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.

Methane concentration detection system based on differential infrared absorption

The infrared absorption method for methane concentration detection is an ideal way to detect methane at present. However, it is difficult to spread this method due to its high cost. In this paper, by using a wideband infrared light emitting di- ode （LED） accompanied with a PIN photo electric diode, a low-cost methane detection system was designed. To overcome the shortcomings caused by the wide working band, a differential light path was designed. By means of a differential ratio algo- rithm, the stability and the accuracy of the system were guaranteed. Finally, the validity of the system with the proposed algo- rithm was verified by the experiment results.

EM-based detection scheme for differential unitary space-time modulation

The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.

The Automated Vehicle Detection of Highway Traffic Images by Differential Morphological Profile

Vehicle detection has been the critical part of the traffic surveillance system for many years. However, vehicle detection method is still challenging. In this paper, differential morphology closing profile is used to extract the vehicle automatically from the traffic image. Along with closing profile, some addition operation has been applied as a part of the algorithm to get the high detection and quality rate. Result demonstrated that the novel method has an excellent detection and quality percentage. We also have compared our automated detection method with other traditional image processing based methods and the results indicate that our proposed method provides better results than traditional image processing based methods.

Multi-Branch Fractional Multi-Bit Differential Detection of Continuous Phase Modulation with Decision Feedback

Differential detection of continuous phase modulation suffers from significant intersymbol interference. To reduce bit error rate, multi-branch fractional multi-bit differential detection (MFMDD) with decision feed-back is proposed. By introducing decision feedback in multi-bit differential detected signals, severe inter-symbol interference can be removed. Simulation results show that the proposed structure can greatly im-proves the performance compared with MFMDD without decision feedback, and the performance of 9 FMDD is very near to the performance of the coherent detection.

Combined Signal Detection Method in Differential Frequency Hopping System

In order to enhance communication reliability of differential frequency hopping system, a receiver implemented with the concatenation of an optimal subblock-by-subblock maximum a posteriori probability (OBB-MAP) detector and a soft-decision Turbo decoder is proposed and validated in both AWGN and Rayleigh flat fading channels. It is shown that the OBB-MAP decoder can iteratively decode a cyclic trellis, and back-search the trellis for any state to obtain estimates for the prior information bits which can be employed by soft-decision Turbo decoder. The proposed receiver achieves a better bit error rate(BER) performance than maximum likelihood sequence estimation(MLSE) detector employing Viterbi algorithm. The simulation results demonstrate that the combined signal detection method improves communication quality.

Decision-feedback subset aided multiple-symbol differential detection

In view of the inaccuracy of the estimated symbols on the edge of the observation window, a decision-feedback subset aided multiple-symbol differential detection(MSDD) framework, dubbed DF-S-MSDD, is proposed in ultra-wideband impulse radio(UWB-IR) system with differential space-time block-code(DSTBC) modulation. Specifically, motivated by the decision-feedback aided MSDD(DF-MSDD), a subset of the decision-feedback symbols is selected, and the optimal symbols are preserved, and then all the remaining symbols are optimized. Furthermore, the simulations validate that the proposed DF-S-MSDD provides solid bit error-rate performance with a low complexity in UWB-IR system with DSTBC modulation.

Differentially-Keyed IR-UWB Signals for MA with Differential-Detection Receiver

Noncoherent communication receivers (differential-detectors) have simple design, however, they always incur bit error rate (BER) performance loss up to 3dB compared to coherent receivers. In this paper, a differential-detector is proposed for impulse radio ultra wideband (IR-UWB) communication systems. The system employs bit-level differential phase shift keying (DPSK) combined with code division (CD) for IR-UWB signals to support multiple-access (MA). It is analyzed under additive white Gaussian noise (AWGN) corrupted by multiple-access interference (MAI) channel. Its BER performance is compared against a reference coherent receiver using Monte-Carlo simulation method. A closed form expression for its average probability of error is derived analytically. Simulation results and theoretical analysis confirm the applicability of the proposed differential-detector for IR-UWB communication systems.

Differential Scheme for Reconfigurable Intelligent Surface-Based Spatial Modulation System

In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the receiver.The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems,as it avoids the high-order matrix calculation and improves the spectral efficiency.A mathematical framework is developed to determine the theoretical average bit error probability(ABEP)of the system using RIS-DSM.The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification.Finally,simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.

Measurements of Nighttime Nitrate Radical Concentrations in the Atmosphere by Long-Path Differential Optical Absorption Spectroscopy

The long-path differential optical absorption spectroscopy （LP-DOAS） technique was developed to mea- sure nighttime atmospheric nitrate radical （NO3） concentrations. An optimized retrieval method, resulting in a small residual structure and low detection limits, was developed to retrieve NO3. The time series of the NO3 concentration were collected from 17 to 24 March, 2006, where a nighttime average value of 15.8 ppt was observed. The interfering factors and errors are also discussed. These results indicate that the DOAS technique provides an essential tool for the quantification of NO3 concentration and in the study of its effects upon nighttime chemistry.

Multitemporal UAV-based photogrammetry for landslide detection and monitoring in a large area:a case study in the Heifangtai terrace in the Loess Plateau of China

With high spatial resolution,on-demand-flying ability,and the capacity for obtaining threedimensional measurements,unmanned aerial vehicle(UAV)photogrammetry is widely used for detailed investigations of single landslides,but its effectiveness for landslide detection and monitoring in a large area needs to be investigated.The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides.This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km^2.Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results.The structure-from-motion(Sf M)technique was used to convert overlapping images into orthographic images,3D point clouds,digital surface models(DSMs)and mesh models.Using multitemporal differential mesh models,landslide vertical movements and potential landslides were detected and monitored.The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area.

4.3 THz quantum-well photodetectors with high detection sensitivity

We demonstrate a high performance GaAs/AlGaAs-based quantum-well photodetector(QWP)device with a peak response frequency of 4.3 THz.The negative differential resistance(NDR)phenomenon is found in the dark currentvoltage(I-V)curve in the current sweeping measurement mode,from which the breakdown voltage is determined.The photocurrent spectra and blackbody current responsivities at different voltages are measured.Based on the experimental data,the peak responsivity of 0.3 A/W(at 0.15 V,8 K)is derived,and the detection sensitivity is higher than 10^(11)Jones,which is in the similar level as that of the commercialized liquid-helium-cooled silicon bolometers.We attribute the high detection performance of the device to the small ohmic contact resistance of-2Ωand the big breakdown bias.

New Iterated Decoding Algorithm Based on Differential Frequency Hopping System

A new iterated decoding algorithm is proposed for differential frequency hopping (DFH) encoder concatenated with multi-frequency shift-key (MFSK) modulator. According to the character of the frequency hopping (FH) pattern trellis produced by DFH function, maximum a posteriori (MAP) probability theory is applied to realize the iterate decoding of it. Further, the initial conditions for the new iterate algorithm based on MAP algorithm are modified for better performance. Finally, the simulation result compared with that from traditional algorithms shows good anti-interference performance.

NDC-IVM:An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.

Giant magneto-impedance sensor with working point selfadaptation for unshielded human bio-magnetic detection

Background Compared with traditional biomagnetic field detection devices,such as superconducting quantum interference devices(SQUIDs)and atomic magnetometers,only giant magneto impedance(GMI)sensors can be applied for unshielded human brain biomagnetic detection,and they have the potential for application in next-generation wearable equipment for brain-computer interfaces(BCIs).Achieving a better GMI sensor without magnetic shielding requires the stimulation of the GMI effect to be maximized and environmental noise interference to be minimized.Moreover,the GMI effect stimulated in an amorphous filament is closely related to its working point,which is sensitive to both the external magnetic field and the drive current of the filament.Methods In this paper,we propose a new noise reducing GMI gradiometer with a dual-loop self-adapting structure.Noise reduction is realized by a direction-flexible differential probe,and the dual-loop structure optimizes and stabilizes the working point by automatically controlling the external magnetic field and drive current.This dual-loop structure is fully program controlled by a micro control unit(MCU),which not only simplifies the traditional constant parameter sensor circuit,saving the time required to adjust the circuit component parameters,but also improves the sensor performance and environmental adaptation.Results In the performance test,within 2 min of self-adaptation,our sensor showed a better sensitivity and signal-to-noise ratio(SNR)than those of the traditional designs and achieved a background noise of 12 pT/√Hz at 10 Hz and 7pT/√Hz at 200 Hz.Conclusion To the best of our knowledge,our sensor is the first to realize self-adaptation of both the external magnetic field and the drive current.

Differential diagnosis of gallstones by using hypericin as a fluorescent optical imaging agent

AIM: To explore the feasibility of using hypericin as an optical imaging probe with affinity for cholesterol for differential fluorescent detection of human gallstones.METHODS: Cholesterol, mixed and pigment stones from cholecystectomy patients were incubated with hypericin or solvent. After 72 h, the stones were analysed for fluorescence(365 nm) and treated with 2-propanol/dimethyl sulfoxide for high performance liquid chromatography(HPLC) analysis. Rats with virtual gallbladder containing human cholesterol, mixed or pigment gallstones(VGHG) received 5 mg/kg hypericin or solvent and VGHG rats with cholesterol stones were given different hypericin doses(5-15 mg/kg). Twelve hours later, the stones were analysed at 365 nm. Biliary excretion and metabolites of hypericin were assessed in common bile duct(CBD) cannulated rats for 9 h using fluorospectrometry, HPLC and matrixassisted laser desorption/ionization-time-of-flight mass spectrometry(MALDI-TOF MS).RESULTS: Homogeneous high fluorescence was seen on cholesterol stones either pre-incubated with hypericin or extracted from VGHG rats receiving hypericin. Mixed stones showed a dotted fluorescent pattern, whereas pigment and solvent-treated ones lacked fluorescence. HPLC showed 7.68, 6.65 and 0.08 × 10^(-3) M of cholesterol in extracts from cholesterol, mixed, and pigment gallstones, respectively. Hypericin accounted for 2.0, 0.5 and 0.2 × 10-6 M in that order. On cholesterol stones from VGHG rats receiving different hypericin doses, a positive correlation was observed between dose and fluorescence. In the bile from CBD-cannulated rats, fluorescence represented 20% of the injected dose with two peaks in 9 h. HPLC analysis revealed that hypericin conjugates reached 60% of the peak area. By MALDI-TOF MS, hypericinglucuronide was detected. CONCLUSION: This study proves the potential use of hypericin for differential fluorescent detection of human gallstones regarding their chemical composition.

Pill Defect Detection Based on Improved YOLOv5s Network

To address the problems of low detection accuracy and slow speed of traditional vision in the pharmaceutical industry,a YOLOv5s-EBD defect detection algorithm:Based on YOLOv5 network,firstly,the channel attention mechanism is introduced into the network to focus the network on defects similar to the pill background,reducing the time-consuming scanning of invalid backgrounds;the PANet module in the network is then replaced with BiFPN for differential fusion of different features;finally,Depth-wise separable convolution is used instead of standard convolution to achieve the output Finally,Depth-wise separable convolution is used instead of standard convolution to achieve the output feature map requirements of standard convolution with less number of parameters and computation,and improve detection speed.the improved model is able to detect all types of defects in tablets with an accuracy of over 94%and a detection speed of 123.8 fps,which is 4.27%higher than the unimproved YOLOv5 network model with 5.2 fps.

Micro capacitance detection circuit for MEMS capacitive sensor

With the development of micro-electr o-mechanical system(MEMS) technolog y,the MEMS-based capacitive sensor has been widely applied in the field of ele ctron components.However,the capacitance of the micromachined sensor is so sma ll that the detection of the smaller value change of the capacitance is a great challenge.Based on the principle of charging and discharging of the capacitor,a kind of pulse width modulated differential circuit is introduced in this paper.For subsequent amplification,a modified amplifier is presented.The different ial circuit converts the weak capacitance change to the change of the pulse widt h of the output voltage,and the linear relationship can be obtained.And the mo dified amplifier implements the processes of amplification and filtering synchro nously,and a large DC output voltage can be obtained by the lo w-pass filter.T he designed circuits have advantages as simplified circuit,high voltage stabili ty,perfect linearity and resolution.Besides,it is feasible to be integrated w ith the sensor to largely reduce the transmission error and interference.

Local parameter identification with neural ordinary differential equations

The data-driven methods extract the feature information from data to build system models, which enable estimation and identification of the systems and can be utilized for prognosis and health management(PHM). However, most data-driven models are still black-box models that cannot be interpreted. In this study, we use the neural ordinary differential equations(ODEs), especially the inherent computational relationships of a system added to the loss function calculation, to approximate the governing equations. In addition, a new strategy for identifying the local parameters of the system is investigated, which can be utilized for system parameter identification and damage detection. The numerical and experimental examples presented in the paper demonstrate that the strategy has high accuracy and good local parameter identification. Moreover, the proposed method has the advantage of being interpretable. It can directly approximate the underlying governing dynamics and be a worthwhile strategy for system identification and PHM.

DPTP-LICD:A differential privacy trajectory protection method based on latent interest community detection

With the rapid development of high-speed mobile network technology and high-precision positioning technology,the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks.Researchers can mine users’trajectories in Location-based Social Networks to obtain sensitive information,such as friendship groups,activity patterns,and consumption habits.Therefore,mobile users’privacy and security issues have received growing attention in Location-based Social networks.It is crucial to strike a balance between privacy protection and data availability.This paper proposes a differential privacy trajectory protection method based on latent interest community detection(DPTP-LICD),ensuring strict privacy protection standards and user data availability.Firstly,based on the historical trajectory information of users,spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users.Secondly,the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory,and their priorities are assigned based on a popularity model.A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable.Finally,to prevent privacy leakage,we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points.Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’privacy from disclosure.