Embedded Sensors Research Articles

On the Energy Behaviors of the Bellman–Ford and Dijkstra Algorithms: A Detailed Empirical Study

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind.

Temperature and ST-segment morphology remote monitoring: new perspectives for implantable cardiac monitors in Brugada syndrome.

Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population. We present a case of a 40-year-old man exhibiting a BrS type 2 pattern on 12-lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug-induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever-induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs. ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients.

Filtering Efficiency and Design Properties of Medical- and Non-Medical-Grade Face Masks: A Multiscale Modeling Approach

Approved medical face masks have been shown to prevent the spread of respiratory droplets associated with coronavirus transmission in specific settings. The primary goal of this study was to develop a new strategy to assess the filtering and transmissibility properties of medical- and non-medical-grade face masks. In this study, we designed and assessed the filtering efficiency of particles through six different masks with a diverse set of fabrics, textures (woven and non-woven), fiber diameters, and porosity. The filtering and transmissibility properties of face mask layers individually and in combination have been assessed using mathematical analyses and new experimental data. The latter provided velocity profiles and filtration efficiencies for which the data were shown to be predictable. The filtration efficacy and pressure drop across each fabric have been tested using an aerosol particle spray and scanning electron microscopy. To assess clinical significance, the temperature and humidity of the masks were tested on a group of healthy volunteers spanning various age ranges (9–79 years old), utilizing an embedded temperature sensor disc. Also, a mask filter model was developed using fluid dynamic simulations (Solidworks Flow) to evaluate the aerodynamic dispersion of respiratory droplets. Overall, the FFP2 and FFP3 masks demonstrated the highest filtration efficiencies, each exceeding 90%, a feature of multi-layered masks that is consistent with simulations demonstrating higher filtering efficiencies for small particles (<5 µm). The velocity and temperature simulations of all six masks revealed a low air velocity (~1 m/s) inside the mask and a temperature variation of approximately 3 °C during the breathing cycle.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Embedded Sensors with 3D Printing Technology: Review.

Embedded sensors (ESs) are used in smart materials to enable continuous and permanent measurements of their structural integrity, while sensing technology involves developing sensors, sensory systems, or smart materials that monitor a wide range of properties of materials. Incorporating 3D-printed sensors into hosting structures has grown in popularity because of improved assembly processes, reduced system complexity, and lower fabrication costs. 3D-printed sensors can be embedded into structures and attached to surfaces through two methods: attaching to surfaces or embedding in 3D-printed sensors. We discussed various additive manufacturing techniques for fabricating sensors in this review. We also discussed the many strategies for manufacturing sensors using additive manufacturing, as well as how sensors are integrated into the manufacturing process. The review also explained the fundamental mechanisms used in sensors and their applications. The study demonstrated that embedded 3D printing sensors facilitate the development of additive sensor materials for smart goods and the Internet of Things.

Single-Ended Eddy Current Micro-Displacement Sensor with High Precision Based on Temperature Compensation.

To measure the micro-displacement reliably with high precision, a single-ended eddy current sensor based on temperature compensation was studied in detail. At first, the principle of the eddy current sensor was introduced, and the manufacturing method of the probe was given. The overall design plan for the processing circuit was induced by analyzing the characteristics of the probe output signal. The variation in the probe output signal was converted to pulses with different widths, and then it was introduced to the digital phase discriminator along with a reference signal. The output from the digital phase discriminator was processed by a low-pass filter to obtain the DC component. At last, the signal was amplified and compensated to reduce the influence of temperature. The selection criteria of the frequency of the exciting signal and the design of the signal conditioning circuit were described in detail, as well as the design of the temperature-compensating circuit based on the digital potentiometer with an embedded temperature sensor. Finally, an experimental setup was constructed to test the sensor, and the results were given. The results show that nonlinearity exists in the single-ended eddy current sensor with a large range. When the range is 500 μm, the resolution can reach 46 nm, and the repeatability error is ±0.70% FR. Within the temperature range from +2 °C to +58 °C, the voltage fluctuation in the sensor is reduced to 44 mV after temperature compensation compared to the value of 586 mV before compensation. The proposed plan is verified to be feasible, and the measuring range, precision, and target material should be considered in real-world applications.

Development of a Stress Sensor for Solids

The paper presents the results of experimental design of a cortex of the embedded stress sensor, which allows to determine stresses in the cross-section of large-scale laboratory monolithic structures made on the basis of mineral and polymer binders (concrete, gypsum, etc.). The objectives of the design were to develop a constructive solution of the stress sensor housing based on strain gauges, which has small dimensions, low manufacturing cost, as well as high resolution and stability of readings over the entire sensitivity area (stress up to 400 kgf/cm2).The stress sensor allows to determine the stress in laboratory structures with high accuracy, without significantly affecting the stress-strain state of the section at different stages of operation of the structural element.

Real-time embedded pressure and flow rate sensor in polydimethylsiloxane-based microchannel using fiber Bragg grating technique

Polydimethylsiloxane (PDMS) has attracted huge interest as the soft material for microchannel fabrication, due to its high biocompatibility, durability and low cost. The rheological properties of the fluid and the deformable properties of PDMS substrate have imposed significant impacts on the fluid flow in the microchannel, therefore it is of critically importance to develop sensing methods for the accurate flow rate monitoring and the flow-induced pressure measurement without disturbing the fluid flow in the microchannel. This paper presents an embedded and miniaturized pressure sensor based on the Fiber Bragg Grating (FBG) technique for the measurement of both flow rate and flow-induced pressure in PDMS-based microchannel. This method has demonstrated a pressure sensitivity of 0.06 p.m./Pa and the Limit of Detection (LoD) for pressure is 19.5 Pa. The linearity between flow-induced pressure and flow rate in PDMS-based microchannel is about 4.86 Pa(ml/h)−1 based on simulation results and is within the range between 4.33 Pa(ml/h)−1 and 6.67 Pa(ml/h)−1 obtained from experimental results. The LoD of flow rate using this method is within the range between 2.9 ml/h and 4.5 ml/h. The random instability induced by the operation of the reconnection of syringe back to the liquid flow system was identified and the calibration of this instability is also presented in this paper. Using this method, the pressure and flow rate within PDMS-based microchannel can be monitored with high reliability and high sensitivity, and this will benefit the application of PDMS-based microfluidics in areas that require highly precise control in flow conditions, e.g. biosensing, cell culture, and drug delivery.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.

Microstrip and Metamaterial Embedded Patch Antenna Sensors for Determination of Moisture in Rice, Wheat, and Pulse Grains

Moisture Content determination inside the grains is essential for grain processing activities including harvesting, storing, inspecting, and transporting. In this paper, microstrip and metamaterial embedded patch sensors were developed to determine the moisture content and mean relative error (MRE) of rice, wheat, and pulse. The sensors were simulated in HFSS, prototypes were fabricated and the measurement was done using VNA and anechoic chamber. The Microstrip sensor was operating at a frequency of 5.2 GHz and the metamaterial-embedded sensor was operating at 4.5 GHz. Calibration equations were obtained by using the values of reflection coefficients and moisture content ranging from 11.76% to 25%. For analyzing the efficacy of designed sensors, predicted moisture content (PMC) and actual moisture content (AMC) have been compared and the lowest mean relative error has been determined. The analysis shows that the metamaterial embedded sensor has better sensitivity and accuracy than the microstrip sensor. The lowest mean relative error in moisture prediction for metamaterial sensor was 1.07% for rice, 1.13% for wheat, and 1.47% for pulse respectively. A comparison of the proposed metamaterial embedded sensor with earlier designed sensor in literature was also presented in this work and it was found that the proposed sensor had more accuracy and sensitivity than earlier designs.

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition.

Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple technical skills of playing badminton, it is usually a challenging task due to the difficulty of feature extraction from the sensor data. As a kind of end-to-end approach, deep neural networks have the capacity of automatic feature learning and extracting. However, most current studies on sensor-based badminton activity recognition adopt CNN-based architectures, which lack the ability of capturing temporal information and global signal comprehension. To overcome these shortcomings, we propose a deep learning framework which combines the convolutional layers, LSTM structure, and self-attention mechanism together. Specifically, this framework can automatically extract the local features of the sensor signals in time domain, take the LSTM structure for processing the badminton activity data, and focus attention on the information that is essential to the badminton activity recognition task. It is demonstrated by the experimental results on an actual badminton single sensor dataset that our proposed framework has obtained a badminton activity recognition (37 classes) accuracy of 97.83%, which outperforms the existing methods, and also has the advantages of lower training time and faster convergence.

Research on Tuning Control Technology for Wireless Power Transfer Systems for Concrete Embedded Sensors

Concrete embedded sensors play a very important role in structural health monitoring. However, the time of endurance of sensors remains a performance bottleneck and sensors need to be charged without damaging the structure as well. Wireless power transfer (WPT) technology is a promising approach to solving this problem. However, the electromagnetic characteristics of concrete medium can cause WPT systems to be untuned and can reduce the energy transmission efficiency of the system. In this paper, the induced medium loss and eddy current loss of a WPT system in concrete are calculated using analytical equations and finite element analysis method. The equivalent circuit model of a concrete–air transmedia WPT system is established according to the calculated losses and a composite tuning control technology is proposed based on the above analysis. In addition, the composite tuning control technology combines the advantages of frequency-modulation tuning and dynamic compensation tuning to ensure the overall resonance of the WPT system. The tuning control technology can ensure the whole resonance of the WPT system and make the natural resonant frequencies of primary and secondary sides consistent. The experimental results show that compared with the untuned control technology, the output power and efficiency of the tuned control system increased by 73% and 11.05%, respectively. The proposed tuning control technology provides direction for future charging of concrete-embedded sensors.

Multi-scale insight into key factors affecting stress perception of smart aggregates

Smart aggregate (SA) is a multi-module integrated embedded monitoring sensor that has been employed in the construction, operation and maintenance of civil infrastructure. However, the differences in the shape and Yong’s modulus of the SA and the host matrix lead to the matching errors between the SA sensor readings and the actual in-situ stress values. A systematic analysis of how the modulus ratio of the SA to the host matrix (E p/E m), the length-to-height ratio of the sensor (L/H), and the non-homogeneity of the particle media interfere with the sensor monitoring data has been conducted in this paper using the finite element method and the discrete element method. In addition, the differences of the stress response from SA sensor under different load contact areas have been further investigated. Simultaneously, the numerical analysis has been validated by means of both theoretical derivations and laboratory tests. The analysis results show that E p/E m and L/H are the two key factors affecting the matching error. In particular, when E p/E m > 1, the matching error is positive and vice versa, while the L/H of the sensor is negatively correlated with the matching error. It is also noteworthy that the errors in the SA sensor monitoring data due to media inhomogeneity can only be negligible when the sensor diameter is larger than 20 times of the largest particle size in the host matrix. This study provides a theoretical guidance for optimizing the design of SA sensors and improving the accuracy of their measurement results.

Information-Based Sensor Placement for Data-Driven Estimation of Unsteady Flows

Estimation of unsteady flowfields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flowfield representations can be very high-dimensional, their dynamics can sometimes have low-order representations that may be estimated using a few, appropriately placed measurements. This paper presents a sensor-selection framework for the intended application of data-driven, flowfield estimation. This framework combines data-driven modeling, steady-state Kalman filter design, and sparse, sequential sensor selection. This paper also uses the sensor selection framework to design sensor arrays that can perform well for a collection of operating conditions. Flow estimation results on numerical data show that the proposed framework produces arrays that are highly effective at flowfield estimation for the flow behind and an airfoil at a high angle of attack using embedded pressure sensors. Analysis of the flowfields reveals that paths of impinging stagnation points along the airfoil’s surface during a shedding period of the flow are highly informative locations for placement of pressure sensors.

Online continual learning for human activity recognition

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.

Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Gait disabilities are among the most frequent impairments worldwide. Their treatment increasingly relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user’s recovery state and autonomy, while reducing the clinicians effort. For that, these should be able to decode human motion and needs, as early as possible. Current walkers decode motion intention using information gathered from wearable or embedded sensors, namely inertial units, force sensors, hall sensors, and lasers, whose main limitations imply an expensive solution or hinder the perception of human movement. Smart walkers commonly lack an advanced and seamless human–robot interaction, which intuitively and promptly understands human motions. A contactless approach is proposed in this work, addressing human motion decoding as an early action recognition/detection problematic, using RGB-D cameras. We studied different deep learning-based algorithms, organised in three different approaches, to process lower body RGB-D video sequences, recorded from an embedded camera of a smart walker, and classify them into 4 classes (stop, walk, turn right/left). A custom dataset involving 15 healthy participants walking with the walker device was acquired and prepared, resulting in 28800 balanced RGB-D frames, to train and evaluate the deep learning networks. The best results were attained by a convolutional neural network with a channel-wise attention mechanism, reaching accuracy values of 99.61% and above 93%, for offline early detection/recognition and trial simulations, respectively. Following the hypothesis that human lower body features encode prominent information, fostering a more robust prediction towards real-time applications, the algorithm focus was also quantitatively evaluated using Dice metric, leading to values slightly higher than 30%. Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.

Continuous remote monitoring of a motorway section using geophones

Pavement monitoring has been a widespread area of research. Monitoring pavements using embeddable sensors and a data acquisition system is an active research topic, with important questions about the selection of appropriate sensors and data acquisition systems, and interpretation of data. This work presents results of the instrumentation of a French motorway section, using geophones and temperature probes. The sensors are connected to a remote monitoring system which is compact, has a low power consumption, and real-time data processing capabilities. This system performs continuous monitoring, under a heavy motorway traffic and transmits the data wirelessly to a remote server. This work focuses on the treatment and analysis of the sensor signals. It indicates a good reliability of the system and embedded sensors, and presents different methods of interpretation of the data, to monitor pavement deflections, but also traffic composition and traffic loads, which could be used for pavement management, for detection of pavement damage and preventive maintenance.

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

AbstractAutomated spraying practices are inevitable for modern polyhouse management to attain a broader objective of minimizing human exposure to agrochemicals. In the present study, an automated mobile robotic sprayer (AMRS) was developed to combat the increased human intervention and safeguard agricultural workers from potential health hazards. The system mainly comprises embedded sensors (ultrasonic, proximity, XBee) and controllers (Arduino, PLC). The controller drives the system on a piping track between the rows as well as on the head space achieving end‐to‐end automation for spraying operations. The system performance was evaluated on the tomato crop with respect to the physiological traits, yield and economics. Additionally, the study leveraged response surface methodology to optimize forward speed, spray distance, and working pressure of AMRS on the responses, droplet density, coverage, volume mean diameter (VMD) and application rate. Optimization of forward speed (0.79 km/h), spray distance (250 mm) and working pressure (0.40 MPa) resulted in 90.7 droplets/cm2 droplet density, 47.1% coverage, 170.2 μm VMD and 86.0 mL/m2 of application rate. Ergonomic aspects of AMRS were assessed by the parameters, human exposure, discomfort and postural assessment with respect to knapsack sprayer. The working heart rate of 103 beats/min, work pulse of 12 beats/min, oxygen consumption rate of 916 mL/min and energy expenditure rate of 18.7 kJ/min recorded during the ergonomic evaluation of the AMRS were 25%, 75%, 42%, and 41% lower compared to manual spraying, respectively. Moreover, three–six times higher work pulse, cardiac cost, body part discomfort score and overall discomfort rating were observed which indicated more drudgery involved in manual spraying. State‐of‐the‐art system developed for polyhouses would minimize the drudgery and health hazards, significantly. The system's resilience and effectiveness pave the way for its wider deployment where the use of agrochemicals are prevalent, particularly in small‐size polyhouses.

A machine learning approach for detecting fatigue during repetitive physical tasks

Prolonged and repetitive stress on muscles, tendons, ligaments, and nerves can have long-term adverse effects on the human body. This can be exasperated while working if the environment and nature of the tasks puts significant strain on the body, which may lead to work-related musculoskeletal disorders (WMSDs). Workers with WMSDs can experience generalized pain, loss of muscle strength, and loss of ability to continue working. Most WMSDs injuries are caused by ergonomic risks, such as repetitive physical movements, awkward postures, inadequate recovery time, and muscular stress. Fatigue can be seen as a detector of ergonomic risk, as the accumulation of fatigue can significantly increase the possibility of injury. Thirty participants completed a series of repetitive physical tasks over a six-hour period while wearing sensors to capture data related to heart rate and movement, while external embedded sensors captured ground reaction and hand exertion force. They also provided subjective ratings of fatigue at the start and end of the experiment. Classifiers for fatigue (high vs low) were constructed using three methods: linear discriminant analysis (LDA), k-nearest neighbor (kNN), and polynomial kernel-based SVM (P-SVM) and were validated using a tenfold cross-validation technique that was repeated a hundred times. Results of our supervised machine learning approach demonstrated a maximum accuracy of 94.15% using P-SVM for the binary classification of fatigue.

Thermally stable multi-directional magnetoelectric based embedded magnetic sensor

Owing to the multifunctional behavior possessed by magnetoelectric (ME) composites, they are sought-after materials for various magnetic field sensing applications. This article proposes a three-directional ME-based embedded magnetic sensor that has been fabricated using the press-fit technique. The employed fabrication method negates the use of epoxy in the ME sensor, thus enabling its use at elevated temperatures in excess of 100 ∘C. The fabricated sensor is tested using an experimental setup capable of producing ac and dc bias magnetic fields in three coordinate directions. Experiments are performed for various dc magnetic field conditions, including x, y, z directions, their simultaneous combinations, and magnetic field aligned at an angle with the sensor. Under all testing conditions, the embedded sensor shows a significantly high output voltage response. Additionally, the effect of the magnetic field generated by the double magnet system and single magnet system on the sensor performance has also been demonstrated, wherein the embedded sensor is observed to be marginally affected by magnetic field due to the presence of only one magnet. Finally, quasi-static ME measurements are performed at elevated temperatures up to 100 ∘C, and it is observed that the novel embedded sensor has reliable sensing capabilities in aggravated thermal environments even in excess of 100 ∘C. Thus, the proposed three-directional embedded magnetic sensor offers reliable response under all conditions of magnetic field and temperature and can thus be a reliable alternative for the traditionally used layered-based counterparts.