Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance levels.Conventional methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging instrumentation.The envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these challenges.To enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image contrast.Subsequently,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent axes.The utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic variances.Advanced computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition potential.Empirical validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced advancements.The refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original imagery.Mean Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX outcomes.The envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of luminosity.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning

Single atomic catalysts(SACs),especially metal-nitrogen doped carbon(M-NC)catalysts,have been extensively explored for the electrochemical oxygen reduction reaction(ORR),owing to their high activity and atomic utilization efficiency.However,there is still a lack of systematic screening and optimization of local structures surrounding active centers of SACs for ORR as the local coordination has an essential impact on their electronic structures and catalytic performance.Herein,we systematic study the ORR catalytic performance of M-NC SACs with different central metals and environmental atoms in the first and second coordination sphere by using density functional theory(DFT)calculation and machine learning(ML).The geometric and electronic informed overpotential model(GEIOM)based on random forest algorithm showed the highest accuracy,and its R^(2) and root mean square errors(RMSE)were 0.96 and 0.21,respectively.30 potential high-performance catalysts were screened out by GEIOM,and the RMSE of the predicted result was only 0.12 V.This work not only helps us fast screen high-performance catalysts,but also provides a low-cost way to improve the accuracy of ML models.

Detection with High Resolution Radar: Advanced Topics & Potential Applications

In a previous companion paper [1], the potential advantages of high resolution radar for improved target detection were introduced. In particular, the concept of shaping both the transmitted waveform and the receiving processor in accordance to the expected target down-range profile was highlighted and performance predictions were provided. In this paper, we present and evaluate an adaptive scheme devised to on-line estimate the target profile, in order to overcome a limited a-priori knowledge. In addition, we introduce a more general model of target impulse response, based on a statistical description, and we discuss the corresponding processing scheme and detection performance.

A DETECTION METHOD OF HIGH RESOLUTION RADAR TARGETS BASED ON POSITION CORRELATION

This paper studies a detection method of targets of high resolution radar operating at the band of millimeter-wave(32-38GHz) under the background of the clutters, and proposes a new nonparametric detection method, which not only does less computation, but also is able to detect multiple extended targets radially distributed along distance "corridor", based on the position (range) correlation information of one-dimensional range images(or called range profiles) of high resolution radar targets. The experimental results, on the real echo data of tank illuminated by the millimeter-wave stepped frequency high resolution radar, have certified that such a method presented in this paper is a very effective detection method for multiple extended targets.

Current molecular methods for the detection of hepatitis C virus in high risk group population:A systematic review

Hepatitis C virus(HCV) is an emerging infection worldwide and the numbers of persons infected are increasing every year. Poor blood transfusion methods along with unsafe injection practices are potential sources for the rapid spread of infection. Early detection of HCV is the need of the hour especially in high riskgroup population as these individuals are severely immunocompromised. Enzyme Immunoassays are the most common detection techniques but they provide no evidence of active viremia or identification of infected individuals in the antibody-negative phase and their efficacy is limited in individuals within high risk group population. Molecular virological techniques have an important role in detecting active infection with utmost specificity and sensitivity. Technologies for assessment of HCV antibody and RNA levels have improved remarkably, as well as our understanding of how to best use these tests in patient management. This review aims to give an overview of the different serological and molecular methods employed in detecting HCV infection used nowadays. Additionally, the review gives an insight in the new molecular techniques that are being developed to improve the detection techniques particularly in High Risk Group population who are severely immunocompromised.

Design and Research of High-throughput Automation System for Food Testing Laboratory

In the process of food testing,human operation is an important variable affecting the experimental results.In order to reasonably avoid the influence of human subjective operation behavior on the accuracy of detection results,the laboratory information management system was used as the information platform to design a high-throughput laboratory automation pre-treatment system based on the deep integration of mechanical principles,visual analysis,high-speed conduction,intelligent storage and other technical systems.The experimental results showed that the system could shorten the sample circulation cycle,effectively improve the laboratory biosafety,and meet the requirements of high-throughput processing of samples.

High-Throughput Growth of Hexagonal Boron Nitride Film Using Porous-Structure Isolation Layer

Chemical vapor deposition is considered as the most hopeful method for the synthesis of large-area high-quality hexagonal boron nitride on the substrate of catalytic metal. However, the size the hexagonal boron nitride films are limited to the size of growth chamber, which indicates a lower production efficiency. In this paper, the utilization efficiency of growth chamber is highly improved by alternately stacking multiple pieces of Cu foils and carbon fiber surface felt with porous structure. Uniform and continuous hexagonal boron nitride films are prepared on Cu foils through chemical vapor deposition utilizing ammonia borane as the precursor. This work develops a simple and practicable method for high-throughput preparation of hexagonal boron nitride films, which could contribute to the industrial application of hexagonal boron nitride. .

Design of Raptor-Like LDPC Codes and High Throughput Decoder Towards 100 Gbit/s Throughput

This paper proposes a raptor-like low-density parity-check(RL-LDPC)code design together with the corresponding decoder hardware architecture aiming at next-generation mobile communication.A new kind of protograph different from the 5G new radio(NR)LDPC basic matrix is presented,and a code construction algorithm is proposed to improve the error-correcting performance.A multi-core layered decoder architecture that supports up to 100 Gbit/s throughput is designed based on the special protograph structure.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

Rapid Determination of Dopamine and Its Metabolites During in vivo Cerebral Microdialysis by Routine High Performance Liquid Chromatography With Electrochemical Detection

To determine dopamine and its metabolites during in vivo cerebral microdialysis by routine high performance liquid chromatography with electrochemical detection. Methods Microdialysis probes were placed into the right striatum of Wistar rat brains and perfused with Ringer＇s solution at a rate of 1.5 pL/min. A reverse phase HPLC with electrochemistry was used to assay DA, DOPAC, and HVA after cerebral microdialysates were collected every 20 minutes from awake and freely moving rats. In order to identify the reliability of this method, its selectivity, linear range, precision and accuracy were tested and the contents of DA, DOPAC, and HVA in rat microdialysates were determined. Results The standard curve was in good linear at the concentration ranging from 74 nmol/L to 1.5 pmol/L for DOPAC （r^2= 0.9996）, from 66 nmol/L to 1.3 gmol/L for DA （r^2=l.0000） and from 69 nmol/L to 1.4 pmol/L for HVA （r^2=0.9992）. The recovery of DOPAC （0.30, 0.77, 1.49 gmol/L）, DA （0,26, 0.69, 1.32 gmol/L）, and HVA （0.27, 0.71, 1.37 gmol/L） was 82.00±1.70%, 104.00±4.00%, 98.70±3.10%; 92.30± 1.50%, 105.30±2.30%, 108.00±2.00%; 80.00±7.80%, 107.69±8.00%, and 108.66±3.10%, respectively at each concentration. Their intra-day RSD was 3.3%, 3.4%, and 2.5%, and inter-day RSD was 4.2%, 2.3%, and 5.6%, respectively. The mean extracellular concentrations of DOPAC, DA, and HVA in rat brain microdialysates were 10.7, 2.4, and 9.2 gmol/L （n=6）, respectively. Conclusion The findings of our study suggested that the simple, accurate and stable method can be applied to basic researches of diseases related to monoamines neurotransmitters by cerebral microdialysis in rats.

Higher-Order Statistics for Automatic Weld Defect Detection

Image processing and image analysis are the main aspects for obtaining information from digital image owing to the fact that this techniques give the desired details in most of the applications generally and Non-Destructive testing specifically. This paper presents a proposed method for the automatic detection of weld defects in radiographic images. Firstly, the radiographic images were enhanced using adaptive histogram equalization and are filtered using mean and wiener filters. Secondly, the welding area is selected from the radiography image. Thirdly, the Cepstral features are extracted from the Higher-Order Spectra (Bispectrum and Trispectrum). Finally, neural networks are used for feature matching. The proposed method is tested using 100 radiographic images in the presence of noise and image blurring. Results show that in spite of time consumption, the proposed method yields best results for the automatic detection of weld defects in radiography images when the features were extracted from the Trispectrum of the image.

High Impedance Fault Detection of Distribution Network by Phasor Measurement Units

This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault current. In this paper, an index based on phasors change is proposed for HIF detection. The phasors are measured by PMU to obtain the square summation of errors. Two types of data are used for error calculation. The first one is sampled data and the second one is estimated data. But this index is not enough to declare presence of a HIF. Therefore another index introduces in order to distinguish the load switching from HIF. Second index utilizes 3rd harmonic current angle because this number of harmonic has a special behaviour during HIF. The verification of the proposed method is done by different simulation cases in EMTP/MATLAB.

Multichannel noncoherent integration detection using high range resolution profile

A multichannel noncoherent integration detection method based on high range resolution profile was presented in this paper. According to the property of the moment generating function, the distribution characteristics of the noncoherent integrated signals with or without target presence were derived under the circumstance with noncorrelated Gaussian distribution noises. The loss of noncoherent integration was due to improper selection of integration range of cell numbers. A multi channel noncoherent integration detection scheme where the integration number in each channel va ries was proposed to solve this problem. The quality of this method for detection of various targets was evaluated. A comparison of fixed integration range cell number detection and multichannel inte gration detection for a high range resolution profile was presented. Simulation results indicated that the principle of the method was correct and performed well for unknown physical dimension targets. The method required little prior knowledge about target and was convenient for practical implementa tion.

Determination of neomycin in water samples by high performance anion chromatography with pulsed amperometric detection

A simple, fast and reliable method, using high performance anion chromatography with pulsed amperometric detection, had been developed for the analysis of neomycin in water samples. The elution and separation were carried out with an isocratic mobile phase, containing 10 mmol/L NaOH. The influence of the concentration and pH of the mobile phase on the separation and detection was investigated. A quadruple-potential waveform used for the detection was optimized. The detection limit of neomycin was down to 0.027 μg/mL. The linearity of neomycin calibration curve ranged from 0.050 to 0.505 μg/mL with correlation coefficient of 0.9997. R.S.D. (n = 11) was 4.0%.

High resolution colonoscopy in a bowel cancer screening program improves polyp detection

AIM:To compare high resolution colonoscopy(Olympus Lucera) with a megapixel high resolution system(Pentax HiLine) as an in-service evaluation.METHODS:Polyp detection rates and measures of performance were collected for 269 colonoscopy procedures.Five colonoscopists conducted the study over a three month period,as part of the United Kingdom bowel cancer screening program.RESULTS:There were no differences in procedure duration(χ2 P = 0.98),caecal intubation rates(χ2 P = 0.67),or depth of sedation(χ2 P = 0.64).Mild discomfort was more common in the Pentax group(χ2 P = 0.036).Adenoma detection rate was significantly higher in the Pentax group(χ2 test for trend P = 0.01).Most of the extra polyps detected were flat or sessile adenomas.CONCLUSION:Megapixel definition colonoscopes improve adenoma detection without compromising other measures of endoscope performance.Increased polyp detection rates may improve future outcomes in bowel cancer screening programs.

Medical equipments high precise detection technology basing on morphology-harris operator

Medical equipments related to life safety of human, it is important to detect by a high precise method. Image mosaic which based on Harris corner operator is a commonly used method in this area;Harris operator has low calculation burden, it is simple and stable, so it is more effective comparing with other feature point extracted operators. But in this algorithm, corner points can only be detected in a single-scale, there may be losing information of corner points, causing corner point location offset, extracting false corner points because of noise. In order to solve this question, the acquired images should be processed by dilation and erosion operation firstly, then do image mosaic. Results show that image noise can be eliminated effectively after those morphological processes, as well as the false positive noise generated by image glitch. The success rate of image mosaic and detection accuracy can be greatly improved through the Morphology-Harris operator. Measurement of precision instruments which based on this new method will improve the measurement accuracy, and the research in this area will promote the further development of machine vision technology.

Highly Sensitive Detection of Deoxyribonucleic Acid Hybridization Using Au-Gated AlInN/GaN High Electron Mobility Transistor-Based Sensors

Gallium nitride- （GaN） based high electron mobility transistors （HEMTs） provide a good platform for biological detection. In this work, both Au-gated AlInN/GaN HEMT and AlGaN/GaN HEMT biosensors are fabricated for the detection of deoxyribonucleic acid （DNA） hybridization. The Au-gated AIInN/GaN HEMT biosensor exhibits higher sensitivity in comparison with the AlGaN/GaN HEMT biosensor. For the former, the drain-source current （VDS = 0.5 V） shows a clear decrease of 69μA upon the introduction of 1μmolL^-1 （μM） complimentary DNA to the probe DNA at the sensor area, while for the latter it is only 38 μA. This current reduction is a notable indication of the hybridization. The high sensitivity can be attributed to the thinner barrier of the AlInN/GaN heterostructure, which makes the two-dimensional electron gas channel more susceptible to a slight change of the surface charge.