A Novel Approach to Improve the Detectability of CO_2 by GC Analysis

A novel stochastic resonance algorithm was employed to enhance the signal-to-noise ratio (SNR) of signals of analytical chemistry. By using a gas chromatographic data set, it was proven that the SNR was greatly improved and the quantitative relationship between concentrations and chromatographic responses remained simultaneously. The linear range was extended beyond the instrumental detection limit.

Effect of Cut-Off Frequency of Butterworth Filter on Detectability and Contrast of Hot and Cold Regions in Tc-99m SPECT

In SPECT, noise is one of the major limitations that degrade image quality. To suppress the noisy signals in an image, digital filters are most commonly applied. However, in SPECT image reconstruction, selection of an appropriate filter and its functions has always remained a difficult task. In this work an attempt was made to investigate the effects of varying cut-off frequencies and in keeping the order of Butterworth filter constant on detectability and contrast of hot and cold re-gions images. A new insert simulating hot and cold regions which provides similar views in a reconstructed image was placed in the phantom’s cylindrical source tank and imaged. Tc-99m radionuclide was distributed uniformly in the phantom. SPECT data were collected in a 20% energy window centered at 140 keV by a Philips ADAC Forte dual head gamma camera mounted with a LEHR collimator. Images were generated by using the filtered backprojection technique. A Butterworth filter of order 5 with cut-off frequencies 0.35 and 0.45 cycles·cm-1 was applied. Images were examined in terms of hot and cold regions, detectability and contrast. Results show that the hot and cold regions’ detectability and contrast vary with the change of cut-off frequency. With a 0.45 cycles·cm-1 cut-off frequency, a significant enhancement in contrast of cold regions was achieved as compared to a 0.35 cycles·cm-1 cut-off frequency. Furthermore, the detectability of hot and cold regions improved with the use of a 0.45 cycles·cm-1 cut-off frequency. In conclusion, image quality of hot and cold regions affected in a different way with a change of cut-off frequency. Thus, care should be taken in selecting the filter cut-off frequency prior to reconstruction of images;particularly, when both types of regions are expected in the reconstructed image.

Improving Detectability of Resistive Shorts in FPGA Interconnects

The behavior of resistive short defects in FPGA interconnects is investigated through simulation and theoretical analysis.The results show that these defects result in timing failures and even Boolean faults for small defect resistance values.The best detection situations for large resistance defect happen when the path under test makes a v-to-v′ transition and another path causing short faults remains at value v.Small defects can be detected easily through static analysis.Under the best test situations,the effects of supply voltage and temperature on test results are evaluated.The results verify that lower voltage helps to improve detectability.If short material has positive temperature coefficient,low temperature is better;otherwise,high temperature is better.

Detectability and reliability analysis of the local seismic network in Pakistan

The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.

Stabilizability,Observability and Detectability for Discrete Stochastic Systems

This paper mainly discusses stabilizatbility, exact observability and exact detectability of discrete stochastic systems with both static and control dependent noise via the spectrum technique. The authors put forward a definition of the spectrum and give some theorems based on the spectrum. Then the relation between discrete generalized Lyapunov equation and discrete generalized algebraic Riccati equation is also analyzed.

Evaluation of Long-Period Detectability of Teleseismic Events at Syowa Station, Antarctica

Phase identification procedures for teleseismic events at Syowa Station (69.0°S, 39.6°E;SYO), East Antarctica have been carried out since 1967 after the International Geophysical Year (IGY;1957-1958). Since the development of INTELSAT telecommunication link, digital waveform data have been transmitted to the National Institute of Polar Research (NIPR) for the utilization of phase identification. Arrival times of teleseismic phases, P, PKP, PP, S, SKS have been detected manually and reported to the International Seismological Centre (ISC), and published by “JARE Data Reports” from NIPR. In this paper, hypocentral distribution and time variations for detected earthquakes are demonstrated over the last four decades in 1967-2010. Characteristics of detected events, magnitude dependency, spatial distributions, seasonal variations, together with classification by focal depth are investigated. Besides the natural increase in the occurrence of teleseismic events on the globe, a technical advance in the observing system and station infrastructure, as well as the improvement of procedures for reading seismic phases, could all combine to produce the increase in detection of events in last few decades. Variations in teleseismic detectability for longer terms may be possible by association with the meteorological environment and seaice spreading area around the Antarctic continent. Recorded teleseismic and local seismic signals have sufficient quality for many analyses on dynamics and structure of the Earth as viewed from Antarctica. The continuously recorded data are applied not only to lithospheric studies but also to the Earth’s deep interiors, as a significant contribution to the Federation of Digital Seismological Networks (FDSN) from high southern latitude.

Analysis on advanced transient EM detectability of coal mine roadway

The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to detect water damage in advance of roadway excavation.In this paper,the time-domain finite element algorithm based on unstructured tetrahedron grids is used to accurate-ly simulate the geological body in front of the roadway excavation face and analyze its response.The authors detect the distance between the roadway excavation face and the low-resistivity water-bearing body,the resistivity difference between the low-resistivity body and surrounding rock,and the influence of the size of the low-resistivity body on the transient EM response.Furthermore,the common types of low-resistivity bodies in the roadway drivage process are used for modeling to analyze the attenuation of the detected EM response when there are low-resistivity bodies in front of the roadway.The research in this paper can help effectively detecting the water-bearing low-resistivity body in front of the roadway drivage and lay a foundation for reducing the risk of water seepage accidents.

ON THE OBSERVABILITY AND DETECTABILITY OF LINEAR STOCHASTIC SYSTEMS WITH MARKOV JUMPS AND MULTIPLICATIVE NOISE

This paper presents the notions of exact observability and exact detectability for Markov jump linear stochastic systems of Ito type with multiplieative noise （for short, MJLSS）. Stochastic Popov-Belevith-Hautus （PBH） Criterions for exact observability and exact detectability are respectively obtained. As an application, stochastic H2/H∞ control for such MJLSS is discussed under exact detectability.

On Observability and Detectability of Continuous-Time Stochastic Markov Jump Systems

This paper mainly studies observability and detectability for continuous-time stochastic Markov jump systems.Two concepts called W-observability and W-detectability for such systems are introduced,which are shown to coincide with various notions of observability and detectability reported recently in literature,such as exact observability,exact detectability and detectability.Besides,by introducing an accumulated energy function,some efficient criteria and interesting properties for both W-observability and W-detectability are obtained.

Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography

Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.

Detectability of the global weed Hypochaeris radicata is influenced by species, environment and observer characteristics

Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conducted time-limited repeat sur-veys of H.radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east australia.single-species single-season site-occupancy modelling was used to determine if detectability of H.radicata was altered by five covariates,observer,litter height,grazing,maximum plant height and flowering state.Important Findings Detectability for H.radicata varied significantly with observer,litter height,plant maximum height and flowering state,but not with graz-ing.Despite significant observer-specific variation,there was a con-sistent increase in detectability with plant height and when plants are in flower for all observers.Detectability generally decreased as litter height increases.Perfect or constant detection rates cannot be assumed in plant surveys,even for easily recognizable plants in simple survey conditions.understanding how detectability is influ-enced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data,or by determining optimal sur-vey conditions to increase the reliability of collected data.For plants with traits similar to H.radicata,surveying when most plants are at maximum height or in flower,increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors.We speculate that detection rates may be lower,more variable and involve additional covariates when surveying during the peak flow-ering spring season with the presence of more warm season and taller annual species.

The effects of seed detectability and seed traits on hoarding preference of two rodent species

Seed traits play an important role in affecting seed preference and hoarding behaviors of small rodents.Despite greatly affected by seed traits,seed detectability of competitors represents pilfering risks and may also modify seed hoarding preference of animals.However,whether seed traits and seed detectability show consistent effects on seed hoarding preference of animals remain largely unknown.Here,we explored how seed traits and seed detectability correlate with seed hoarding preference of Leopoldamys edwardsi and Apodemus chevrieri in a subtropical forest.Despite the effects of seed coat thickness and caloric value on hoarding preference of L.edwardsi,we detected no significant effects of other seed traits on hording preference of the 2 rodent species.There was no correlation between larder-hoarding preference and inter-or intra-specific seed detectability of L.edwardsi;however,seed detectability of L.edwardsi was negatively correlated with its own scatter-hoarding preference.Although scatter-hoarding preference of A.chevrieri was not correlated with inter-and intra-specific seed detectability,larder-hoarding preference of A.chevrieri was positively correlated with intra-specific seed detectability.Our study may provide evidence that intra-specific seed detectability rather than seed traits and inter-specific pilfering risks play an important role in modifying seed hoarding preference of rodents.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

A Robust Security Detection Strategy for Next Generation IoT Networks

Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

GFRF R-CNN:Object Detection Algorithm for Transmission Lines

To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.

Enhancing Deepfake Detection:Proactive Forensics Techniques Using Digital Watermarking

With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.

Amperometric Glucose Sensor Based on Batch CuO-Modified Screen-Printed Silver-Carbon Electrodes

CuO nanoparticles were successfully synthesized via a two-jet electrospun method,and then screen-printed on silver-carbon electrodes,forming CuO-modified Ag-C(CuO/Ag-C)disposable strip electrodes.In natural environment condition for glucose detection,the obtained CuO/Ag-C electrodes show a high sensitivity of 540 nA·mM^(-1)·cm^(-2),and a low limit of detection(0.68 mM)in a wide linear response range of 0.68 mM and 3 mM(signal/noise=3),respectively.In addition,the CuO/Ag-C electrodes also exhibit excellent anti-interference,air stability and repeatability.As a result,the fabrication of CuO nanoparticles via an electrospun process and the technique of screen-printed electrodes are of great significance for glucose detection.

Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing systems.However,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also challenging.Herein,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic deformations.The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse waves.The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase.Additionally,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%accuracy.The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

A Survey of Link Failure Detection and Recovery in Software-Defined Networks

Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.