Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Isomeric fluorescence sensors for wide range detection of ionizing radiations

In order to achieve a wider range of ionizing radiations detection,novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduced electron transfer(PET)effect.Two perylene diimide isomers PDI-P and PDI-B were designed and synthesized,and their molecular structures were characterized by high-resolution Fourier transform mass spectrometry(HRMS),nuclear magnetic resonance hydrogen and carbon spectroscopy(~1H and~(13)C NMR).The interaction between ionizing radiation and fluorescent molecules was simulated by HCl titration.The results show that combining PDIs and HCl can improve fluorescence through the retro-PET process.Despite the similarities in chemical structures,the fluorescent enhancement multiple of PDI-B with aromatic amine as electron donor is much higher than that of PDI-P with alkyl amine.In the direct irradiation experiments of ionizing radiation,the emission enhancement multiples of PDI-P and PDI-B are 2.01 and 45.4,respectively.Furthermore,density functional theory(DFT)and time-dependent density functional theory(TDDFT)calculations indicate that the HOMO and HOMO-1 energy ranges of PDI-P and PDI-B are 0.54 e V and 1.13 e V,respectively.A wider energy range has a stronger driving force on electrons,which is conducive to fluorescence quenching.Both femtosecond transient absorption spectroscopy(fs-TAS)and transient fluorescence spectroscopy(TFS)tests show that PDI-B has shorter charge separation lifetime and higher electron transfer rate constant.Although both isomers can significantly reduce LOD during PET process,PDI-B with aromatic amine has a wider detection range of 0.118—240 Gy due to its larger emission enhancement,which is a leap of three orders of magnitude.It breaks through the detection range of gamma radiation reported in existing studies,and provides theoretical support for the further study of sensitive and effective new materials for ionizing radiation detection.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Real-time Fluorescence PCR Method for Detection of Burkholderia glumae from Rice

Burkholderia glumae causing seedling rot and grain rot of rice was listed as a plant quarantine disease of China in 2007. It＇s quite necessary to set up effective detection methods for the pathogen to manage further dispersal of this disease. The present study combined the real-time PCR method with classical PCR to increase the detecting efficiency, and to develop an accurate, rapid and sensitive method to detect the pathogen in the seed quarantine for effective management of the disease. The results showed that all the tested strains of B. glumae produced about 139 bp specific fragments by the real-time PCR and the general PCR methods, while others showed negative PCR result. The bacteria could be detected at the concentrations of 1×10^4 CFU/mL by general PCR method and at the concentrations below 100 CFU/mL by real-time fluorescence PCR method. B. glumae could be detected when the inoculated and healthy seeds were mixed with a proportion of 1：100.

Detection of Ratoon Stunting Disease in Virus-free Seedcane via Real-time Fluorescence Quantitative PCR

This study was to develop the real-time fluorescence quantitative PCR technique for detecting the ratoon stunting disease （RSD） in virus-free seedcane seedlings. Healthy tissue culture seedlings were obtained from six plants of sugarcane ROC22, which had been confirmed RSD-positive by detecting the sugarcane juice, by employing the sugarcane seedlings production protocol. Real-time fluorescence quantitative PCR was used to detect RSD pathogens in tissue culture sam- pies. The results showed that target fragment of RSD pathogens was not found in all 10 samples in real-time fluorescence quantitative PCR, with the Ct values of 37 - 39. The healthy tissue culture sugarcane seedlings do not carry RSD pathogens, indicating that adopting healthy seedcane seedlings production technique could thoroughly get rid of RSD pathogens.

Detection of Survivin mRNA in nasopharyngeal carcinoma by real-time fluorescence quantitative RT-PCR

Objective： To establish the method of real time fluorescence quantitative RT-PCR for detecting the expression of Survivin mRNA in nasopharyngeat carcinoma （NPC） tissues. Methods： The total RNA was extracted from NPC cell line CNE-2 and tissues with Trizol and then been transcribed reversely to cDNA, a method of real time fluorescence quantitative RT-PCR for detecting the expression of Survivin mRNA in NPC tissues had been established, in which chronic nasopharyn-gitis patients＇ nasopharynx tissues treated as control group. Results： The expression of Survivin mRNA all could be detected either in CNE-2 cells, NPC tissues or in chronic nasopharyngitis patients＇ nasopharynx tissues, and there was higher the expression level of Survivin mRNA in NPC tissues than which in chronic nasopharyngitis patients＇ nasopharynx tissues, the difference was significant （P 〈 0.01）. The expression of Survivin mRNA could be detected both in stage Ⅰ ＋ Ⅱ and stage Ⅲ ＋ Ⅳ NPC, and there was no significant difference in relative quantifications of gene expression between these two groups （P 〉 0.05）. There was no relationship between Survivin mRNA expression and age and sex of NPC patients （P 〉 0.05）. Conclusion： Real time fluorescence quantitative RT-PCR is a rapid, effective and high sensitive method for detecting the expression of Survivin mRNA in NPC tissues. The overexpression of Survivin mRNA may play some roles in pathogenesis of NPC.

Detection and clinical significance of multidrug resistance-1 mRNA in bone marrow cells in children with acute lymphoblastic leukemia by real-time fluorescence quantitative RT-PCR

Objective： Multidrug resistance（MDR） is one of the most important reasons for treatment failure and recurrence of acute leukemia. Its manifestations are different in children with acute lymphoblastic leukemia（ALL） which may be due to different detection methods. This study was to detect the expression of MDR1 mRNA in bone marrow cells of children with ALL by real-time fluorescence- quantitative reverse transcription polymerase-chain reaction（FQ-RT-PCR）, and combine minimal residual desease（MRD） detection by flow cytometry（FCM） and to study their relationship with treatment response and prognosis of ALL. Methods：The MDR1 mRNA levels in bone marrow cells from 67 children with ALL[28 had newly diagnosed disease, 27 had achieved complete remission（CR）, 12 recurrent] and 22 children without leukemia were detected by FQ-RT-PCR. MRD was detected by FCM. The patients were observed for 9-101 months, with a median of 64 months. Results：Standard curves of human MDR1 and GAPDH genes were constructed successfully. MDR1 mRNA was detected in all children with a positive rate of 100%. The mRNA level of MDR1 was similar among the newly diagnosed ALL group, CR group, and control group（P 〉 0.05）, but significantly higher in the recurrence group than that in newly diagnosed disease group and control group（0.50 ± 0.55 vs. 0.09 ± 0.26 and 0.12 ± 0.23, P〈 0.05）. 54 ALL patients were followed up, and it was found that MDR1 mRNA level was significantly higher in ALL patients within 3 years duration than that of ALL patients with 3-6 years and over 6 years duration（0.63 ± 0.56 vs. 0.11 ± 0.12 and 0.04 ± 0.06, P〈 0.01）. For the 28 children with newly diagnosed disease, the MDR1 mRNA level was similar between WBC 〉 50 ~ 109 group and WBC〈50 × 10^9 group（P〉 0.05）. In the 33 CR patients, the MDR1 mRNA level was significantly higher in MRD〉10a group than that in MRD〈10a group（0.39 ± 0.47 vs. 0.03 ± 0.03, P 〈 0.05）. Conclusion：The sensitivity and specificity of FQ-RT-PCR in detecting MDR1 mRNA in bone marrowy cells of children with ALL patients are high. MDR1 mRNA is expressed in children with and without leukemia. MDR1 mRNA is highly expressed in the CR ALL patients with high MRD, recurrence and short duration（within 3 years）. Monitoring MRD and the MDR1 mRNA level might be helpful for individual treatment.

Synchronous Detection of DNA/RNA of Four Shrimp Viruses by Real-time Fluorescence Quantitative RT-PCR

[ Objective] This study aimed to establish a simultaneous detection method of shrimp viruses by real-time fluorescence quantitative RT-PCR, to improve the efficiency of inspection and quarantine. [ Method] A novel real-time fluorescence quantitative RT-PCR assay was established and optimized for simultaneously detecting DNA/RNA of four shrimp viruses （WSSV, IHHNV, TSV and YHV ）. [ Result] The optimized real-time fluorescence quantitative RT-PCR system gener- ated typical amplification curves with high amplification efficiencies （E = 1.06, 1.07, 0.92 and 0.92, respectively）, good hnear relationship （ r = 1 ）, uniform repeatability （ standard deviation = 0.05 - 0.46 ; variation coefficient = 0.26% - 1.62% ） and high sensitivity, exhibiting no significant differences compared with re- al-time fluorescence quantitative PCR （average error of Ct value = 0.04 -0.40; T = 0.53 -2.50; P 〉 0.05 ）. The total detection time was about 1 h. [ Conclusion] The optimized real-time fluorescence quantitative RT-PCR system can be used for rapid detection of WSSV, IHHNV, TSV and YHV.

Real-time Fluorescence Reverse-transcription Loop-mediated Isothermal Amplification for Detection of Porcine Epidemic Diarrhea Virus

Porcine epidemic diarrhea,a highly contagious enteric infectious disease caused by the porcine epidemic diarrhea virus(PEDV)with symptoms of vomit,diarrhea,loss of appetite of suckling pig,has led to serious economic loss to the global swine industry.In this study,a real-time fluorescence reverse transcription loop-mediated isothermal amplification(RT-LAMP)assay was developed to detect PEDV RNA.The real-time fluorescence RT-LAMP assay was performed at62℃for 60 min,using a simple and portable device,the ESE-Quant Tube Scanner.The detection limit of RNA was 2.9×10^(6) copies/μl,10 times as sensitive as RT-PCR,and the detection was specific only to PEDV.Application of this method to clinical samples yielded a positivity rate of 93%,which was higher than that of RT-PCR.This technique saves time and is efficient,and is thus expected to be useful for the diagnosis of PEDV infection in the field.

Simple synthesis of silver nanocluster composites AgNCs@PE-g-PAA by irradiation method and fluorescence detection of Cr^(3+)

Silver nanoclusters(AgNCs)are a new type of nanomaterials with similar properties to molecules and unique applications.The applications of AgNCs can be significantly expanded by combining them with different matrix materials to obtain AgNC composites.Using irradiation techniques,we developed a simple two-step method for preparing silver nanocluster composites.First,polyacrylic acid(PAA)chains were grafted onto the surface of a PE film as templates(PE-g-PAA).Subsequently,silver ions were reduced in situ on the surface of the template material to obtain the AgNC composites(AgNCs@PE-g-PAA).The degree of AgNC loading on the composite film was easily controlled by adjusting the reaction conditions.The loaded AgNCs were anchored to the carboxyl groups of the PAA and wrapped in the graft chain.The particle size of the AgNCs was only 4.38±0.85 nm,with a very uniform particle size distribution.The AgNCs@PE-g-PAA exhibited fluorescence characteristics derived from the AgNCs.The fluorescence of the AgNCs@PE-g-PAA was easily quenched by Cr^(3+)ions.The composite can be used as a fluorescence test paper to realize visual detection of Cr^(3+).

LDA-ID:An LDA-Based Framework for Real-Time Network Intrusion Detection

Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.

Establishment and Application of the TaqMan Real-Time Fluorescence Quantitative PCR Detection Assay for Koi Herpes Virus

[ Objective ] This study aimed to establish a rapid and effective quarantine method of Koi herpes virus. [ Method] Primers and corresponding TaqMan probe were designed based on the conserved sequence of Koi herpes virus （KHV） pol-ymerase gene （Sph） to establish a rapid and effective fluorescence quantitative PCR method for Koi herpes virus detection. The cell cultures were detected by using the established fluorescence quantitative PCR assay, and the results were com- pared with that of conventional PCR. [ Result] The sensitivity of fluorescence quantitative PCR was higher than that of conventional PCR. The minimum copy num- ber that could be detected was 1.6 - 102 copies/p.1. The established method was adopted for sample detection, and a reliable diagnostic result could be obtained within 4 h. [Conclusion] The established method is rapid, sensitive, specific and repeatable, which is conducive to the rapid detection of Koi herpes virus. Key words Koi herpes virus; Fluorescence quantitative PCR; Detection

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this work.The addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine discomfort.This novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on it.Additionally,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted trees.The proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in Simulink.The results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing works.The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap,nondestructive,and easy to use,gas sensors play important roles in the food industry.However,most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing.Also,an ideal electronic nose(E-nose)in a cold chain should be stable to its surroundings and remain highly accurate and portable.In this work,a portable film bulk acoustic resonator(FBAR)-based E-nose was built for real-time measurement of banana shelf time.The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone,and by introducing an air-tight FBAR as a reference,the E-nose can avoid most of the drift caused by surroundings.With the help of porous layer by layer(LBL)coating of the FBAR,the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate,while the detection range is large enough to cover a relative humidity of 0.8.In this regard,the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state,thereby showing the possibility of real-time shelf time detection.This portable FBAR-based E-nose has a large testing scale,high sensitivity,good humidity tolerance,and low frequency drift to its surroundings,thereby meeting the needs of cold-chain usage.

A mini review and hypothesis for coronavirus detection using photonics: surface enhanced Raman scattering and fluorescence resonance energy transfer

COVID-19 has devastated numerous nations around the world and has overburdened numerous healthcare systems,which has also caused the loss of livelihoods due to prolonged shutdowns and further led to a cascading effect on the global economy.COVID-19 infections have an incubation period of 2–7 days,but 40 to 45%of cases are asymptomatic or show mild to moderate respiratory symptoms after the period due to subclinical lung abnormalities,making it more likely to spread the pandemic disease.To restrict the spread of the virus,on-site diagnosis methods that are quicker,more precise,and easily accessible are required.Rapid Antigen Detection Tests and Polymerase Chain Reaction tests are currently the primary methods used to determine the presence of COVID-19 viruses.These tests are typically time-consuming,not accurate,and,more importantly,not available to everyone.Hence,in this review and hypothesis,we proposed equipment that employs the properties of photonics to improve the detection of COVID-19 viruses by taking the advantage of typical binding of coronavirus with angiotensin-converting enzyme 2(ACE2)receptors.This hypothetical model would combine Surface-Enhanced Raman Scattering(SERS)and Fluorescence Resonance Energy Transfer(FRET)to provide great flexibility,high sensitivities,and enhanced accessibility.