Gravity Sensor Research Articles

Sub-Hz fundamental, sub-kHz integral linewidth self-injection locked 780 nm hybrid integrated laser

Today’s precision experiments for timekeeping, inertial sensing, and fundamental science place strict requirements on the spectral distribution of laser frequency noise. Rubidium-based experiments utilize table-top 780 nm laser systems for high-performance clocks, gravity sensors, and quantum gates. Wafer-scale integration of these lasers is critical for enabling systems-on-chip. Despite progress towards chip-scale 780 nm ultra-narrow linewidth lasers, achieving sub-Hz fundamental linewidth and sub-kHz integral linewidth has remained elusive. Here we report a hybrid integrated 780 nm self-injection locked laser with 0.74 Hz fundamental and 864 Hz integral linewidths and thermorefractive-noise-limited 100 Hz2/Hz at 10 kHz. These linewidths are over an order of magnitude lower than previous photonic-integrated 780 nm implementations. The laser consists of a Fabry-Pérot diode edge-coupled to an on-chip splitter and a tunable 90 million Q resonator realized in the CMOS foundry-compatible silicon nitride platform. We achieve 2 mW output power, 36 dB side mode suppression ratio, and a 2.5 GHz mode-hop-free tuning range. To demonstrate the potential for quantum atomic applications, we analyze the laser noise influence on sensitivity limits for atomic clocks, quantum gates, and atom interferometer gravimeters. This technology can be translated to other atomic wavelengths, enabling compact, ultra-low noise lasers for quantum sensing, computing, and metrology.

Robust Human Activity Recognition for Intelligent Transportation Systems Using Smartphone Sensors: A Position-Independent Approach

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Attitude estimation for an all-rotating monocopter through attitude decomposition and MARG Sensor fusion

A monocopter is a biology-inspired aircraft based on samara. Due to the constant high spin during flight, attitude estimation is highly challenging. The difficulty of attitude estimation is extracting the effective gravity acceleration information from the large centripetal acceleration interference. Based on the characteristics of monocopters, our study proposes a method to accomplish monocopter attitude estimation through onboard sensor fusion. Our method decomposes the body attitude angles into two-disc attitude angles and three relative attitude angles. Through five steps (a priori body attitude estimation, relative attitude calculation, gravity acceleration information extraction, body attitude estimation through data fusion, and disc attitude estimation), a low-cost attitude determination method relying only on magnetic, angular rate, and gravity sensors is achieved. Finally, the performance of our proposed attitude estimation method is demonstrated through tests on a rotational platform and three degrees of freedom virtual flight testing of a monocopter. The test results show that the attitude estimation method has high precision and the ability for practical applications.

High-performance silicon photonic single-sideband modulators for cold-atom interferometry.

The laser system is the most complex component of a light-pulse atom interferometer (LPAI), controlling frequencies and intensities of multiple laser beams to configure quantum gravity and inertial sensors. Its main functions include cold-atom generation, state preparation, state-selective detection, and generating a coherent two-photon process for the light-pulse sequence. To achieve substantial miniaturization and ruggedization, we integrate key laser system functions onto a photonic integrated circuit. Our study focuses on a high-performance silicon photonic suppressed-carrier single-sideband (SC-SSB) modulator at 1560 nanometers, capable of dynamic frequency shifting within the LPAI. By independently controlling radio frequency (RF) channels, we achieve 30-decibel carrier suppression and unprecedented 47.8-decibel sideband suppression at peak conversion efficiency of -6.846 decibels (20.7%). We investigate imbalances in both amplitudes and phases between the RF signals. Using this modulator, we demonstrate cold-atom generation, state-selective detection, and atom interferometer fringes to estimate gravitational acceleration, g ≈ 9.77 ± 0.01 meters per second squared, in a rubidium (87Rb) atom system.

An Android Sensors-Based Portable Road Health Monitoring System Utilizing Measurement Uncertainty Analysis

Abstract Road health monitoring systems aim to obtain the technical indexes of roads, and then analyze the usage and the degree of damage of the roads, which can provide an important basis for road construction, maintenance, and management. Road roughness is one of the main technical indexes for road quality evaluation and road health monitoring. This study built a system and implemented it as an application to measure and analyze road longitudinal profiles simply and conveniently using the sensors in a mobile phone. The application uses the accelerometer sensor and the gravity sensor to obtain vertical acceleration by a projection method, then denoises through empirical mode decompositions and a Butterworth filter, which has a repeated measurement error of 11%. Different filters were compared and the repeatability, accuracy, robustness, and effectiveness of the system were analyzed. An index to evaluated road longitudinal profiles is given, so that the results can be analyzed and viewed interactively in the application, and a series of cases are given in this paper.

Research on Overload Early Warning System for Railcars

Railcars are prone to suffer from overloading problems during its operation, which seriously affects the service life of railcars and rails, while posing a serious potential safety hazard. This paper In this paper, the weight of the car load is calculated by installation of a gravity sensor on the railcar and collection of the data from the gravity sensor through applying the A/D conversion module of the MCU. The overloading warning function of railcars is realized according to the drive alarm indicator of overloading information, and displaying the overloading information on the liquid crystal display screen.

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human-Computer Interaction.

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human-computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human-computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

Gap Junctions in Ctenophora.

Gap junction proteins form specialized intercellular communication channels, including electrical synapses, that regulate cellular metabolism and signaling. We present a molecular inventory of the gap junction proteins-innexins (INX-like) in ctenophores, focusing on two reference species, Pleurobrachia bachei and Mnemiopsis leidyi. Innexins were identified in more than 15 ctenophore species, including such genera as Euplokamis, Pukia, Hormiphora, Bolinopsis, Cestum, Ocyropsis, Dryodora, Beroe, benthic ctenophores, Coeloplana and Vallicula, and undescribed species of Mertensiidae. The observed diversity of innexins resulted from the independent expansion of this family from the common ancestor of ctenophores. Innexins show the conserved topology with four transmembrane domains connected by two extracellular loops, which bridge intracellular gaps. However, INX-like genes have highly diverse exon organization and low percentage identity for their amino acid sequences within the same species and between ctenophore species. Such a broad scope of molecular diversity differs from innexins in other phyla. We predicted posttranslational modifications in innexins: 249 and 188 for M. leidyi and P. bachei, respectively. Neither their number nor their locations were conserved within or between species. When the number of posttranslational modifications is factored into the innexins' radiation, the potential for molecular and physiological diversity within gap junctions of ctenophores is almost unfathomable. RNA-seq and in situ hybridization data revealed that innexins are expressed across embryogenesis, including early cleavage stages and gastrulation. They are abundant in all adult tissues, with the highest expression level in the aboral organ (the major integrative center and the gravity sensor in ctenophores), followed by tentacles and comb plates. Nevertheless, each organ and tissue has a unique combination of innexins, suggesting their involvement in complex integrative functions and behaviors of ctenophores.

The Simons Observatory: A fully remote controlled calibration system with a sparse wire grid for cosmic microwave background telescopes.

For cosmic microwave background (CMB) polarization observations, calibration of detector polarization angles is essential. We have developed a fully remote controlled calibration system with a sparse wire grid that reflects linearly polarized light along the wire direction. The new feature is a remote-controlled system for regular calibration, which has not been possible in sparse wire grid calibrators in past experiments. The remote control can be achieved by two electric linear actuators that load or unload the sparse wire grid into a position centered on the optical axis of a telescope between the calibration time and CMB observation. Furthermore, the sparse wire grid can be rotated by using a motor. A rotary encoder and a gravity sensor are installed on the sparse wire grid to monitor the wire direction. They allow us to achieve detector polarization angle calibration with an expected systematic error of 0.08°. The calibration system will be installed in small-aperture telescopes at Simons Observatory.

AI-ACCIDENT DETECTION SYSTEM FOR FAST RESCUE ON FREEWAYS USING ON-BOARD CAR MOTION SENSORS FUSION

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.

Twin-array capacitance sensor for multi-parameter measurement of gas-solid particle flow based on BP-Adaboost

The measurement of gas-solid particle flow is a complex and error-prone task due to the intricacies of the flow process. To address the limitations associated with Electrical Capacitance Tomography (ECT) such as the complexity of hardware structure and the computational demands of imaging algorithms, this study proposes a novel TAC measurement method that utilizes the BP-Adaboost algorithm. The proposed method utilizes TAC to measure multiple parameters of the gas-solid particle flow under several working conditions and calibrate the measured flow rate with gravity sensors. At first, we conduct experiments to identify the working conditions that have the least impact on the measurements of concentration and velocity, offering valuable insights for subsequent two-phase flow measurement. The BP-Adaboost algorithm is then utilized to classify the flow pattern based on capacitance data obtained from the TAC sensor. Finally, the pattern, concentration, and velocity are used as inputs for BP-Adaboost neural network to optimize the mass flow rate and improve the measuring accuracy. Experimental results demonstrate the effectiveness of the method, with an accuracy of 83.3% for two-phase flow pattern classification and a mass flow rate error of 6.71%. Additionally, BP-Adaboost algorithm, an ensemble learning technique, effectively avoids overfitting, leading to good generalizability.

Airbag Protection and Alerting System for Elderly People

In the elderly population, falling is an important cause of severe injuries like hip injuries. Injury- or unconsciousness-related immobility implies that the affected are unable to seek assistance. Building fall detection systems is essential since it is usual for senior individuals residing alone to go many hours without being located after a fall, considerably increasing the impact of injuries caused by falls. The primary goal of this paper is to implement an airbag protection and alerting system for elderly people. The system can be installed on a waist belt as an airbag. It is connected wirelessly to the elderly person’s mobile, where an Android mobile application is created to receive alert notifications from the system. The system will detect the fall using a gravity sensor that is connected to an Arduino board. If a fall is detected by the gravity sensor, then the system will activate an air valve and inflate the airbag from the air tank. The system will also send a warning notification to the elderly person’s mobile application via Bluetooth. Then, the elderly person’s GPS location will be determined from their mobile phone and an SMS will be transmitted to a mobile phone belonging to his/her emergency contact of choice. The system is tuned to provide a proper fall detection sensitivity with good accuracy (90%). It has a relatively low cost, very quick response time (only 150 ms delay from the electrical grid), and low power consumption (11.1 Wh).

Magneto-optical trapping in a near-suface borehole.

Borehole gravity sensing can be used in a number of applications to measure features around a well, including rock-type change mapping and determination of reservoir porosity. Quantum technology gravity sensors, based on atom interferometry, have the ability to offer increased survey speeds and reduced need for calibration. While surface sensors have been demonstrated in real world environments, significant improvements in robustness and reductions to radial size, weight, and power consumption are required for such devices to be deployed in boreholes. To realise the first step towards the deployment of cold atom-based sensors down boreholes, we demonstrate a borehole-deployable magneto-optical trap, the core package of many cold atom-based systems. The enclosure containing the magneto-optical trap itself had an outer radius of (60 ± 0.1) mm at its widest point and a length of (890 ± 5) mm. This system was used to generate atom clouds at 1 m intervals in a 14 cm wide, 50 m deep borehole, to simulate how in-borehole gravity surveys are performed. During the survey, the system generated, on average, clouds of (3.0 ± 0.1) × 105 87Rb atoms with the standard deviation in atom number across the survey observed to be as low as 8.9 × 104.

Review of Atom Chips for Absolute Gravity Sensors

As a powerful tool in scientific research and industrial technologies, the cold atom absolute gravity sensor (CAGS) based on cold atom interferometry has been proven to be the most promising new generation high-precision absolute gravity sensor. However, large size, heavy weight, and high–power consumption are still the main restriction factors of CAGS being applied for practical applications on mobile platforms. Combined with cold atom chips, it is possible to drastically reduce the complexity, weight, and size of CAGS. In this review, we started from the basic theory of atom chips to chart a clear development path to related technologies. Several related technologies including micro-magnetic traps, micro magneto–optical traps, material selection, fabrication, and packaging methods have been discussed. This review gives an overview of the current developments in a variety of cold atom chips, and some actual CAGS systems based on atom chips are also discussed. We summarize by listing some of the challenges and possible directions for further development in this area.

Simplification of calibration of low-cost MARG sensors without high-precision laboratory equipment

AbstractIn mechatronic-related applications, estimating orientation from a magnetic, angular rate, and gravity (MARG) sensor array is a significant topic. Representing attitude orientation is a well-known topic in the aerospace industry, where it plays a critical role in airplanes and unmanned aerial vehicles (UAVs), but it has also gained relevance in other sectors. However, most of the sensors utilized are quite expensive, heavy, and large, making them unsuitable for modest applications. This paper examines the performance of several sensors in low-cost hardware and high-acceleration environments. A theorical method was adopted to estimate Euler angles by using accelerometer, gyroscope and magnetometer, and a robust and easy to implement method calibration was proposed to calibrate the MARG sensor without any external equipment. An experimental verification of the proposed calibration method was completed. The experimental results are then interpreted to provide an insight to advantages and disadvantages for using each sensor separately.

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion.

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.

A Novel Orientation Determination Approach of Mobile Robot Using Inertial and Magnetic Sensors

The orientation information is critical for the mobile robot. However, the robot’s constrained locomotion, complicated magnetic field environment, and the coupled orientation estimation present challenges for using the microelectromechanical magnetometer in real-time robot orientation determination. This article focuses on calibrating the magnetometer in constrained robot locomotion and achieving the robot’s decoupled roll, pitch, and yaw estimates. A magnitude-free and reference-free calibration approach is proposed to calibrate the magnetometer using the measurements collected from the principal plane of the robot locomotion. Moreover, a decoupled orientation estimator is created under the framework of the extended Kalman filter using the calibrated triaxial magnetic, angular rate, and gravity sensor readings. Two independent experiments validate the effectiveness of magnetometer calibration and orientation decoupling, respectively. A case study of estimating the mobile robot’s orientation shows that the proposed methods can achieve accurate heading estimation and decouple the roll and pitch from the magnetic field data.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

Using the quantum properties of atoms to reveal what's underground

In the 300 years since Newton’s apple, gravity sensors have emerged as a useful tool to investigate the underground, looking for hazards and even mapping the distribution of water from space. Using the quantum properties of atoms, a new sensor has been made that overcomes several practical limitations of gravity survey, with the potential to speed up future surveys.