Adaptive photoelectric detection system based on system parameter library

In the photoelectric detection system, the photoelectric detector can convert the optical signal to be measured into a current signal, and the current amplifier transforms the current signal output by the detector into a voltage signal for amplification. In this study, the photo-multiplier tube (PMT) is selected as the photoelectric detector. Compared with other photoelectric detectors, it can obtain higher internal gain, higher sensitivity, and better response performance. The current amplifier is prepared by pre-amplifier and voltage amplifier. In order to capture photoelectric signals well, a large-format scanning system is set up to design each component module, control module, and host computer module of the system. Besides, a machine learning-based algorithm is proposed, namely semi-supervised manifold image recognition algorithm, which is used for identify photoelectric detection images. In the test process, the printed circuit board (PCB) and sapphire material are firstly used as the substrate of the current amplifier, and their influences in the circuit are compared. The peak value of the output noise of each substrate circuit is around 2.8 mV when the input of the current amplifier is short-circuited. Then, the signal gain and signal bandwidth of the photoelectric detection system remain stable when there is no optical signal input. During the process of changing the system signal gain ratio, the noise output of the system is the lowest when the voltage of PMT is 0.50 V and the current amplifier gain is set to 2.2 × 105 V/A. The proposed recognition algorithm can identify different types of targets well. After the image is projected into a two-dimensional space by the algorithm, the distance between classes increases, and the targets in the class promote aggregation, thereby enhancing the identify-ability between samples.

Self-powered silicon PIN photoelectric detection system based on triboelectric nanogenerator

The photoelectric detection system stands as a crucial instrument for target identification and task execution within diverse environments. The system is frequently confronted with rapid task alterations in the context of communication remote sensing observations. Conventional detection systems encounter challenges in swiftly adapting, underscoring the pressing necessity for an intelligent photoelectric detection system capable of multifaceted tasks and rapid alignment with communication remote sensing settings. This study undertakes a technical exploration of the intelligent photoelectric detection system, delineating the coexistence of semi-intelligent and fully intelligent modes. While the semi-intelligent mode is selected for specific task scenarios, the fully intelligent mode seamlessly takes precedence in the absence of specific tasks. Upon task assignment, the detection mode is designated, automatically calibrating system parameters (operating bands, aperture, integration time, gain, and focal length) in alignment with task requisites. The architecture comprises a detection module capable of seamlessly transitioning between imaging and spectral dimensions, complemented by an autonomous data processing module crafted through DSP+FPGA+ARM technologies. Grounded in this technological foundation, the study designs and employs an intelligent photoelectric detection system to procure communication remote sensing image data, focusing on underwater acoustic signal analysis. The system’s configuration facilitates the creation of a communication remote sensing photoelectric detection mechanism specifically tailored for underwater acoustic signals. Rigorous experimentation involving laser communication in air and sound waves in water culminates in the successful acquisition of communication remote sensing image data. Experimental findings affirm the system’s efficiency in effectively detecting underwater acoustic signals.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

Cybercriminals have increasingly leveraged sophisticated techniques to bypass traditional signature-based detection systems through the use of Ransomware-as-a-Service (RaaS) platforms, double and triple extortion strategies, and advanced evasion mechanisms. As a result, ransomware attacks have reached unprecedented levels. Using this systematic evaluation framework, we examine the current state and effectiveness of machine learning (ML) and deep learning (DL) approaches for ransomware detection, addressing critical gaps in existing research methodologies while providing comprehensive recommendations for future research. The study analyses multiple AI paradigms including supervised learning algorithms such as Random Forests and Support Vector Machines, unsupervised techniques such as clustering and anomaly detection, and deep learning architectures such as Convolutional Neural Networks and Long Short-Term Memory networks. Hybrid approaches combining static and dynamic analysis consistently achieve superior performance, with accuracy rates exceeding 99% when properly implemented. As part of the framework, fundamental challenges are addressed such as dataset quality and diversity, feature extraction and selection methodologies, data preprocessing techniques, and performance evaluation metrics that have been tailored specifically for cybersecurity applications. Several findings indicate that ensemble learning methods outperform individual classifiers, with Random Forest algorithms being particularly effective at handling high-dimensional feature spaces while maintaining interpretability for security analysts. As a result of the study, significant limitations have been identified in current research, including an overreliance on static data sets that do not capture evolving threat landscapes, an inadequate representation of modern attack vectors, and a limited ability to generalize across different operational environments. Future directions of this research include explainable AI integration for transparent decision-making, adaptive real-time detection systems, and federated learning approaches for collaborative threat intelligence sharing while maintaining organizational privacy. It provides standardized methodologies for data curation, feature engineering, model development, and performance benchmarking, enabling fair comparisons between different AI approaches and facilitating reproducible research. This work contributes to essential guidance for cybersecurity practitioners, policymakers, and researchers in developing robust, adaptive, and interpretable ransomware detection systems capable of defending against increasingly sophisticated cyber threats while considering ethical concerns and regulatory compliance requirements in modern digital ecosystems.

There are considerable hurdles that must be overcome in order to maintain adequate security against cyber-attacks in cloud settings because of their dynamic and dispersed nature. A proactive method to identifying and mitigating new threats in real time is provided by adaptive intrusion detection systems (IDS) that make use of machine learning (ML). The design and operation of adaptive intrusion detection systems (IDS) that are adapted for cloud platforms are investigated in this study. Particular attention is paid to the utilisation of supervised, unsupervised, and reinforcement learning methods. It investigates the benefits of adaptive intrusion detection systems (IDS) in terms of identifying abnormal behaviours, minimising the number of false positives, and adapting to shifting threat environments. Several important factors, including the selection of features, the quality of the dataset, and the optimisation of the algorithm, are covered. In addition to this, we investigate the possibility of integrating adaptive intrusion detection systems with cloud-native technologies such as serverless computing and containers. Performance measurements and comparative evaluations demonstrate that machine learning-based intrusion detection systems are more effective than older techniques. Additionally, the study addresses issues such as scalability, data privacy, and adversarial assaults, and it proposes viable methods to improve dependability. The adaptive intrusion detection system (IDS) is an essential component of cloud security methods since it enables continuous monitoring and reaction to complex kinds of attacks.

The general architecture of adaptive robotic systems for manufacturing applications

Smartphone cyber-attacks are one of the major security threats of the present-day mobile computing era. Such attacks are used on the weaknesses that arise upon initial device configuration and they tend to extract sensitive data of the users, the system integrity and the functionality of a given device. This paper presents the post-installation attack vectors and detection together with a new Dynamic Security Assessment Framework (DSAF) that can be used to detect and mitigate the attacks in real time. The proposed method integrated behavior analysis, machine learning, and anomaly detection methods to detect post-installed suspicious activity. The proposed method uses two prominent algorithms, i.e., the Adaptive Threat Detection Algorithm (ATDA) and the Risk-Based Response Algorithm (RBRA). We have carried out an experimental study that shows that our strategy reaches a 94.7 percent accuracy in detection with a false positive of 2.3 percent that is much higher than what the current security solutions are capable of. The effectiveness of the framework is proved through thorough testing on 1,500 smartphones of various platforms whereby a 78 percent post-installation invasion is curbed using the framework as compared to conventional security systems.

The technology of moving target detection algorithms based on PC platforms is relatively mature and has been widely used in various fields. However, it has certain drawbacks, such as poor real-time performance and insufficient processing speed. Therefore, this design adopts a moving target recognition and detection scheme based on ZYNQ hardware parallel acceleration, overcoming the speed limitations of traditional CPUs in processing video stream data. This solution not only performs image filtering and binarization but also processes various signals, optimizing the processing speed from traditional single-threaded operations to parallel cycle-time-level operations. By conducting comparative experiments using OpenCV on the PC side, the frame rate and various data in image processing were analyzed and compared. The experimental results show that the FPGA target detection system can detect and track moving targets in real-time at various resolutions and frame rates. The hardware parallel acceleration scheme significantly outperforms the traditional single-threaded approach in processing and recognition speed, and the utilization rate has also been greatly improved. This proves the feasibility of the scheme in high-speed recognition scenarios.

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both adaptive detectors are assessed and the influence of mismatched steering vector on detection probability is analyzed. The experimental results show that the adaptive detectors have an acceptable loss with respect to the nonadaptive counterparts. Furthermore, the ANSDD-GLRT converges much faster than the ASDD-GLRT, however, the latter rejects the misaligned signals more effectively. Both detectors perform well for the uniformly-distributed-energy target, but degenerate for the fluctuating targets. In addition, the correlation between target scatterers results in an additional gain for low signal-to-clutter power ratio.

A white light photoelectric aerosol detection system suitable for free atmospheric determination of particle concentration from ground level to about 28 km altitude is described. Particle size distributions obtained in situ over the radius range of approximately 0.16 to 1 μm on three soundings are presented. The tropospheric distributions show close to power-law dependence, dN/dlogr=r−3.3, whereas the stratospheric distributions have a steeper power law slope for r≳0.3 μm and a flatter slope for r≳0.3 μm, agreeing well with a recent impactor-derived distribution using a new analysis model. These findings question the validity of earlier globally representative distribution models.

The dynamic deflection measurement is an important component to the bridge loading test of high-speed railway bridge. Using inclinometers to test the Dynamic deflection is a good method for High-Speed Railway bridge deflection detection. In this paper, we develop a new inclinometer and a new theory to fast calculate deflection. It has been use to detect the dynamic deflection of Baishui River bridge on FuZhou-WenZhou High-Speed Railway when high speed train runs across at kinds of speed. Results indicate: comparing with the measurement results of photoelectric deflection detection system, all dynamic deflection measurement results of inclinometers are very accuracy. Their deflection curves are also similar. All results also show: Using our inclinometers to test High-Speed Railway bridge dynamic deflection can get precise results and can satisfy the dynamic deflection measurement request of high-speed railway bridge. Comparing with other methods of dynamic deflection measurement, this method is very easy to operate., do not need stop the traffic, and not affected by weather, and not need a static reference point, can measure the dynamic deflection of any beam bridge.

Photoelectric detector surface damage state polarization imaging type detection system

High power consumption and hardware cost are the two challenges for practical massive MIMO systems. One promising solution is to employ low resolution analog to digital converter (ADC). In this paper, massive MIMO relaying systems are investigated with double resolution ADCs at base station receiver, where some antennas are connected to low resolution ADCs, while the rest of the antennas are connected to high resolution ADCs. Closed form expression for uplink spectral efficiency (SE) is derived using the maximum ratio combining receiver assuming perfect channel state information. Some asymptotic analysis is discussed to explore the effects of system parameters on SE such as; the number of receive antennas, ADC quantization bits, user transmit power and the rate of high precision ADC in double resolution ADC structure. Moreover, energy efficiency with some system parameters is studied. It is shown that the performance wastage owing to low precision ADCs can be recompensed by growing the number of receive antennas $$ \varvec{N} $$, following a logarithmic scaling law instead of increasing the transmit power at both of source and relay. The double resolution ADCs can achieve a better tradeoff between power consumption and SE when using 4 quantization bits. By using only around 30 antennas, the double resolution ADCs achieves its optimal energy efficiency for selecting system parameters.

The changed environment factors will affect the capture rate of target and the detection performance in photoelectric detection screen testing system, to improve the detection performance of system, it is very necessary to establish the target optical characteristic model and correlation detection probability calculation model. Based on the detection principle of unit photoelectric detection screen and the spatial geometric relation of four photoelectric detection screens testing system, this article establishes a new target radiation characteristics calculation model by using target's radiation luminance and radiation energy, gives the output signal expression function, derived the detection probability function by signal to noise ratio (SNR) and false alarm probability. Through calculation analysis, the results show that the detection probability of four photoelectric detection screens testing system is related to the target optical characteristic, the output photoelectric current and SNR of photoelectric detection receiving sensor can describe target detection performance. According to the experiments testing, the proposed calculation model can effectively evaluate the performance of photoelectric detection screen testing system.