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.

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

Self-Powered Implantable Skin-Like Glucometer for Real-Time Detection of Blood Glucose Level In Vivo

Implantable bioelectronics for analyzing physiological biomarkers has recently been recognized as a promising technique in medical treatment or diagnostics. In this study, we developed a self-powered implantable skinlike glucometer for real-time detection of blood glucose level in vivo. Based on the piezo-enzymatic-reaction coupling effect of GOx@ZnO nanowire, the device under an applied deformation can actively output piezoelectric signal containing the glucose-detecting information. No external electricity power source or battery is needed for this device, and the outputting piezoelectric voltage acts as both the biosensing signal and electricity power. A practical application of the skin-like glucometer implanted in mouse body for detecting blood glucose level has been simply demonstrated. These results provide a new technique path for diabetes prophylaxis and treatment.

Established and Emerging Optical Technologies for the Real-Time Detection of Cervical Neoplasia: A Review

Cervical cancer remains a critically important problem for women, especially those women in the developing world where the case-fatality rate is high. There are an estimated 528,000 cases and 266,000 deaths worldwide. Established screening and detection programs in the developed world have lowered the mortality from 40/100,000 to 2/100,000 over the last 60 years. The standard of care has been and continues to be: a screening Papanicolaou smear with or without Human Papilloma Virus (HPV) testing;followed by colposcopy and biopsies and if the smear is abnormal;and followed by treatment if the biopsies show high grade disease (cervical intraepithelial neoplasia (CIN) grades 2 and 3 and Carcinoma-in-situ). Low grade lesions (Pap smears with Atypical Cells of Uncertain Significance (ASCUS), Low Grade Squamous Intraepithelial Lesions (LGSIL), biopsies showing HPV changes or showing CIN 1);are usually followed for two years and then treated if persistent. Treatment can be performed with loop excision, LASER, or cryotherapy. Loop excision yields a specimen which can be reviewed to establish the diagnosis more accurately. LASER vaporizes the lesion and cryotherapy leads to tissue destruction. Under long term study;loop excision, LASER, and cryotherapy have the same rate of cure. The standard of care is expensive and takes 6 - 12 weeks for the individual patient. During the last twenty years, new technologies that can view the cervix and even image the cervix with cellular resolution have been developed. These technologies could lead to a new paradigm in which diagnosis and treatment occurs at a single visit. These technologies include fluorescence and reflectance spectroscopy (probe or wide-field, whole cervix scanning approaches) and fluorescence confocal endomicroscopy or high resolution micro-endoscopy. Both technologies have received Federal Drug Administration (FDA) and have been commercialized. Research trials continue to show their remarkable performance. These technologies are reviewed and clinical trials are summarized. Emerging technologies are coming along that may compete with those already approved and include optical coherence tomography, optical coherence tomography with autofluorescence, diffuse optical microscopy, and dual mode micro-endoscopy. These technologies are also reviewed and where available, clinical data is reported. Optical technologies are ready to diffuse into clinical practice because they will save money and 3 or 4 visits in the developed world and offer the same standard of care to the developing world where more cervical cancer exists.

Real-time detection of moving objects in video sequences

An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.

Real-Time Detection of Unstable Control Loop Behavior in a Feedback Active Noise Cancellation System for In-Ear Headphones

Active noise controls are used in a wide field of applications to cancel out unwanted surrounding noise. Control systems based on the feedback structure however have the disadvantage that they may become unstable during run-time due to changes in the control path—in this context including the listener’s ear. Especially when applied to active noise cancellation (ANC) headphones, the risk of instability is associated with the risk of harmful influence on the listener’s ear, which is exposed to the speaker in striking distance. This paper discusses several methods to enable the analysis of a feedback ANC system during run-time to immediately detect instability. Finally, a solution is proposed, which identifies the open loop behavior parametrically by means of an adaptive filter to subsequently evaluate the coefficients regarding stability.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

Instantaneous Real-Time Detection Technology of GLI on FY-4 Geostationary Meteorological Satellite

Lightning is a typical example of an instantaneous random point source target. It has close connection with severe convective phenomena such as a thunderstorm, whose distribution, variation, position and forecasting can be acquired through lightning observation. In this paper, we discuss the way to achieve instantaneous lightning signal intensification and detection from geostationary orbit by using the differences between the lightning signal and the slowly changing background noise such as that of cloud, land and ocean, combining three methods, spectral filtering, spatial filtering and background noise, enabling removal between frames. After six months of operation in orbit, lightning within the coverage of the Geostationary Lightning Imager was effectively detected, strongly supporting the case for shorttime and real-time early warning, forecasting and tracking of severe convective phenomena in China.

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

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.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

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.

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.

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.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

A multiplex real-time PCR assay for simultaneous detection of classical swine fever virus,African swine fever virus,and atypical porcine pestivirus

With the implementation of the C-strain vaccine,classical swine fever(CSF) has been under control in China,which is currently in a chronic atypical epidemic situation.African swine fever(ASF) emerged in China in 2018 and spread quickly across the country.It is presently occurring sporadically due to the lack of commercial vaccines and farmers’ increased awareness of biosafety.Atypical porcine pestivirus(APPV) was first detected in Guangdong Province,China,in 2016,which mainly harms piglets and has a local epidemic situation in southern China.These three diseases have similar clinical symptoms in pig herds,which cause considerable losses to the pig industry.They are difficult to be distinguished only by clinical diagnosis.Therefore,developing an early and accurate simultaneous detection and differential diagnosis of the diseases induced by these viruses is essential.In this study,three pairs of specific primers and Taq-man probes were designed from highly conserved genomic regions of CSFV(5’ UTR),African swine fever virus(ASFV)(B646L),and APPV(5’ UTR),followed by the optimization of reaction conditions to establish a multiplex real-time PCR detection assay.The results showed that the method did not cross-react with other swine pathogens(porcine circovirus type 2(PCV2),porcine reproductive and respiratory syndrome virus(PRRSV),foot-and-mouth disease virus(FMDV),pseudorabies virus(PRV),porcine parvovirus(PPV),and bovine viral diarrhea virus BVDV).The sensitivity results showed that CSFV,ASFV,and APPV could be detected as low as 1 copy μL–1;the repeatability results showed that the intra-assay and interassay coefficient of variation of ASFV,CSFV,and APPV was less than 1%.Twenty-two virus samples were detected by the multiplex real-time PCR,compared with national standard diagnostic and patented method assay for CSF(GB/T 27540–2011),ASF(GB/T 18648–2020),and APPV(CN108611442A),respectively.The sensitivity of this triple real-time PCR for CSFV,ASFV,and APPV was almost the same,and the compliance results were the same(100%).A total of 451 clinical samples were detected,and the results showed that the positive rates of CSFV,ASFV,and APPV were 0.22% (1/451),1.3%(6/451),and 0%(0/451),respectively.This assay provides a valuale tool for rapid detection and accurate diagnosis of CSFV,ASFV,and APPV.

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area.One of the simplest and most efficient algorithms,the artificial potential field algorithm(APF),may provide real-time navigation in those places but fall into local mini-mum in some cases.To overcome this problem and to present alternative escape routes for a robot,possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm.This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows,doors,and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm.The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging(LIDAR)sensors to navigate through an unknown closed space and reach a desired goal point.Inclusion of crossing points allows the robot to escape from areas where it is con-gested.The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods.The results showed that the improved APF methods and the methods rein-forced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room.For the goals outside the current room,traditional APF methods were quite unsuccessful in reaching the goals.Even though improved methods were able to reach some outside targets,the proposed method gave approximately 17%better results than the most success-ful example in achieving targets outside the current room.The proposed method can also work in real-time to discover a building and navigate between rooms.

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.

A systematic review of real-time detection and classification of power quality disturbances

This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances(PQDs).A particular focus is given to voltage sags and notches,as voltage sags cause huge economic losses while research on voltage notches is still very incipient.A systematic method based on scientometrics,text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature.A biblio-metric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time,top publishing countries,and the distribution by relevant topics.A set of articles is subsequently selected to be critically analyzed.The critical review is structured in steps for real-time detection and classification of PQDs,namely,input data preparation,preprocessing,transformation,feature extraction,feature selec-tion,detection,classification,and characterization.Aspects associated with the type of disturbance(s)addressed in the literature are also explored throughout the review,including the perspectives of those studies aimed at multiple PQDs,or specifically focused on voltage sags or voltage notches.The real-time performance of the reviewed tools is also examined.Finally,unsolved issues are discussed,and prospects are highlighted.