Neural Research Articles

Reliable prediction of peak cladding temperature during top reflooding processes: A neural network-based perception-interaction approach

The reliable prediction of peek cladding temperature is crucial for heat transfer and reactor security when large break loss of coolant accident (LBLOCA) happens. This study proposes a neural network-based Perception-Interaction system to effectively predict and monitor the peak cladding temperature during reflooding phase after LOCA. The system employs a narrow rectangular channel for conducting top reflooding experiments, wherein the peak temperature is predicted by the Back Propagation Neural Network (BPNN) algorithm. The system’s primary objective is to accurately predict the peak cladding temperature of various test conditions. First, the special Counter-Current Flow Limitation (CCFL) phenomenon is identified based on the different heat exchange patterns inside the test section. Then the experimental data is collected by LabVIEW and calculated by MATLAB to accurately predict the quench velocity and peak temperature at a specific location by conducting various test conditions. The final experiment results indicate that the built Perception-Interaction system in this article has a high prediction accuracy, with a relative error within 2% between the predicted peak temperatures and actual temperatures.

A Wirelessly Powered Scattered Neural Recording Wearable System.

This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 μW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12×12×5 mm3, enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 μVRMS. The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system's functionality is fully evaluated in human experiments.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Detection of minimal extended driver nodes in energetic costs reduction.

Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.

A Standard-Cell-Based Neuro-Inspired Integrate-and-Fire Analog-to-Time Converter for Biological and Low-Frequency Signals - Comparison With Analog Version.

Continuous-time asynchronous data converters namely, analog-to-digital converters and analog-to-time converters, can be beneficial for certain types of applications, such as, processing of biological signals with sparse information. A particular case of these converters is the integrate-and-fire converter (IFC) that is inspired by the neural system. If it is possible to develop a standard-cell-based (SCB) IFC circuit to perform well in advanced technology nodes, it will benefit from the simplicity of SCB circuit designs and can be implemented in widely available field-programmable gate arrays (FPGAs). This way, this paper proposes two IFC circuits designed and prototyped in a 130 nm CMOS standard process. The first is a novel SCB open-loop dynamic IFC. The latter, is a closed-loop analog IFC with conventional blocks. This paper presents a through comparison between the two IFC circuits. They have a power dissipation of 59 μW and 53 μW, and an energy per pulse of 18 pJ and 1060 pJ, SCB and analog IFC, respectively. The SCB IFC has one of the lowest energy per pulse consumption reported for IFC circuits. The analog IFC, being fully differential, is to our knowledge the first of its kind. Moreover, they do not require an external clock. They can convert signals with a peak-to-peak amplitude from 1.6 mV to 28 mV and 0.6 mV to 2.4 mV, and a frequency range of 2 Hz to 42 kHz and 10 Hz to 4 kHz, SCB and analog IFC, respectively. Presenting low normalized RMS conversion plus reconstruction errors, below 5.2%. The maximum pulse density (average firing-rate) is 3300 kHz, for the SCB and 50 kHz, for the analog IFC.

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer.

Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor-infiltrating immune cells in modulating the antitumor therapy response. However, the prognostic value of using these features in combination, and their potential for guiding individualized treatment for breast cancer, remains vague. To address this challenge, we recently developed the metastatic and immunogenomic risk score (MIRS), a comprehensive and user-friendly scoring system that leverages advanced bioinformatics methods to facilitate transcriptomics data analysis. To help users become familiar with the MIRS tool and apply it effectively in analyzing new breast cancer datasets, we describe detailed protocols that require no advanced programming skills. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Calculating a MIRS score from transcriptomics data Basic Protocol 2: Predicting clinical outcomes from MIRS scores Basic Protocol 3: Evaluating treatment responses and guiding therapeutic strategies in breast cancer patients Basic Protocol 4: Guidelines for utilizing the MIRS webtool.

The rapid improvement method of fatigue life reliability for single-crystal aeroengine turbine blades: Casting orientation design technology

The uncertainty in the casting orientation of single-crystal aeroengine turbine blades is an intractable engineering problem, and breaking through the crystal orientation design technology is vitally important to enhance the fatigue lifetime reliability of the blade. In this paper, a three-dimensional decoupling model for casting orientation is established according to the crystallographic characteristics of single-crystal blade, and the distribution information and statistical features of the orientation deviation angle of single-crystal turbine blades are obtained for the first time according to this decoupling model. Subsequently, the orientation-dependent models and generalized regression neural networks (GRNNs) analysis system of reliability are established, and the analysis of fatigue life response for single-crystal blades in four quadrants is carried out based on the neural network analysis system. Finally, three orientation design schemes are developed, and the improvement effect of each scheme on lifetime reliability are investigated. For the secondary orientation design scheme, the lifetime reliability is improved from 62 % to more than 95 % at the design life of 15,000 cycles; For the integrated orientation design scheme, the life reliability is improved from 50 % to more than 95 % at the design life of 20,000 cycles. The results show that the orientation design can effectively improve the fatigue life reliability of the product, especially in the long-life design interval of 10,000 to 20,000 cycles. Therefore, the orientation design should be highly emphasized in the design of single-crystal turbine blades for aeroengine.

Cortico-striatal white-matter connectivity underlies the ability to exert goal-directed control.

The balance between goal-directed and habitual control has been proposed to determine the flexibility of instrumental behaviour, in both humans and animals. This view is supported by neuroscientific studies that have implicated dissociable neural pathways in the ability to flexibly adjust behaviour when outcome values change. A previous Diffusion Tensor Imaging study provided preliminary evidence that flexible instrumental performance depends on the strength of parallel cortico-striatal white-matter pathways previously implicated in goal-directed and habitual control. Specifically, estimated white-matter strength between caudate and ventromedial prefrontal cortex correlated positively with behavioural flexibility, and posterior putamen-premotor cortex connectivity correlated negatively, in line with the notion that these pathways compete for control. However, the sample size of the original study was limited, and so far, there have been no attempts to replicate these findings. In the present study, we aimed to conceptually replicate these findings by testing a large sample of 205 young adults to relate cortico-striatal connectivity to performance on the slips-of-action task. In short, we found only positive neural correlates of goal-directed performance, including striatal connectivity (caudate and anterior putamen) with the dorsolateral prefrontal cortex. However, we failed to provide converging evidence for the existence of a neural habit system that puts limits on the capacity for flexible, goal-directed action. We discuss the implications of our findings for dual-process theories of instrumental action.

Generalized Slowing of Resting-State Neural Oscillations in People With Schizophrenia

BackgroundRecent interest in how neural oscillations reflect the flow of information through the brain has led to partitioning electroencephalography (EEG) recordings into periodic (i.e., oscillatory) and aperiodic (i.e., nonoscillatory) components. While both contribute to conventional measures of power within the frequencies that compose EEG recordings, the periodic aspect characterizes true oscillations, the speed of which is thought to be critical to efficient functioning of neural systems. Given evidence of EEG power abnormalities in schizophrenia (SCZ), we sought to determine whether the periodic aspect of EEG was aberrant in people with SCZ and could serve as a general measure of brain efficiency. MethodsResting-state EEGs were gathered from 104 participants with SCZ and 105 healthy control participants. We used the FOOOF toolbox to remove aperiodic neural activity. We computed the cross-correlation between power spectra for individual participants and the mean power spectrum for all participants to quantify the relative speed of neural oscillations. ResultsPeriodic activity in SCZ was shifted toward lower frequencies than control participants during eyes-closed rest. On average, participants with SCZ had a 0.55-Hz shift toward oscillatory slowing across the frequency spectrum that predicted worse perceptual reasoning. Slower neural oscillations were associated with weaker perceptual reasoning in SCZ. ConclusionsSlowed periodic activity at rest is evident in SCZ and may represent inefficient functioning of neural circuits as reflected in worse perceptual reasoning. A slower pace of neural oscillations may be a general limitation on the transmission of information within the brain.

Memory Reflections of the Microbiota-Gut and Oligodendrocyte Axis.

Memory is the persisting consequence of cognitive activities instigated by and engrossed on exterior information from the environment and commenced by an intensive on internal mental representations. Establishing a gut-brain axis (GBA) in health and disease has recently brought the gut, the main portal of communication with the external environment, to the forefront of this interaction. Dietary stimuli have long been linked to brain development, behavioral responses, and memory reflections. Vagus nerve, immune system, bacterial metabolites and products are just a few of the linkages that make up the GBA, a bidirectional arrangement of signaling pathways that connects the neurological system with the gastrointestinal tract. GBA involves two-way communication between central and enteric neural systems, connecting the brain's affective and cognitive regions to peripheral activities of the intestine. Recent scientific progress has highlighted the significance of gut microbiota in affecting these relationships. By controlling myelination at the prefrontal cortex, a crucial area for multifaceted cognitive behavior forecast and decision-making, this axis influences social behavior, including memory reflections. Humans may experience late myelination of the prefrontal cortex's axonal projections into the third decade of life, making it vulnerable to outside factors like microbial metabolites. It has been demonstrated that changes in the gut microbiome can change the microbial metabolome's composition, impacting highly permeable bioactive chemicals like p-cresol that may hinder oligodendrocyte differentiation. This review will discuss the memory reflections of the microbiota-gut and oligodendrocyte axis. Adopting this concept should encourage a new arena of thinking that recognizes the intricate central and periphery dynamics influencing behavior and uses that knowledge to develop novel therapies and interventions for maladjusted memory and learning systems.

Bioactive Peptides in Dairy Milk: Highlighting the Role of Melatonin.

Melatonin, an endogenous indolamine derived from tryptophan, is primarily synthesized by the pineal gland in mammals and regulated by a complex neural system. Its release follows a circadian rhythm, which is crucial for regulating physiological processes in response to light-dark cycles in both humans and animals. In this review, we report that the presence of this hormone in bovine milk, with significant differences in concentration between daytime and nighttime milking, has increased interest in milk as a natural source of bioactive molecules. Melatonin lowers cortisol levels at night, reduces body temperature and blood pressure, coinciding with decreased alertness and performance, acts as an antioxidant and anti-inflammatory agent, modulates the immune system, offers neuroprotective benefits, and supports gastrointestinal health by scavenging free radicals and reducing oxidative stress in dairy cows. Many factors influence the release of melatonin, such as the intensity of artificial lighting during nighttime milking, the frequency of milkings, milk yield, and genetic differences between animals. Nocturnal milking under low-intensity light boosts melatonin, potentially reducing oxidative damage and mastitis risk. Additionally, ultra-high temperature (UHT) treatment does not significantly affect the melatonin content in milk. However, further research on its stability during milk processing and storage is crucial for ensuring product efficacy. In some countries, nighttime milk with naturally elevated melatonin content is already commercialized as a natural aid for sleep. Thus, naturally melatonin-rich milk may be a promising alternative to synthetic supplements for promoting better sleep and overall well-being.

A Neural Signature for Reappraisal as an Emotion Regulation Strategy: Relationship to Stress-Related Suicidal Ideation and Negative Affect in Major Depression

BackgroundImpaired emotion regulation (ER) contributes to major depression and suicidal ideation and behavior. ER is typically studied by explicitly directing participants to regulate, but this may not capture spontaneous tendencies of individuals with depression to engage ER in daily life. MethodsIn 82 participants with major depressive disorder, we examined the relationship of spontaneous engagement of ER to real-world responses to stress. We used a machine learning–derived neural signature reflecting neural systems that underlie cognitive reappraisal (an ER strategy) to identify reappraisal-related activity while participants recalled negative autobiographical memories under the following conditions: 1) unstructured recall; 2) distanced recall, a form of reappraisal; and 3) immersed recall (comparison condition). Participants also completed a week of ecological momentary assessment measuring daily stressors, suicidal ideation, and negative affect. ResultsHigher reappraisal signature output for the unstructured period, a proxy for the spontaneous tendency to engage ER, was associated with greater increases in suicidal ideation following stressors (b = 0.083, p = .041). Higher signature output for distanced recall, a proxy for the capacity to engage ER when directed, was associated with lower negative affect following stressors (b = −0.085, p = .029). Output for the immerse period was not associated with ecological momentary assessment outcomes. ConclusionsFindings suggest that in major depressive disorder, the spontaneous tendency to react to negative memories with attempts to reappraise may indicate greater reactivity to negative cues, while intact capacity to use reappraisal when directed may be associated with more adaptive responses to stress. These data have implications for understanding stress-related increases in suicide risk in depression.

Probabilistic neural transfer function estimation with Bayesian system identification.

Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually require a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as most exciting inputs (MEIs), from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. At the same time, our variational method provides us with an effectively infinite ensemble, avoiding the idiosyncrasy of any single model, to generate MEIs. This allows us to estimate the uncertainty of stimulus-response function, which we have found to be negatively correlated with the predictive performance at model level and may serve to evaluate models. Furthermore, our approach enables us to identify response properties with credible intervals and to determine whether the inferred features are meaningful by performing statistical tests on MEIs. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models in the limited-data regime.

IScan: Detection of Colorectal Cancer from CT Scan Images Using Deep Learning

Colorectal cancer, a highly lethal form of cancer, can be treated effectively if detected early. However, the current diagnosis process involves a time-consuming and manual review of CT scans to identify cancerous regions and behavior, leading to resource consumption, subjectivity, and dependency on manual assessment. We propose a 3-phase deep neural system for automated colorectal cancer detection using CT scan images to address these challenges. It includes a SegNet network to identify tumor locations, an InceptionResNet V2 network to classify tumors as benign or malignant, and an analysis of tumor area cum perimeter to predict the cancer stage. The proposed model offers a fully automated solution by combining these functionalities under a single umbrella. In real-life CT scans from 37 patients, the proposed model achieved 95.8% ROI segmentation accuracy, a dice coefficient of 0.6214, 69.75% IoU score, and 95.83% tumor classification accuracy. The unique approach using Radial Length (RL) and Circularity (C) parameters predicted the T-stage with close to 85% accuracy. Based on these outcomes, the proposed system establishes itself as a reliable and suitable alternative to traditional cancer diagnosis techniques by leveraging the power of automation, deep learning, and innovative parameter analysis.

Evaluation of the Impacts of Pesticides Exposure in Different Durations on the Neural System and Antioxidants in Iraqi Farmers

The objective of this research was to investigate the effects of pesticides in different durations on some neurotransmitters and oxidative parameters compared to controls. A total of 120 male farmers participated in this study, 30 not exposed to pesticides served as control group and 90 exposed to pesticides and were split into two groups based on their exposure durations (˂5 & ≥5 years) and three age-groups (˂20, 20-40 and ≥41 years). Blood was collected to determine acetylcholinesterase (ACHE), serotonin (5-TH), dopamine (DA), reactive oxygen species (ROS), total antioxidant capacity (TAC) and malondialdehyde (MDA) levels using ELISA kits. The results of ACHE, serotonin, dopamine and TAC levels increased, whereas MDA and ROS levels decreased among farmers exposed to the pesticides. According to the exposure duration and farmers age, only 5-TH, DA and TAC levels were affected. It can be concluded that pesticides exposure may have an acute effect on acetylcholine and a chronic effect on the serotonin and dopamine via change in the antioxidant system which may play an important role in the neural diseases.

Differences Between Resting State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.

Resting state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and mean based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. 34 healthy participants were separated into a younger adult group (age 25-35, n=17) and an older adult group (age 65+, n=17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

AI-based rock strength assessment from tunnel face images using hybrid neural networks

In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways

Detecting looming signals for collision avoidance encounters challenges in real‐world scenarios, where moving backgrounds can interfere as an agent navigates through complex natural environments. Remarkably, even insects with limited neural systems adeptly respond to looming stimuli while in motion at high speeds. Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. Coordinating interneuron signals from both pathways can enhance the looming detection performance under dynamic conditions. An artificial neural network is employed to construct a combined‐pathway model based on Drosophila anatomy. The model outperforms state‐of‐the‐art bio‐inspired looming‐detection models in tasks involving dynamic backgrounds, simulated by animated 2D‐moving natural scenes or recorded in reality when an unmanned aerial vehicle performs obstacle collision avoidance tasks. Notably, by combining neural anatomy architecture and appropriate multiobjective tasks, the model exhibits convergent neural dynamics with biological counterparts post‐training, offering network explanations and mechanistic insights. Specifically, a multiplicative interneuron operation enhances looming signal patterns and reduces background interferences, generalizing to more complex scenarios, such as AirSim 3D environments and real‐world situations. The work introduces testable biological hypotheses and a promising bioinspired solution for looming detection in dynamic visual environments.

Voltage inspector: Sender identification for in-vehicle CAN bus using voltage slice

Controller Area Network (CAN) serves as the neural system of modern cars, connecting and coordinating various electronic control units (ECUs) responsible for vehicle operation. However, the inherent features of CAN, such as broadcast communication and lack of authentication, make it increasingly vulnerable to cyberattacks. Although existing intrusion detection systems (IDSs) perform well in detecting malicious attacks, they often lack the ability to accurately locate the senders of these malicious messages. In this paper, we propose an efficient sender identification method called Voltage Inspector, which leverages physical voltage signal slice to accurately identify the source of messages for CAN bus. We start by extracting voltage slices from the raw physical signals of the CAN bus. Next, we leverage clustering technology to infer the ECU mapping information, which is typically considered confidential. This mapping information, combined with a machine learning classifier, is then utilized to construct an identification model capable of accurately identifying the sender of each message. To validate the effectiveness of our proposed method, we conducted extensive experiments using a publicly available voltage dataset collected from ten real vehicles. The experimental results demonstrate the remarkable accuracy of our approach, achieving a minimum identification accuracy of 99%. Furthermore, our method significantly reduces the data volume by half and reduces the identification time by a quarter when compared to state-of-the-art methods. Our research reveals that even a small portion of the voltage signal can be used to uniquely fingerprint an ECU. We emphasize that our method serves as an alternative identification approach and can complement existing works in the field.

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Design and application of fuzzy neural network systems optimized with hybrid algorithms