Detection Alarm Research Articles

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

As an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments.

Research on optimizing the scope of impact considering decomposition products in the event of a nitric acid spill chemical accident

As the size of the chemical industry increases, chemical accidents continue to occur as the handling volume of chemicals also increases. Currently, in the case of a chemical accident, the prediction of the scope of influence mainly analyzes the scope of the impact on a single substance in the accident and does not consider the scope of the decomposition and reaction products. Nitric acid, one of the many chemical accidents, produces nitrogen dioxide, which is harmful when decomposed. In this research, we at first assumed an outflow accident of nitric acid. After conducting our research using the Chemical Accident Response Information System (CARIS), we discovered that the impact range and breakdown of nitric acid were comparable to that of nitrogen dioxide. Furthermore, we were able to identify the potential ramifications associated with such occurrences. In this study, the impact scenarios were evaluated according to the worst-case scenario, the alternative scenario, the size of the leak hole diameter, atmospheric temperature, atmospheric wind speed, and atmospheric stability. The results showed that the end point concentration distance of nitrogen in ERPG-1, 2 and 3 was larger than nitric acid in most conditions, with differences of up to 5.45 times in the worst-case scenario, 5.96 times in the alternative scenario, up to 6.952 times in the leak hole diameter scenario, up to 10 times in the atmospheric temperature scenario, up to 13 times in the wind speed-specific scenario, and up to 4 times in the atmospheric stability scenario. In the event of a nitric acid spill chemical accident, the response agency should set up a boundary area at a greater distance in consideration of the impact of nitrogen dioxide when setting up a boundary area such as the Hot zone and the Warm zone. Nitric acid handling sites should install additional detectors for nitrogen dioxide, which is decomposition products, to monitor the diffusion of decomposition products into the atmosphere when installing detection alarm equipment in preparation for leakage accidents. It is believed that the government should continuously secure and study data on other decomposable chemicals besides nitric acid to provide material information and disaster prevention methods to chemical accident response agencies and communities for effective and early treatment of chemical accidents.

Automated cardiac arrest detection using a photoplethysmography wristband: algorithm performance in relation to sex, skin color type and arm hair density

Abstract Background While survival from witnessed out-of-hospital cardiac arrest (OHCA) has improved, this is not the case for unwitnessed cardiac arrest as the event goes unnoticed. Automated cardiac arrest detection and alerting through a wristband is considered a promising technique also facilitating early assistance for this large group. In the DETECT-1 study, a photoplethysmography(PPG) algorithm for circulatory arrest detection was developed in patients with induced circulatory arrest. However, patient characteristics, such as skin color and arm hair density, can influence the accuracy of the PPG signal. Thus, we performed further analyses to study the effect of patient characteristics on the algorithm’s performance. Purpose To assess the performance of the developed PPG algorithm for cardiac arrest detection in relation to sex, skin color type and arm hair density. Methods Patients who underwent short-lasting circulatory arrest induction during transcatheter aortic valve implantation, ventricular tachycardia ablation or subcutaneous ICD implantation wore a PPG wristband during the procedure. Continuous ECG and invasive blood pressure recordings served as reference standards. A PPG algorithm for circulatory arrest detection was developed. The algorithm’s performance was assessed in subgroups based on sex, skin color type (according to the Fitzpatrick scale), and arm hair density. The sensitivity for the detection of circulatory arrest and false positive alarms were compared between the different subgroups. Results In total, 151 patients were included in the current analysis. Analyzed data consisted of 159 hours of PPG data including 176 induced circulatory arrest events. The population consisted of 57 (38%) females and 94 (62%) males. The sensitivity for circulatory arrest detection was 100% in the female group vs. 98% in the male group (p=ns); false positive alarms occurred in 4 females and 9 males, resulting in a positive predictive value (PPV) of 94% and 92% (p=ns), respectively. Within the skin color subgroup analysis, 114 (75%) patients had skin color type I or II, and 37 (25%) patients had a skin color type III, IV, V or VI. There were no differences in sensitivity (p=ns) and PPV (p=ns) in the skin color subgroup (table 1). The population consisted of 106 (71%) patients with nil or sparse arm hair density, 43 (29%) patients with moderate or dense arm hair density, and 2 not reported. There were no differences in sensitivity (p=ns) and PPV (p=ns) within the arm hair density subgroup (table 1). Conclusions Automated cardiac arrest detection using a smartwatch is feasible and its accuracy is not affected by sex, skin color type or arm hair density in this study. Further study is needed to assess performance of the algorithm in a larger sample size and a more diverse study population. If proven to be effective, automated cardiac arrest detection has the potential to improve survival from OHCA.Sensitivity/PPV for arrest detection

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

Alarm management approach for supervision of Green Hydrogen Plants

Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs.

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

Intrusion detection systems for the internet of things: a probabilistic anomaly detection approach

The expansion of IoT domains presents new cybersecurity challenges. In this paper, we propose an enhanced Intrusion Detection System (IDS) designed to tackle these emerging security issues. Our approach harnesses the power of unsupervised learning techniques, particularly the Variational Autoencoder (VAE), to reduce data dimensionality while preserving essential features. By integrating advanced clustering algorithms–including K-means, Gaussian Mixture Model (GMM), and Variational Bayesian Gaussian Mixture Model (VBGMM)–we facilitate precise data categorization within IoT networks. Our conducted experiments using the IoT Network Intrusion Dataset demonstrate that VAE-based methods consistently outperform traditional Autoencoders (AE), achieving superior performance across critical metrics such as False Alarm Rate (FAR), Detection Rate (DR), Accuracy, Precision, and Area Under the ROC Curve (AUC).

Fusion neutron source and array of particle detectors for nondestructive interrogation of special nuclear materials

Presented herein are the outcomes of an experimental test involving a pioneering portable-active interrogation system designed for the nondestructive detection of special nuclear materials (SNMs). The system relies on the threshold energy neutron analysis concept and incorporates a portable deuterium–deuterium (DD) neutron generator producing a particle intensity of 5 × 107 n/s, coupled with three arrays of tensioned metastable fluid detectors (TMFDs) to detect secondary neutrons from the fissile material. In the presence of the fissile material, prompt fission neutrons are emitted, with an average energy of approximately 2 MeV, and around 30% of these neutrons have energies above that of the DD neutron source (2.45 MeV). The detection of a statistically significant neutron population exceeding this threshold firmly indicates the presence of SNM. TMFDs exhibit high sensitivity in efficiently detecting neutrons above the threshold while adeptly discriminating against neutrons below the threshold as well as gamma rays. This unique feature allows the interrogation system to maintain a lightweight profile without necessitating substantial shielding materials. The validation experiments involved the placement of 70 or 140 g masses of U-235 within a 1 m3 inspection volume. Measurements were carried out over 30 min intervals, repeated numerous times, both with and without U-235, at a DD neutron source intensity of 8 × 105 n/sec. Experimental count rates with natural uranium (NU) are consistently above those without NU. The probability of detection (PD) and probability of false alarm (PFA) were assessed utilizing these count rates. The DD neutron source intensity and inspection time were normalized at 5 × 107 n/sec and 90 s, respectively. The results indicated a PD of approximately 74% and 98% for detecting 70 and 140 g of U-235, respectively, with a PFA of <5%. These promising outcomes align with the specified PD (>90%) and PFA (<5%) targets outlined in ANSI standards.

Development of the Internet of Things (IoT) in early detection of emergencies using postpartum alert

Background: IoT-based postpartum alert is the development of a vital sign-checking tool using obstetric early warning system indicators. This is an effort to prevent maternal death during the postpartum period. Monitoring is carried out manually without interpretation and recommendations and is not connected to the Internet. Adding interpretations and recommendations and connecting to the Internet can help mothers and families with self-examination and remote monitoring by midwives or health workers because, generally, mothers are at home during the postpartum period.Purpose: This research aims to create and test an IoT-based Postpartum alert detection tool that has been developed. Methods: This research is R&D, divided into five stages: collecting information through literature study, product/model design, design validation, design revision, and small-scale product/model testing.Results: An IoT-based early postpartum detection tool with a portable design has been created. The test results are based on the Receiver Operating Characteristic (ROC) classification in the area under the ROC curve, which shows that the tool has an outstanding category with an accuracy level of 90%.Conclusion: The IoT-based postpartum emergency detection alarm is a vital sign measuring tool that can interpret and recommend signs and connect to health workers using internet features. The measurement and interpretation results are sent to the cloud system to provide recommendations, data storage, and notification alarms to health workers.

TLDM: An Enhanced Traffic Light Detection Model Based on YOLOv5

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.

Exploiting Cascaded Channel Signature for PHY-Layer Authentication in RIS-Enabled UAV Communication Systems

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes.

Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure. [1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224. [2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.

Assessing the accuracy and reliability of satellite-derived precipitation products in the Kosi River basin (India).

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.

DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires

Wildfires usually lead to a large amount of property damage and threaten life safety. Image recognition for fire detection is now an important tool for intelligent fire protection, and the advancement of deep learning technologies has enabled an increasing number of cameras to possess functionalities for fire detection and automatic alarm triggering. To address the inaccuracies in extracting texture and positional information during intelligent fire recognition, we have developed a novel network called DCP-Net based on UNet, which excels at capturing flame features across multiple scales. We conducted experiments using the Corsican Fire Dataset produced by the “Environmental Science UMR CNRS 6134 SPE” laboratory at the University of Corsica and the BoWFire Dataset by Chino et al. Our algorithm was compared with networks such as SegNet, UNet, UNet++, and PSPNet, demonstrating superior performance across three metrics: mIoU, F1-score, and OA. Our proposed deep learning model achieves the best mIoU (78.9%), F1-score (76.1%), and OA (96.7%). These results underscore the robustness of our algorithm, which accurately identifies complex flames, thereby making a significant contribution to intelligent fire recognition. Therefore, the proposed DCP-Net model offers a viable solution to the challenges of wildfire monitoring using cameras, with hardware and software requirements typical of deep learning setups.

Design of anti-theft alarm and fire detection system based on IoT cloud platform

This time for a STM 32 single chip computer as the core of the intelligent home system of anti-theft alarm system for a detailed design, It aims to provide a comprehensive understanding of the hardware composition, software design and implementation principles of the smart home system, And analyze its advantages and disadvantages, Thus better providing intelligent early warning for human beings, Prevent the emergence of emergencies, Saving the lives and property of the people, The security information of the user can be monitored remotely, It can also turn it off via a button, In the anti-theft mode, You can also use an infrared sensor module installed in the human body to detect whether there is anyone in the home, So as to achieve the purpose of fire and fire prevention and theft. On this basis, a residential intelligent management system based on wireless communication is designed. The OLED LCD screen can display the currently detected temperature and the density of the flue gas, and use the button to set an upper alarm limit. When the detected temperature or smoke content exceeds the upper limit, the LED indicator light will light up and issue an alarm. Also, there is an emergency button to send out a distress signal at any time.

TRANSLIENT: Detecting Transients Resulting from Point-source Motion or Astrometric Errors

Detection of moving sources over a complicated background is important for several reasons. First is measuring the astrophysical motion of the source. Second is that such motion resulting from atmospheric scintillation, color refraction, or astrophysical reasons is a major source of false alarms for image-subtraction methods. We extend the Zackay, Ofek, and Gal-Yam image-subtraction formalism to deal with moving sources. The new method, named the translient (translational transient) detector, applies hypothesis testing between the hypothesis that the source is stationary and that the source is moving. It can be used to detect source motion or to distinguish between stellar variability and motion. For moving source detection, we show the superiority of translient over the proper image subtraction, using the improvement in the receiver-operating characteristic curve. We show that in the small translation limit, translient is an optimal detector of point-source motion in any direction. Furthermore, it is numerically stable, fast to calculate, and presented in a closed form. Efficient transient detection requires both the proper image-subtraction statistics and the translient statistics: When the translient statistic is higher, then the subtraction residual is likely due to motion. We test our algorithm both on simulated data and on real images obtained by the Large Array Survey Telescope. We demonstrate the ability of translient to distinguish between motion and variability, which has the potential to reduce the number of false alarms in transients detection. We provide the translient implementation in Python and MATLAB.