False Triggering Research Articles

Voice Activated Clap Switch using Arduino with AI Technology

The development of a voice-activated clap switch system that incorporates Artificial Intelligence (AI) and Arduino technology holds significant potential for advancing smart home automation, particularly for individuals with disabilities and the elderly. In India, 4.3% of the population has disabilities, and 10.5% are over the age of 60 (Ministry of Statistics and Programme Implementation, 2021), highlighting the need for accessible technologies that facilitate independent living. Traditional clap switches, which rely solely on sound intensity detection, are prone to false triggers and background noise interference, limiting their reliability and effectiveness (Smith et al., 2020). This paper presents an AI-enhanced clap switch system capable of recognizing specific sound patterns, enabling it to accurately distinguish claps from other noises while also supporting voice commands for improved control and accessibility. The methodology involves collecting sound data, training an AI model to identify claps and process voice commands, and deploying the model on an Arduino microcontroller that controls a relay for appliance operation. Experimental results show that the AI-powered system significantly reduces false activations in noisy environments, providing a reliable, energy-efficient, and hands-free solution for smart homes, particularly benefiting users who require enhanced accessibility in their living spaces. Keywords: Voice Command, Clap Switch, AI, Smart Home Automation, Arduino.

Perceived cognitive loss, symptomology, and psychological well-being with bipolar disorder

BackgroundAdults with bipolar disorder (BD) commonly present with cognitive deficits. Many also report subjective or perceived cognitive failures. For this study, we identified four distinct clusters of adults with BD on the basis of both BD symptoms (depression and hypo/mania) and perceived cognitive errors (i.e., forgetfulness, distractibility, false triggering). We hypothesized that participants reporting more BD symptoms and cognitive errors would report lower psychological well-being (i.e., self-efficacy, life scheme, life satisfaction). A second objective was to determine if and how clusters differed in terms of BD related factors (e.g., subtypes, sleep, medications) and sociodemographic differences such as age of participants. From the BADAS (Bipolar Affective Disorder and older Adults) Study, we identified 281adults with BD (M = 44.27 years of age, range 19–81), recruited via social media. ResultsAll clusters significantly differed across all grouping variables except symptoms of hypo/mania due to low frequency. Across clusters, perceived cognitive failures and BD symptoms increased in lockstep; that is, those reporting more cognitive errors also reported significantly higher symptoms of both depression and hypo/mania. As hypothesized, they also reported significantly lower psychological well-being.ConclusionsAge did not significantly differ across clusters in contrast to existing research in which cognitive loss is objectively measured. That is, perceived cognitive errors are significantly associated with lower psychological well-being for both young and older adults with BD.

Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis

Background and objectiveMotor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance. MethodsThe idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test. ResultsThe recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001). ConclusionsThe research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.

Wearable electro-optical sensor for monitoring of nitrogen dioxide gas in environment

Environmental monitoring for detection of harmful pollutants is a challenge in today’s world. Development of wearable sensors with cost-effectiveness and practicality, while using accessible electronic components can be a solution to this challenge. In the present work ultraviolet light emitting diodes (UV-LEDs) and a Light Dependent Resistor (LDR) pair with carefully designed programmable circuitry is used to detect Nitrogen Dioxide (NO2) in the environment. The sensor is battery operated and rechargeable, making it convenient and eco-friendly. The sensor developed is miniaturized and wearable in nature for the detection of analyte gas in the environment. The sensor is stable and inexpensive as compared to the existing NO2 sensors available, which are electro-chemical, or oxide based. The sensor system is sensitive and selective to its exposure to NO2 gas and has no interference with typical environmental attributes such as humidity and temperature, which is a common challenge in gas detection technologies. The principle of gas detection is absorption based, light falling on the detector absorbed by the test gas present in the environment, thus changing the detector resistance. Absorption-based gas detection is indigenously simple but highly effective technique. This method not only ensures good sensitivity but enhances the selectivity of the electro-optical sensor towards the test gas preventing false triggering in the presence of other interfering gases.

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, an all-sky gamma-ray monitor launched on 2020 December 10. We developed a multifunctional trigger and localization software operating in the CPU of the GECAM Electronic Box. This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for both short and long bursts, and low time latency of the trigger information which is downlinked through the Global Short Message Communication service of the global BeiDou navigation system. This paper provides a detailed description of the design and development of the trigger and localization software system for GECAM. It covers the general design, workflow, the main functions, and the algorithms used in the system. The paper also includes on-ground trigger tests using simulated gamma-ray bursts generated by a dedicated X-ray tube, as well as an overview of the performance for real celestial bursts during its in-orbit operation.

Data-driven design of a derailment detection system for freight wagons

Railway transportation is widely known for its high safety level. Nevertheless, derailments still occur, typically causing large service disruptions and economic losses. For these reasons, identifying derailments is of paramount importance for improving the railway transportation safety. This paper presents the design and testing of a derailment detection system for freight trains. A derailment detection algorithm able to process and analyze data in real-time has been designed leveraging the knowledge gained from full-scale derailment tests on freight wagons. Moreover, a LoRa-based wireless communication system has been implemented to relay freight wagon derailment warnings to the locomotive. The effectiveness of the proposed derailment detection algorithm is proved through its deployment to a freight wagon used for common operations. In that, it demonstrated increased robustness against derailment false triggering compared to traditional mechanic-pneumatic detectors. Furthermore, the proposed wagon-locomotive wireless communication system is proven effective in transmitting information across various convoy compositions and freight wagon loading conditions during operational testing on the railway line.

Fault diagnosis and analysis of automobile capacitive touch display false triggering

Abstract Nowadays, automobile handling is mainly concentrated in the central control display. The use of high-definition and large-size capacitive touch displays has become the mainstream trend. Driving information and entertainment information are displayed on it, and the operation of the electronic control function of the car is generally realized by touching the touch panel of the central control display. As the integration of the central control display becomes higher, the fault of the display needs to be paid more attention to. The common faults include display faults and touch faults. The display fault will affect the display of vehicle information, and the driver cannot obtain vehicle status information; the touch fault will affect the car operation and can not carry out human-machine interaction. Therefore, once the central control display fails, it will affect the safety driving of the car. This paper studies the false triggering failure which is a kind of touch fault of the vehicle’s 9-inch capacitive touch display. Firstly, we test the parasitic capacitance of faulty parts and repeat the touch failure. After testing and analyzing, it has been found that the mechanical structure design of the display and the air gap bonding process can change the value of parasitic capacitance which leads to false triggering. This paper proposes solutions to solve this failure which are changing the wideness of glue to avoid glue overflow and strengthening size management to improve the spacing between the touch panel and the TFT display screen. After the implementation of the solutions, the false triggering failure no longer occurs, which proves that the solutions are effective and feasible.

A large-aperture, high-power ultrawideband radiation system with beam broadening capacity.

Due to the limited maximum output power of the pulsers based on avalanche transistors, high-power ultrawideband (UWB) radiation systems usually synthesize plenty of modules simultaneously to achieve a high peak effective potential (rEp). However, this would lead to an increased aperture size as well as a narrower beam, which would limit their applications in intentional electromagnetic interference fields. In this paper, a high-power UWB radiation system with beam broadening capacity is developed. To achieve beam broadening in the time domain, a power-law time delay distribution method is proposed and studied by simulation, and then the relative excitation time delays of the modules are optimized to achieve higher rEp and avoid beam splitting in the beam broadening mode. In order to avoid false triggering of the pulser elements when implementing the beam broadening, the mutual coupling effect in the system is analyzed and suppressed by employing onboard high-pass filters, since the mutual coupling effect is much more severe in the low-frequency range. Finally, a radiation system with 36 modules is developed. Measuring results indicate that in the high-rEp mode, the developed system could achieve a maximum effective potential rEp of 313.6kV and a maximum pulse-repetition-rate of 20kHz. In the beam broadening mode, its half-peak-power beam width in the H-plane is broadened from the original value of 3.9° to 7.9°, with a maximum rEp of 272.9kV. The polarization direction of the system could be flexibly adjusted by a built-in motor.

A false trigger pulse elimination circuit for low-frequency energy harvesting active rectifiers

Abstract In this article, a false trigger pulse elimination circuit is proposed to be used for a low-frequency energy harvesting active rectifier. It is divided into two-stage circuits, each stage almost including a switching signal generation circuit, a switching inverter, and an inverter to eliminate false triggering pulses on the rising edge and falling edge respectively. There is an edge detection circuit in each switching signal generation circuit, generating a switching signal that controls the switching inverter’s on-off behavior. This allows the inverter to achieve inverter output voltage clamping by turning off its inverter function and turning it into a switch that is only turned on at the high or low level. At the same time, a thyristor delay element is used to determine and adjust the effective level time of the switching signal, which can reach over 150 μs, completely covering the rising or falling edge false trigger pulse so that no GΩ-level resistors are needed in the low-frequency field. The results show that the low-frequency false trigger pulse elimination circuit proposed in this article can better eliminate false trigger pulses on both rising and falling edges. It can also achieve on-chip integration, reduce the power consumption of the self-adaptive delay compensation loop, and enhance the stability of the circuit.

Fuzzy models for cyber incident detection in military information and communication systems databases

Protecting databases of military information and communication systems is an extremely important task in the modern cybersecurity sphere. Growing threats from cyberattacks, the need to effectively detect, counteract and prevent them require the use of new, more effective models and methods. The main disadvantages of existing models and methods include insufficient sensitivity to new threats, a large number of detection errors, low response to new threats, the possibility of bypassing protective measures, and low scalability, which are key challenges for further improvement and development of cybersecurity. The article analyzes the existing fuzzy models for detecting cyber incidents, identifies their shortcomings and emphasizes the need for their further improvement and development. An improved fuzzy model for detecting cyber incidents in databases of military information and communication systems and an improved fuzzy model for detecting cyber incidents in databases of military information and communication systems with weighted rules based on the expansion of cyber incident signs by obtaining them from different levels of cyber security of the data are proposed. The main levels of database cybersecurity include: the operating system level, the network level, and the database management system level. To eliminate the shortcomings associated with the false triggering of cyber incident detection rules and the complexity of their configuration in a dynamically changing cyberattack landscape, as well as the dimensionality of the knowledge base of the information and security event management system, a fuzzy model for detecting cyber incidents in databases of military information and communication systems with weights of rule antecedents is proposed. The expediency of applying the developed model is shown.

Deep Learning Approach for Gait Detection for Precise Stimulation of FES to Correct Foot Drop

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient’s foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system.

Study of SVOM/ECLAIRs inhomogeneities in the detection plane below 8 keV and their mitigation for the trigger performances

Context. The Space-based multi-band astronomical Variable Objects Monitor is a Chinese-French mission dedicated to the study of the transient sky. It is scheduled to start operations in 2024. ECLAIRs is a coded-mask telescope with a large field of view. It is designed to detect and localize gamma-ray bursts in the energy range from 4 keV up to 120 keV. In 2021, the ECLAIRs telescope underwent various calibration campaigns in vacuum test-chambers to evaluate its performance. Between 4 and 8 keV, the counting response of the detection plane shows inhomogeneities between pixels from different production batches. The efficiency inhomogeneity is caused by low-efficiency pixels (LEPs) from one of the two batches, together with high-threshold pixels (HTPs) whose threshold was raised to avoid cross-talk effects. In addition, some unexpected noise was found in the detection plane regions close to the heat pipes. Aims. We study the impact of these inhomogeneities and of the heat-pipe noise at low energies on the ECLAIRs onboard triggers. We propose different strategies in order to mitigate these impacts and to improve the onboard trigger performance. Methods. We analyzed the data from the calibration campaigns and performed simulations with the ground model of the ECLAIRs trigger software in order to design and evaluate the different strategies. Most of the impact of HTPs can be corrected for by excluding HTPs from the trigger processing. To correct for the impact of LEPs, an efficiency correction in the shadowgram seems to be a good solution. An effective solution for the heat-pipe noise is selecting the noisy pixels and ignoring their data in the 4–8 keV band during the data analysis. Results. The trigger threshold is the minimum value of the signal-to-noise ratio that is required to claim that ECLAIRs has detected a candidate event that is not related to a background fluctuation. After introducing the efficiency inhomogeneity in the imaging simulation, the trigger threshold in the 4–8 keV band increased by a factor of 5.75 times and 1.43 times due to the impact of HTPs and LEPs, respectively, in the worst case (on a timescale of about 20 min). The trigger threshold value was restored to its normal value after we applied an efficiency-correction method. Introducing the heat-pipe noise in our simulations in the worst case (timescale of about 20 min) resulted in an increase in the trigger threshold of approximately 100% in the 4–8 keV band compared to observations without heat-pipe noise. Moreover, even with this increased threshold, we estimated a false-trigger rate of 99.26% in the 4–8 keV band and 4.44% in the 4-120 keV band. By accepting a loss of 2.5-5% noisy pixels in the 4–8 keV energy band, we can prevent false triggers caused by heat-pipe noise and reduce the threshold increment to about 20% for the longest timescale (about 20 min) of the ECLAIRs trigger in the 4–8 keV range.

Highly Efficient Self-checking Matrix Multiplication on Tiled AMX Accelerators

General Matrix Multiplication (GEMM) is a computationally expensive operation that is used in many applications such as machine learning. Hardware accelerators are increasingly popular for speeding up GEMM computation, with Tiled Matrix Multiplication (TMUL) in recent Intel processors being an example. Unfortunately, the TMUL hardware is susceptible to errors, necessitating online error detection. The Algorithm-based Error Detection (ABED) technique is a powerful technique to detect errors in matrix multiplications. In this article, we consider implementation of an ABED technique that integrates seamlessly with the TMUL hardware to minimize performance overhead. Unfortunately, rounding errors introduced by floating-point operations do not allow a straightforward implementation of ABED in TMUL. Previously an error bound was considered for addressing rounding errors in ABED. If the error detection threshold is set too low, it will a trigger false alarm, while a loose bound will allow errors to escape detection. In this article, we propose an adaptive error threshold that takes into account the TMUL input values to address the problem of false triggers and error escapes and provide a taxonomy of various error classes. This threshold is obtained from theoretical error analysis but is not easy to implement in hardware. Consequently, we relax the threshold such that it can be easily computed in hardware. While ABED ensures error-free computation, it does not guarantee full coverage of all hardware faults. To address this problem, we propose an algorithmic pattern generation technique to ensure full coverage for all hardware faults. To evaluate the benefits of our proposed solution, we conducted fault injection experiments and show that our approach does not produce any false alarms or detection escapes for observable errors. We conducted additional fault injection experiments on a Deep Neural Network (DNN) model and find that if a fault is not detected, it does not cause any misclassification.

ПРИМЕНЕНИЕ БОЛЬШОЙ ЯЗЫКОВОЙ МОДЕЛИ ДЛЯ УМЕНЬШЕНИЯ ЛОЖНОПОЗИТИВНЫХ СРАБАТЫВАНИЙ В ЗАДАЧАХ ВЫЯВЛЕНИЯ АНОМАЛИЙ В СЕТЕВОМ ТРАФИКЕ

Every year, network threats become more sophisticated and complex, which requires researchers in the field of network security to seek and develop new and more advanced methods for detec¬ting security threats. Despite the fact that constant research is being conducted in this area and researchers are improving machine learning algorithms, false positive triggers of intrusion detection systems remain a significant problem. In this regard, the development of methods and approaches to reduce the number of false positive positives is one of the most urgent tasks. Aim. The purpose of the study is to study the effectiveness and possibilities of using large language models to reduce false positive responses of intrusion detection systems. Materials and methods. The main focus of the article is on training recurrent neural networks to detect anomalies using a training sample of CIS-IDS2017 network traffic. Special attention was paid to algorithms for selecting key values in the training sample to improve the accuracy of the training model. The paper examines the architecture of recurrent networks, as well as their advantages and disadvantages in the specifics of the task being solved. Further research was conducted using a large language model, as a result of which a comparison was made of the number of false positives with and without this solution. Results. A basic neural network model based on the LSTM algorithm for the initial classification of network threats was built, and a large language model was trained. A comparative analysis of the results of anomaly detection with and without a large linguistic model is carried out. Experiments confirm the effectiveness of the proposed solution. Conclusion. The obtained research results can be used in the deve¬lopment of new, modern intelligent intrusion detection systems to improve the accuracy of threat detection or increase the effectiveness of existing intrusion detection algorithms.

Mechanism Analysis and Oscillation Suppression of the False Triggering Oscillation for Parallel-Connected GaN Devices

Mechanism Analysis and Oscillation Suppression of the False Triggering Oscillation for Parallel-Connected GaN Devices

Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers

Objective. In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain–computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential-based BCIs. Approach. The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis. Oscillatory features are then extracted using the spectral power of fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory, aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools by Bonferroni corrected p-values from two-way analysis of variance. Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state. Main results. On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features. Significance. Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.

Fault- Tolerant DC-DC Converter with Zero Interruption Time Using Capacitor Health Prognosis

A high-end critical electronic system is expected to have hundreds of electronic subsystems, which rely on the Power Management Unit (PMU) to be energized. Having an efficient PMU is crucial and it requires reliable and well-structured voltage buck converters to translate the supplied voltage levels. The buck converters employed in PMU are expected to be fault tolerant and supply uninterrupted power while serving critical subsystems. Active redundant parallel buck converters employed in PMU to achieve fault tolerance increases overhead in terms of area, cost and power dissipation. In this paper, a DC-DC converter is designed for the PMU by combining two legs of buck converters with an effective output of 3.3 V. A simple yet effective technique is proposed to design a fault-tolerant buck DC-DC converter by bypassing a faulty converter leg. The proposed system utilizes an online signal processing-based method for prognostic fault detection. Ripple content in the voltage of the output Aluminum Electrolytic Capacitor (AEC) is monitored and used as a primary health indicator for the primary buck converter leg. Increase in the output ripple due to degradation is used for the prognosis of primary converter failure. The secondary buck converter leg is activated only upon the confirmed prognosis of a faulty primary converter leg to avoid false triggering. The timely prognosis of primary converter failure and activation of secondary converter facilitates uninterrupted power supply. An experimental setup is built and tested in the laboratory. Experimental results indicate a smooth transition from the primary converter leg to the secondary demonstrating an uninterrupted power supply along with the simplicity and effectiveness of the proposed solution

Concurrent-Validity and Reliability of Photocells in Sport: A Systematic Review.

Specific physical qualities such as sprint running, change-of-direction or jump height are determinants of sports performance. Photocell systems are practical and easy to use systems to assess the time from point A to point B. In addition, these photoelectric systems are also used to obtain the time of vertically displaced movements. Knowing the accuracy and precision of photocell timing can be a determinant of ensuring a higher quality interpretation of results and of selecting the most appropriate devices for specific objectives. This systematic review aimed to identify and summarize studies that have examined the validity and reliability of photocells in sport sciences. A systematic review of PubMed, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 164 studies initially identified, 16 were fully reviewed, and their outcome measures were extracted and analyzed. Photocells appear to have a strong agreement with force plates (gold standard), but are not interchangeable to measure the vertical jump. For monitoring horizontal displacement, double beam systems, compared to single beam systems, are more valid and reliable when it comes to avoiding false triggers caused by swinging arms or legs.

Design of a Low-Cost, Self-Adaptive and MRI-Compatible Cardiac Gating System.

Cardiac gating, synchronizing medical scans with cardiac activity, is widely used to make quantitative measurements of physiological events and to obtain high-quality scans free of pulsatile artefacts. This can provide important information for disease diagnosis, targeted control of medical microrobots, etc. The current work proposes a low-cost, self-adaptive, MRI-compatible cardiac gating system. The system and its processing algorithm, based on the monitoring and analysis of blood pressure waveforms, are proposed. The system is tested in an in vitro experiment and two living pigs using four-dimensional (4D) flow magnetic resonance imaging (MRI) and two-dimensional phase-contrast (2D-PC) sequences. in vitro and in vivo experiments reveal that the proposed system can provide stable cardiac synchronicity, has good MRI compatibility, and can cope with the fringe magnetic field of the MRI scanner, radiofrequency signals during image acquisition, and heart rate changes. High-resolution 4D flow imaging is successfully acquired both in vivo and in vitro. The difference between the 2D and 4D measurements is ≤ 21%. The incidence of false triggers is 0% in all tests, which is unattainable for other known cardiac gating methods. The system has good MRI compatibility and can provide a stable and accurate trigger signal based on pressure waveform. It opens the door to applications where the previous gating methods were difficult to implement or not applicable.

Anxiety related distractibility deficits: too much smartphone use is not such a smart call

ABSTRACT Smartphones can increase productivity and ease accessing information, however the possible negative implications for high smartphone use or problematic smartphone use (PSU) are not fully understood. The current study examined anxiety-linked memory and attention deficits to determine whether PSU moderates these relationships. Cross-sectional data from 506 young adults aged 18–29 years (68% female) were analysed in separate regression models to investigate whether PSU (Mobile Phone Problem Use scale) moderated the relationship between state and trait anxiety (State-Cognitive and Trait-Cognitive subscales of State Trait Inventory for Cognitive and Somatic Anxiety) and everyday memory and attentional failures (the False Triggering, Forgetfulness and Distractibility subscales of the Cognitive Failures Questionnaire). Our results showed that PSU moderated the combined influence of state and trait anxiety for distractibility, such that those who reported higher PSU and higher trait anxiety reported greater errors of distractibility during higher, but not lower state anxiety. However, our predictions for false triggering and forgetfulness were not supported; the only significant finding was a trend for higher trait anxiety to be related to increased failures of false triggering and forgetfulness. Real-world implications of findings are discussed.