have been integrated into repeated surveys in order to evaluate the performance of a new camera system. We used a low-cost camera based on Raspberry Pi called VISTAE (Video monitoring Intelligent STAtion for Environmental applications) for long-term remote observations and GNSS-laser tools for field measurements. The case study was a coastal tract in Riccione, Italy (Northern Adriatic Sea), which is the seat of nourishment interventions and of different types of underwater protection structures to combat coastal erosion. We performed data acquisition and analysis of the emerged beach and of the swash zone in terms of the intertidal bathymetry and shoreline. The results show a generally good agreement between the field and remote measurements through image processing, with a small discrepancy of the order of ≈0.05 m in the vertical and ≈1.5 m in the horizontal in terms of the root mean square error (RMSE). These values are comparable with that of current video monitoring instruments, but the VISTAE has the advantages of its low-cost, programmability and automatized analyses. This result, together with the possibility of continuous monitoring during daylight hours, supports the advantages of a combined approach in coastal flooding vulnerability assessment through integrated and complementary techniques.

Modelling of biosignal based decision making model for intracranial haemorrhage diagnosis in IoT environment

AbstractIn recent times, the Internet of Things (IoT) in healthcare is a novel and promising emerging area that offers a number of benefits to users and healthcare professionals, enabling real time monitoring of diseases, convenience, and ease of use. With respect to aid the patients by the use of IoT with biosignals enabled solutions, recent researches make use of machine learning (ML) techniques for decision making, particularly haemorrhage diagnosis. In this view, this paper presents an intelligent intracranial haemorrhage (ICH) diagnosis using biosignals (IICHD‐BS) in IoT environment. The IICHD‐BS technique performs data acquisition process using two sensors namely complementary metal oxide semiconductor sensor and ESP8266 Wi‐Fi module. They are employed to collect CT images of the CT scanner and transforms them into electrical signals for storing them in the server. Besides, the IICHD‐BS technique employs optimal region growing based segmentation approach for detecting the infected brain regions in the CT images. In addition, EfficientNet based feature extraction and functional link neural network (FLNN) based classification approach is used for detecting and classifying the existence of ICH. For experimental validation, a set of two benchmark dataset ICH datasets are used and the experimental outcomes are evaluated with respect to different measures. The simulation outcomes demonstrated the improved performances of the proposed algorithm over the current state of art techniques.

RAMI 4.0 w transformacji cyfrowej przedsiębiorstwa - studium przypadku

The implementation of the HMI (Human-Machine Interface) operator panel on the production line is a new stage of the digital transformation of a paints and varnishes manufacturer in the SMEs sector. As part of an innovation plan, the process of production data acquisition was restructured, by means of IT (Information Technology), into an ERP (Enterprise Resource Planning) system. The introduction of an HMI panel made it possible to digitalize data acquisition and operation reporting to the ERP system. The digital transformation of the SME 4.0 company was modelled on the Industry 4.0 trend solutions. The specialized model, developed according to RAMI 4.0 (Reference Architecture Model Industry 4.0), effected change of a partial and local nature in the enterprise. The data is now entered directly into the production area by the employees on the technological line, once they have logged in to the ERP system. Access to digital production data obtained from a SSOT (single source of truth) benefited all those who used the ERP system. A postimplementation analysis of a two-year application of the restructured data acquisition process indicates the business areas which profit from digital operational data acquisition and higher quality production reporting.

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the nonlinear and multidimensional data. However, most DL models are data-driven black boxes suffering from nontransparent inner workings. In this work, we developed an explainable DL-assisted visualized fluorometric array-based sensing method. Based on a data set of 8496 fluorometric images of various target molecule fingerprint patterns, two typical DL algorithms and eight machine learning algorithms were investigated for the efficient qualitative and quantitative analysis of six aminoglycoside antibiotics (AGs). The convolutional neural network (CNN) approached 100% prediction accuracy and 1.34 ppm limit of detection of six AG analysis in domestic, industrial, medical, consumption, or aquaculture water. The class activation mapping assessment explicates how the CNN model assesses the importance of sensor elements and makes the discrimination decision. The feedback mechanism guides the sensor array evolution for less material using a simplified operation or efficient data acquisition. The explainable DL-assisted analysis method establishes an "end-to-end" strategy to resolve the black box of the DL algorithm, promote hardware design or principle optimization, and contribute facile indicators for environment monitoring, disease diagnosis, and even new scientific discovery.

The Application of Microelectromechanical Systems (MEMS) Accelerometers to the Assessment of Blast Threat to Armored Vehicle Crew.

This paper describes the development and application of an autonomous register and measurement system (ARMS), and the application of microelectromechanical systems (MEMS) accelerometers to the assessment of blast threat to armored vehicle crews. Taking measurements with reference to an explosion is one of the principal issues in the protection of crews of special vehicles. The proposed ARMS reduces research costs and contributes to the development of an autonomous, wireless test stand, applicable in various research areas and industry. The ARMS performs data acquisition with simultaneous measurement in multiple channels. The maximum sampling rate is 100 kHz and the sensor range is ±500 g. This solution is an alternative to cable systems, which have a high energy demand. The functionality of the developed autonomous measuring system is demonstrated experimentally. The paper concludes with a field study of the proposed system and the application of MEMS accelerometers via a mine blast test of a military vehicle at level 4 of STANAG 4569.

상업 운항 중인 대형 컨테이너선의 항차 중 속력 시운전을 통한 선속-동력 특성 연구 Part I

This paper presents the analysis of the speed-power performance in the real sea using a large container vessel data provided as a test bed from a shipping company. To perform a speed trial of the vessel during a commercial voyage, the on-board measuring device and various operation data acquisition systems were mounted on the vessel for long-term performance monitoring and the voyage operated under the container loading condition close to the design draft was adopted. The content of this paper consists of Part I and Part II. Part I, such as this paper, contains the speed trial method and analysis results of the operating vessel. Part II contains the analysis of the speed-power characteristics change over time and before and after hull cleaning using operation data measured from the voyage operated under a condition similar to the speed trial.

Smart monitoring of a full-scale composite hydrofoil manufactured using automated fibre placement under high cycle fatigue

Fibre reinforced composites materials offer a pathway to produce passive shape adaptive smart marine propellers, which have improved performance characteristics over traditional metallic alloys. Automated fibre placement (AFP) technology can provide a leap forward in cyber-physical automated manufacturing, which is essential for the implementation and operation of smart factories in the marine propeller industry towards Industry 4.0 readiness. In this paper, a comprehensive structural health monitoring routine was performed on an AFP full-scale composite hydrofoil to gain confidence in its dynamic and structural performances through a number of active and passive sensors. The hydrofoil was subjected to constant amplitude flexural fatigue loading in a purpose-built test rig for 105 cycles. The hydrofoil was embedded with distributed optical fibre sensors, traditional electrical strain gauges and linear variable displacement transducers. Both microelectromechanical system and piezoelectric accelerometers were used to conduct experimental modal analyses to observe changes in the modal response of the hydrofoil at regular intervals throughout the fatigue program. The hydrofoils modal response, as well as the stiffness measured using both displacements and strains, remained unchanged over the fatigue loading regime demonstrating the structural integrity of the hydrofoil. The optical fibre sensors endured the fatigue test cycles showing their robustness under fatigue loads. Furthermore, the sensing systems demonstrated the potential of being utilised as a useful maintenance tool combining their adaptability with automated manufacturing during manufacturing through integration within the hydrofoil, a structural test framework for performance measurement, data acquisition and analytics for visualisation, and the prospect of decision making for maintenance requirement during any onset in structural performance.

Research on Application of Truck Operation Data Acquisition System in Open-pit Coal Mine

Research on Application of Truck Operation Data Acquisition System in Open-pit Coal Mine

Acupuncture Efficacy Study Using Electromyographic Signals

Acupuncture is a centuries-old therapeutic technique. However, because of the large number of complicating circumstances, it has been difficult to clearly prove the treatment's therapeutic effectiveness.As a result, acupuncture has failed to acquire acceptance in the mainstream clinical sector. An electromyography (EMG) sensor was built and used to test the efficacy of acupuncture in alleviating muscular stiffness in this study. Electrodes, differential and inverting amplifiers, filters, and a full-wave rectifier made up the EMG circuit. The output of the circuit was sent to a microcontroller for analog-to-digital transformation in order to perform data acquisition. Acupuncture was used to treat four participants who had muscular dysfunction in various regions of their bodies in our case study. Before and after the therapy, EMG signals at the damaged regions were recorded. The findings revealed that the therapy had no immediate conceivable impact on the patients, since the levels of muscular contraction before and after the treatment were comparable. When the EMG signals were measured 30 minutes after the therapy, signs of muscular alleviation were found. This shows that acupuncture does supply patients with beneficial medicine, although slowly. The act of placing the highly conducting needles into the acupuncture sites, we believe, is similar to connecting a parallel wire to a circuit, resulting in a short-circuited route at the meridian. It permits the meridian's polarized in- ner energy, or qi, to pass through. The equilibrium in qi regulation can therefore be restored by unclogging the ow of qi.The repair process is relatively slow, and the treatment impact may not be immediately apparent, because the consti- tutive qualities at the acupuncture points where the needles are pricked may not alter quickly.

Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over Sensor Networks

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

Optimization of data acquisition operation in optical tomography based on estimation theory.

The data acquisition process is occasionally the most time consuming and costly operation in tomography. Currently, raster scanning is still the common practice in making sequential measurements in most tomography scanners. Raster scanning is known to be slow and such scanners usually cannot catch up with the speed of changes when imaging dynamically evolving objects. In this research, we studied the possibility of using estimation theory and our prior knowledge about the sample under test to reduce the number of measurements required to achieve a given image quality. This systematic approach for optimization of the data acquisition process also provides a vision toward improving the geometry of the scanner and reducing the effect of noise, including the common state-dependent noise of detectors. The theory is developed in the article and simulations are provided to better display discussed concepts.

Workplace occupant comfort monitoring with a multi-sensory and portable autonomous robot

The majority of the techniques in the building comfort monitoring state-of-the-art are based on local/manual measurements or on permanent sensor networks. These techniques entail imprecision and randomness (in the first case) and high-cost installations and a lack of flexibility to eventual changes in buildings (in the second). However, intelligent mobile platforms are becoming significantly more important as they perform data acquisition adapted to specific scenarios and schedules. In this paper, we present a robotic platform focused on performing the workplace occupant comfort-monitoring process. The soundness of our proposal compared to others lies on that it gathers most of the necessary properties of an effective monitoring platform: it collects a wider range of variables; it autonomously navigates in inhabited buildings managing occlusions and unexpected events; it conducts multiple monitoring sessions in one or several days; it provides comfort evaluations. Additionally, it can be very useful for energy engineers and construction professional as it provides valuable information in regard to comfort: it detects the best/worst results of the tested variables, locates discomfort in specific areas and moments, recognizes discomfort patterns and globally classifies zones into comfort classes. This robotic platform has been successfully tested in the interiors of buildings, providing significant and clear results in comfort terms (a case study is presented in this paper). However, some limitations and improvements should be addressed. Among other aspects, the computer-robot communication robustness for long distances and the procedure for detecting of small obstacles must be improved in the future.

Embedded system for ultrasonic imaging of under-water concrete structures

Many of the modern concrete structures such as sea-links and bridges are built in water. They require structural health monitoring (SHM) and audit to ascertain the quality and estimation of remaining useful life. To meet these requirements, a single-channel ultrasonic imaging system has been designed and developed to detect defects, porosities and rebar locations inside the Concrete/ Reinforced Cement Concrete (RCC) structures. This real-time and reconfigurable embedded system whas evaluated by carrying out under-water imaging of Concrete and RCC test blocks. Conventionally, Ultrasonic Pulse Velocity (UPV) method is being utilized over three decades in Transmit-Receive (T-R) mode to estimate the density and strength of the concrete structure under test. UPV method requires a skill to interpret the data provided by the UPV instrument. The novelty of the single-channel system discussed in this paper is that it is capable of inspecting Concrete/ RCC structures both in Pulse Echo (PE) as well as in T-R mode. An application software has been developed using C# in a visual environment for image acquisition and the control of Data Acquisition (DAQ) hardware has been interfaced to the computer via USB. The imaging system performs data acquisition and coherent averaging of the RF bipolar data to achieve Signal-to-Noise Ratio (SNR) higher than 20 dB. The performance of the system was evaluated by acquiring B-Scan images of the water submerged concrete test blocks having Side Drilled Hole (SDH) and RCC blocks with one as well as two steel rebars, using 54 kHz water immersion transducer in PE mode. The acquired B-Scan images have revealed the internal details of the sample test blocks, resembling the defects generated in the test blocks. High voltage tone burst bipolar pulser with maximum output of (± 350 V) has been specifically designed for this system to inspect highly attenuative porous and non-homogeneous Concrete/ RCC materials for the detection of SDH and reinforced steel bars in the concrete test blocks, using B-Scan imaging technique.