Random Frequency Research Articles

Estimate of the random match frequency of acquired characteristics in footwear: Part II — Impressions in dust

This study serves as Part II of an investigation into the random match frequency of randomly acquired characteristics (RAC-RMF) in footwear evidence. In Part I, RAC-RMF was estimated in a dataset of laboratory-simulated crime scene impressions deposited in blood. For Part II, a second dataset was created composed of impressions deposited in dust on paper or tile, with the latter lifted using gelatin or Mylar film. A total of 1,513 RACs were identified from more than 160 dust impressions and compared to RACs with positional similarity in test impressions from 1,299 non-mated outsoles. RACs of any size deposited in dust exhibited a 31% decrease in shoes with non-zero RAC-RMFs as compared to their mated test impressions, while those deposited in blood exhibited a 45% increase. When only considering shoes with at least one RAC deemed forensically-reliable (length ⩾ 2.8 mm), 3.1% of shoes contributing dust impressions and 3.4% of shoes contributing blood impressions exhibited relative RAC-RMFs at a value ⩾ 0.0008. Although each dataset resulted in a comparable rate for encountering non-zero RAC-RMFs, the estimate for dust was based on twice the number of RAC comparisons (154,477) than those performed when assessing blood (77,566). While these results are considered specific to the non-mated impressions and methods of analysis described herein, and continued work is required before rates can be fully understood and reported in forensic casework, this study encountered non-zero RAC-RMFs for shoes exhibiting at least one forensically-reliable RAC at a more frequent rate than any estimates previously reported.

Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia

Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.

Use of pupil-difference moments for predicting optical performance impacts of generalized mid-spatial frequency surface errors.

In this work, we present a methodology for predicting the optical performance impacts of random and structured MSF surface errors using pupil-difference probability distribution (PDPD) moments. In addition, we show that, for random mid-spatial frequency (MSF) surface errors, performance estimates from the PDPD moments converge to performance estimates that assume random statistics. Finally, we apply these methods to several MSF surface errors with different distributions and compare estimated optical performance values to predictions based on earlier methods assuming random error distributions.

RECOGNITION OF REFERENCE SIGNALS AND DETERMINATION OF THEIR WEIGHTING COEFFICIENTS IF AN ADDITIVE INTERFERENCE PRESENTS

Context. The subject matter of the article is the recognition of a reference signal in the presence of additive interference.
 Objective. The recognition of the reference signal by the obtained value of its weighting factor in conditions where additive interference is imposed on the spectrum of the reference signal at unknown random frequencies. The task is the development of a method for recognizing a reference signal for the case when the interference consists of an unknown periodic signal that can be represented by a finite sum of basis functions. In addition, interference may also include deterministic signals from a given set with unknown weighting coefficients, which are simultaneously transmitted over the communication channel with the reference signal.
 Method. The method of approximating the unknown periodic component of the interference by the sum of basis functions is used. The current number of values of the signal that enters the recognition system depends on the number of basis functions. This signal is the sum of the basis functions and the reference signal with unknown weighting coefficients. To obtain the values of these coefficients, the method based on the properties of the disproportion functions is used. The recognition process is reduced to the calculation of the weight coefficient of the reference signal. If it is zero, it indicates that the reference signal is not part of the signal being analyzed. The recognition system is multi-level. The number of levels depends on the number of basis functions.
 Results. The obtained results show that, provided that the reference signal differs by at least one component from the given set of basis functions, the recognition is successful. The given examples show that the system recognizes the reference signal even in conditions where the weighting coefficient of the interference is almost 1000 times greater than the coefficient for the reference signal. The recognition system also works successfully in conditions where the interference includes the sum of deterministic signals from a given set, which are simultaneously transmitted over the communication channel.
 Conclusions. The scientific novelty of the obtained results is that a method for recognizing the reference signal has been developed in conditions where only an upper estimate of its maximum frequency is known for the periodic component of the interference. Also, recognition occurs when, in addition to unknown periodic interference, the signals from a given set with unknown weighting coefficients are superimposed on the reference signal. In the process of recognition, in addition to the weighting factor for the reference signal, the factors for the interference components are also obtained.

Electrical stimulation of the auricular branch of the vagus nerve potentiates analgesia induced by physical exercise in mice with peripheral inflammation.

This study evaluated the antihyperalgesic and anti-inflammatory effects of percutaneous vagus nerve electrical stimulation (pVNS) associated with physical exercise, i.e., swimming, in mice with peripheral inflammation. The pain model was induced by intraplantar (i.pl.) injection of Freund's complete adjuvant (CFA). Sixty-four male Swiss mice (35-40 g) received an i.pl. of CFA and underwent behavioral tests, i.e., mechanical hyperalgesia, edema, and paw temperature tests. Additionally, cytokine levels, specifically interleukin-6 (IL-6) and interleukin-10 (IL-10), were determined by enzyme-linked immunosorbent assay. Mice were treated with swimming exercise for 30 min alone or associated with different time protocols (10, 20, or 30 min) of stimulation in the left ear with random frequency during four consecutive days. pVNS for 20 min prolonged the antihyperalgesic effect for up to 2 h, 24 h after CFA injection. pVNS for 30 min prolonged the antihyperalgesic effect for up to 7 h, 96 h after CFA injection. However, it did not alter the edema or temperature at both analyzed times (24 and 96 h). Furthermore, the combination of pVNS plus swimming exercise, but not swimming exercise alone, reduced IL-6 levels in the paw and spinal cord, as well as IL-10 levels in the spinal cord. pVNS potentiates the analgesic effect induced by swimming, which may be, at least in part, mediated by the modulation of inflammatory cytokines in the periphery (paw) and central nervous system (spinal cord). Therefore, the combination of these therapies may serve as an important adjunctive treatment for persistent inflammatory pain.

Disrupting perceptual anchoring to pure-tone sequences in human listeners

Perceptual anchoring, a process akin to statistical learning, occurs rapidly and without conscious awareness and is integral to our ability to successfully navigate a noisy world. Here, we investigated anchoring abilities in typical hearing and reading participants by implementing an anchoring paradigm (Agus et al., 2014) using rapid pure-tone sequences (Barascud etal., 2016). We then attempted to disrupt anchoring by applying rapid transcranial magnetic stimulation (rTMS) to frontal cortical regions—areas implicated in the processing of and integration of tone sequences (Abla and Okanoya, 2008). Pure-tone sequences consisted of 50 ms tone-pips that were arranged according to two segments, random (RAND) and regular (REG). RAND segments were generated as tones of random frequencies, and REG segments were generated as in RAND but were iterated to create a repeating pattern. Sequences were presented across three conditions: REPfixed (identical sequences repeated across trials), REPnovel (identical sequences not repeated across trials), and NonREPnovel (nonidentical sequences not repeated across trials). We observed a significantly higher sensitivity to REPfixed relative to REPnovel across all participants (i.e., an anchoring effect). The disruption of frontal regions using rTMS did not significantly impact overall performance but did alter how participants completed the task over time.

Dual Random Noise Suppression Strategy for Permanent Magnet Synchronous Motor Based on Random Pulse Width Modulation and Random High-frequency Injection Method

This study proposes an innovative dual control strategy to tackle the challenge of electromagnetic noise in the sensorless low-speed operation of Permanent Magnet Synchronous Motors (PMSM). The strategy combines random Pulse Width Modulation (PWM) technology with a stochastic high-frequency injection approach. By incorporating two distinct randomized frequency switching techniques into the high-frequency injection circuit, the method integrates two-state Markov chains into both PWM technology and the random high-frequency injection method. Subsequently, a mathematical model for signal processing is developed, and extensive simulation and experimental verification are conducted. The experimental findings affirm the effectiveness of this approach in effectively mitigating electromagnetic noise while ensuring accurate location estimation within an acceptable range.

Frequency selective fingerprint sensor: the Terahertz unity platform for broadband chiral enantiomers multiplexed signals and narrowband molecular AIT enhancement

Chiral enantiomers have different pharmacological and pharmacokinetic characteristics. It is important to strictly detect chiral component for avoiding being harmful to the human body due to side effects. Terahertz (THz) trace fingerprint detection is essential because the molecular vibrations of various biological substances such as chiral enantiomers are located in THz range. Recent reported enhanced trace fingerprint technologies have some drawbacks. For instance, multiplexing technology suffered from narrow operation range and limitation by frequency resolution of commercial THz time domain spectroscopy; Absorption induced transparency (AIT) identification for narrowband molecular oscillations suffered from random resonance frequency drift due to fabrication error. In this paper, we proposed frequency-selective fingerprint sensor (FSFS), which can experimentally achieve enhanced trace fingerprint detection by both broadband multiplexing technology and robust AIT identification. Such FSFS is based on polarization independent reconfiguration metasurfaces array. Broadband absorption lines of trace-amount chiral carnitine were boosted with absorption enhancement factors of about 7.3 times based on frequency-selective multiplexing at 0.95–2.0 THz. Enhanced trace narrowband α-lactose fingerprint sensing can be observed at several array structures with absorption enhancement factors of about 7 times based on AIT, exhibiting good robustness. The flexibility and versatility of proposed FSFS has potential applications for boosting trace chiral enantiomer detection as well as diversity of molecular fingerprints identification by both multiplexing and AIT.

Multiaverage Random Switching Frequency Space Vector Pulsewidth Modulation Strategy for High-Order Harmonics Dispersion

A multi-average random switching frequency space vector pulse width modulation (MARSF-SVPWM) strategy is proposed in this paper, in order to further disperse the high-order harmonics concentrated at the switching frequency and its integer multiplies in the output voltage of the inverter. The proposed strategy divides the fundamental period equally, with different stages corresponding to different average switching frequencies and spread-spectrum ranges, in contrast with the conventional random switching frequency space vector pulse width modulation (RSF-SVPWM), under the premise of maintaining the same overall average switching frequency and spread-spectrum range. The simulation and experimental results demonstrate that, even in the case of a narrow spread-spectrum range, the proposed MARSF-SVPWM strategy can obtain a better effect of high-order harmonic dispersion.

Dual randomized discontinuous pulse width modulation for three-phase inverter

ABSTRACT The discontinuous PWM technique, with its advantage over the continuous PWM technique in reducing switching losses in power converters, is gaining popularity. However, its deterministic nature results in discrete harmonics with significant amplitudes, which leads to electromagnetic interference (EMI) emissions. The randomised PWM technique is gaining more interest because of its effectiveness in spreading the harmonic spectrum and thus reducing EMI problems in power converters. This paper presents a carrier-based dual randomised discontinuous PWM (DRDPWM) method for a three-phase voltage inverter. The DRDPWM method combines the random carrier frequency (RCF-DPWM) and random pulse position (RPP-DPWM) schemes. In addition to reducing switching losses, DRDPWM can achieve a broad spread of the voltage spectrum with significantly smaller amplitudes. Furthermore, it produces less inverter output current distortion when compared to traditional DPWM and simple random (RCF-DPWM and RPP-DPWM) schemes. Simulations and experimental results confirm the effectiveness of the suggested method for spreading the voltage spectrum compared to simple random schemes. Additionally, an evaluation of the performance of all randomised discontinuous schemes is conducted based on a number of assessment metrics, including the switching loss factor (SLF), the power spectral density (PSD), the harmonic spread factor (HSF), and the quality factor (QF).

Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models

Evaluation of landslide susceptibility is essential to planning of land and space utilization. For this purpose, the paper presents a case study from Fugu County, Shaanxi Province, China. Firstly, the geological environment and current state of landslides in Fugu County were investigated. Then, slope, aspect, terrain relief, curvature, lithology, land type, and normalized difference vegetation index (NDVI) were considered as the landslide susceptibility condition factors, and the correlation between these carried out by using Multicollinearity Analysis method. Next, landslide and non-landslide samples were divided into training samples and testing samples according to the sample ratios of 8/2, 7/3, 6/4, and 5/5, respectively. The landslide susceptibility mapping was carried out by using Random Forest (RF) model and Frequency Ratio coupled with Random Forest (FR-RF) model, respectively. Lastly, the landslide density (LD), landslide frequency ratio (LFR), the area under the curve (AUC) of the receiver operator, and other indicators were used to validate the rationality, accuracy, and performance of the landslide susceptibility maps produced from different models and ratios. The results indicated that all maps are reasonable, except the map when ratio is 5/5. For each map, regardless of ratios, the LD and LFR are the greatest in the zones classed as having a very high susceptibility, followed by those with a high, moderate, low, and very low classes.In the Random Forest (RF) model, when the training test set is not at the same time its in the area of extremely high sensitivity of LD and the size of the FR value respectively 7/3 (201.026) ​> ​8/2 (154.440) ​> ​6/4 (93.696) >5/5 (136.364) and 7/3 (4.806) ​> ​8/2 (3.692) ​> ​6/4 (3.260) ​> ​5/5 (2.240); in the Frequency Ratio coupled with Random Forest (FR-RF) model, Inall the training test sets the size of the proportion of LD and FR value respectively 7/3 (145.693) ​> ​6/4 (127.151) ​> ​5/5 (122.857) ​> ​8/2 (113.263) and 7/3 (3.334) ​> ​6/4 (3.073) ​> ​5/5 (2.811) ​> ​8/2 (2.592). What else, from the comparison of ROC curves, when ratio is 7/3, the accuracy of the two models is higher than that of other ratios. Similarly, the results of the ensemble model (A combination of two models with different learning abilities.) are not more reasonable than the results of the single model, which reflects that the combination of a weaker learner model (Frequency Ratio model here) with a stronger learner model (Random Forest model here) can diminish the performance of the stronger model.

Method for Evaluating the Reliability and Competitive Failure of Wind Turbine Gearbox Bearings Considering the Fault Incubation Point

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines.

POWER SPECTRAL DENSITY OF DUAL RANDOMIZED DISCONTINUOUS PULSE WIDTH MODULATION

This paper addresses the spectral characteristics of a carrier-based dual randomized discontinuous pulse width modulation (DRDPWM) technique for reducing conducted EMI and switching losses in three-phase voltage source inverters. It combines two simple random schemes, random carrier frequency modulation (RCF-DPWM) and random pulse position modulation (RPP-DPWM). First, the modulating principle of the DRDPWM is presented. Then, the analytical expressions of power spectral density (PSD) that characterize the output voltage of the three-phase inverter are derived. Analytical and simulation results show the effectiveness of PSD spreading of the DRDPWM. Finally, experimental results verify the analytical and simulation results and prove the effectiveness of the DRDPWM scheme in spreading the spectrum compared to simple schemes (RPP-DPWM and RCF-DPWM) and conventional scheme DPWM.

Investigation on jamming detection in WSN using optimal decision rule

A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches.

Random Integration Analysis of Recombinant Adeno-Associated Virus 6 Packaged in Sf9 Insect Cells

Recently, there have been growing concerns over the integration of recombinant adeno-associated virus (rAAV) used in gene therapy. Wild-type adeno-associated virus (AAV) site specifically integrates into AAVS1 site of human genome, while rAAV randomly integrates into host chromosomes at low frequencies. This research aims to study the random integration events of rAAV6-EGFP packaged in Sf9 insect cells. Baculo-Sf9 manufacturing platform has the advantages of high-density suspension culture of Sf9 insect cells and large-scale production of rAAV vectors. In this study, we used different doses of Baculo-Sf9 produced rAAV6-EGFP to transduce HEK293T cells and A549-implanted tumors in vitro and in vivo. Using flow cytometry and fluorescence microscopy, we studied their EGFP gene expression efficiencies and EGFP fluorescence intensities. Using inverse nested PCR and DNA sequencing, random integration sites of rAAV6-EGFP genome into human chromosomes were identified. In vitro results showed that gene expression efficiencies became stable after 20 days and random integration frequencies were 0.2‒4.2%. Both in vitro and in vivo results indicated that random integration of Baculo-Sf9 rAAV6 was dose-dependent. Sequencing results showed two random integration sites, which were on human chromosomes 8 and 12. The findings suggest that we should use as low dose of rAAV vector as possible for safe gene therapy.

The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery.

Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.

A Novel Electromagnetic Noise and Image Stripe Noise Suppression Method Between SMA-OIS Actuator and CMOS Image Sensor

In order to solve the electromagnetic compatibility (EMC) problem between shape memory alloy (SMA) optical image stabilization (OIS) actuator and CMOS image sensor (CIS), a novel electromagnetic noise and image stripe noise (ISN) suppression method is proposed in this paper. The electromagnetic interference mechanism of SMA-OIS actuator is analyzed by electromagnetic field simulation and magnetic dipole mathematical model. The CIS noise mechanism based on the readout circuit model and image signal processing (ISP) pipeline is analyzed and the ISN evaluation method of CIS is proposed. The drive frequency and ISN model is established by frequency sweep experiment through the joint experimental platform of SMA-OIS actuator and CIS. Furthermore, the random frequency modulation PWM method based on the spread spectrum principle is proposed to suppress the electromagnetic noise and ISN. The experimental results show that the state-of-the-art noise suppression method proposed in this paper can not only eliminate ISN, but also have good technical adaptability in practical applications.

Anti-Jamming Communication Using Slotted Cross Q Learning

Most of the existing intelligent anti-jamming communication algorithms model sensing, learning, and transmission as a serial process, and ideally assume that the duration of sensing and learning timeslots is very short, almost negligible. However, when the jamming environment changes rapidly, the sensing and learning time can no longer be ignored, and the adaptability of the wireless communication system to the time-varying jamming environment will be significantly reduced. To solve this problem, this paper proposes a parallel Q-learning (PQL) algorithm. In the case of long sensing and learning time, by modeling sensing, learning, and transmission as parallel processes, the time that the transmitter remains silent during sensing and learning is reduced. Aiming at the situation that the PQL algorithm is susceptible to jamming when the jamming changes faster, this paper proposes an intelligent anti-jamming algorithm for wireless communication based on Slot Cross Q-learning (SCQL). In the case of rapid change of jamming channel, the system can sense and learn the jamming patterns in multiple successive jamming periods at the same time in the same timeslot, and use multiple Q-tables to learn the jamming patterns in different jamming periods, so as to achieve the effect of reliable communication in the environment with rapid change of jamming. The simulation results show that the jamming collision rate of the proposed algorithm under the condition of intelligent blocking jamming is equivalent to that of the traditional Q-learning (QL), but the timeslot utilization rate is higher. Compared with PQL, the proposed algorithm has the same slot utilization and lower jamming collision rate. Compared with random frequency hopping (RFH) anti-jamming, the proposed algorithm not only has higher timeslot utilization, but also has lower jamming collision rate.

Chaotic ant colony algorithm-based frequency-optimized random switching frequency SVPWM control strategy

Chaotic ant colony algorithm-based frequency-optimized random switching frequency SVPWM control strategy

Statistical topology optimization scheme for structural damage identification

Statistical topology optimization (TO) scheme, which statistically records and analyzes local optima at random frequencies of the excitation force, is developed for material damage and open crack. To identify the alternations of stiffness, mass, and strength due to the damages, the structural responses of healthy and suspicious systems can be compared and optimized by TO for fault identification. Owing to the local optima problem in TO, slight modifications in responses caused by relatively minimal damage, and inadequate comparison of insufficient vibration information, the application of TO to a damage identification problem is often complicated. To overcome this complication, this study establishes a new approach that records local optima (layouts) and performs a statistical process (clustering and averaging of layouts). To illustrate the concept of the present scheme, several examples with material damage and open crack are solved.