Basic Signal Research Articles

Ralstonia solanacearum Infection Drives the Assembly and Functional Adaptation of Potato Rhizosphere Microbial Communities.

Bacterial wilt caused by Ralstonia solanacearum is a destructive disease that affects potato production, leading to severe yield losses. Currently, little is known about the changes in the assembly and functional adaptation of potato rhizosphere microbial communities during different stages of R. solanacearum infection. In this study, using amplicon and metagenomic sequencing approaches, we analyzed the changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere across four stages of R. solanacearum infection. The results showed that R. solanacearum infection led to significant changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere, with various microbial properties (including α,β-diversity, species composition, and community ecological functions) all being driven by R. solanacearum infection. The relative abundance of some beneficial microorganisms in the potato rhizosphere, including Firmicutes, Bacillus, Pseudomonas, and Mortierella, decreased as the duration of infection increased. Moreover, the related microbial communities played a significant role in basic metabolism and signal transduction; however, the functions involved in soil C, N, and P transformation weakened. This study provides new insights into the dynamic changes in the composition and functions of potato rhizosphere microbial communities at different stages of R. solanacearum infection to adapt to the growth promotion or disease suppression strategies of host plants, which may provide guidance for formulating future strategies to regulate microbial communities for the integrated control of soil-borne plant diseases.

Evaluation of biopsychosocial model for pain management in postoral cancer therapy patients

Background. Approximately 50 % of cancer patients have pain in their daily lives, which is multifaceted sensation goes beyond basic biochemical signal of pain. Oral mucositis is one of the negative consequences with intense pain, discomfort, challenges in speaking and eating. These components collectively influence patient's total quality of life across mental, biological, social aspects. Biopsychosocial model (BPS) is an effective technique for understanding and addressing conceptualization and treatment of pain in cancer patients. The aim of this study is to assess efficacy of the BPS in managing post-cancer distress and potential to improve quality of life for individuals with cancer. Material and Methods. This study evaluated 30 cancer patients who completed radiotherapy and were referred from cancer hospital. The examination encompasses three distinct categories: biological, psychological, and social components. The biological aspect was documented based on mucosal lesions and VAS scores for individuals; followed by photo-biomodulation was given. Palliative care was provided in psychological aspect through implementation of exercise, meditation, music therapy. The social component encompasses community engagement, social activities, counseling services for family members. Patients were categorized into 3 groups – A, B, C. All three components were carefully evaluated and one-month follow-up was done. Results. The outcome derived from statistical analysis of the data collected from groups A, B, and C. When group A was compared to group B, there was 46 % increase in quality of life. When group C was compared to group A, 65 % of patients exhibited favorable quality of life. The average VAS score of patients decreased from 9.2 to 4.5 after Low-Level Laser Therapy (LLLT), demonstrating gradual decrease in discomfort. P value showed statistically significant (<0.05). Conclusion. Healthcare providers can enhance their treatment efficiency, addressing root cause of illness, and enhance overall well-being by incorporating BPS into their practice. Addressing the various elements of biological, psychological, and social factors would have beneficial effect on overall quality of life.

Computational analysis of GCaMP fluorescence data in neuronal activity

The background and motivation of our research is to explore how to use computational methods to analyze GCaMP fluorescence data. GCaMP is a genetically encoded calcium indicator that can be used to observe the activity of thousands of neurons simultaneously using modern fluorescence imaging techniques. Our research includes the application of principles such as basic signal processing, statistical inference, hypothesis testing, and graph theory to help understand raw GCaMP fluorescence data recorded from awake, behavioral mice. The data set includes GCaMP fluorescence trace and correlation coefficient matrix among neurons. Our methods include high-pass filtering, Gaussian mixture model fitting and online active set method peak inference (oasis). By analyzing the correlation between neurons, we can understand the connection and centrality between neurons.

Multi-layered computational gene networks by engineered tristate logics

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics invivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

Design and Research of a Strain Elastic Element with a Double-Layer Cross Floating Beam for Strain Gauge Wireless Rotating Dynamometers.

Cutting force is one of the most basic signals that can reflect the information of the cutting process, so it is very necessary to study the strain elastic element of strain gauge wireless rotating dynamometers. This paper proposes a strain elastic element with a double-layer cross floating beam that can be applied to the strain gauge wireless rotating dynamometer, which can simultaneously obtain the four-component cutting force/torque information of FX, FY, FZ, and MZ. Based on the proposed strain elastic element, a compact strain gauge wireless rotating dynamometer is designed, which is composed of a tool holder, upper connection flange, strain elastic element, lower connection flange, tool base, and data acquisition and wireless transmission system. The static model of the double-layer cross floating beam on the strain elastic element is established by the segmented rigid body method, and the relationships between the material, force, structural parameters, and the strain and deformation of the floating beam are obtained. The static model is consistent with the finite element solution, which proves the rationality of the static model. Based on the established static model, the sequential quadratic programming algorithm is used to optimize the structural parameters of the double-layer cross floating beam to maximize the sensitivity of the floating beam. The overall structure of the strain elastic element is analyzed by finite element software, and the strain of the structure under simulation conditions is obtained, which provides a reference for subsequent calibration tests and circuit design. The calibration matrix and dynamic performance of the strain elastic element are obtained by the static calibration test, dynamic calibration test, and cutting test. The results show that the proposed strain elastic element has high sensitivity and low cross-sensitivity error, and can be applied to the strain gauge wireless rotating dynamometer to measure medium- and low-speed cutting forces.

An infrastructure‐less emergency location detection system using basic cellular device‐to‐device connection

AbstractOne of the most time‐sensitive and critical tasks for first responders in the aftermath of a disaster is to locate people for effective and successful rescue operations. The existing solutions for large‐scale location detection of citizens depend on expensive equipment that may not be readily available in many urban areas and also cannot cover a vast area quickly. To address this challenge, the present study proposes a novel location estimation method by creating a Mobile Ad Hoc Network (MANET) using citizens' mobile phones. The proposed solution can accurately estimate the location of mobile devices in a MANET based on their Received Signal Strength (RSS) and communicate the estimated location coordinates to first responders without any dependencies on telecommunication infrastructures. This system will help first responders locate and map mobile devices in the affected area in a matter of seconds to improve the efficiency and effectiveness of postdisaster rescue operations. The envisioned solution offers unique capabilities that differentiate it from previously existing methods. The proposed method directly estimates device locations using their basic cellular signal strength without any dependency on GPS and relies only on basic cellphone signals. Therefore, nonsmartphones can also join and function in the envisioned MANET system. It also does not impose any computational operations or requirements for installing an app on citizens' phones. The performance of the proposed system was empirically evaluated using a simulation analysis. The results indicated that the method could efficiently and accurately discover more than 93% of mobile devices and estimate their locations.

A molecularly imprinted electrochemiluminescence sensor based on ZnO@CdTe for the detection of mevastatin

In this paper, CdTe quantum dots with near-infrared fluorescence (NIR) and hexagonal prism-shaped ZnO were synthesized, and the composites were characterized for their performance. A sensitive molecularly imprinted electrochemiluminescence (ECL) sensor for the determination of mevastatin was constructed based on ZnO@CdTe and molecularly imprinted polymer (MIP). The highest ECL intensity of the system was obtained by ZnO@CdTe as the main luminophores reacted with the co-reactant K2S2O8. The ECL response generated by the reaction of ZnO@CdTe and K2S2O8 was used as the basic signal detection platform. The MIP was prepared by the electropolymerization process on the above platform, which laid the foundation for forming the mevastatin cavities with specific recognition sites. In addition, the response mechanism was investigated, and the determination conditions were optimized. The ECL intensity change values (ΔI) of the sensor showed a good linear relationship with the logarithm of mevastatin concentration in the range of 2.0 × 10−11 to 2.0 × 10−6 mol L−1, and the limit of detection was 6.7 × 10−12 mol L−1 (S/N = 3). This method obtained satisfactory results in the detection of mevastatin in tablets.

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

LUAEMA: A Loop Unrolling Approach Extending Memory Accessing for Vector Very-Long-Instruction-Word Digital Signal Processor with Multiple Register Files

Loop unrolling can provide more instruction-level parallelism opportunities for code and enables a greater range of instruction pipeline scheduling. In high-performance very-long-instruction-word (VLIW) digital signal processors (DSPs), there are special registers to address. To further improve the instruction-level parallelism of code for such DSPs by making full use of these registers, in this paper, we propose a more effective loop unrolling approach through extending memory accessing (LUAEMA). In this approach, the final unrolling factor is computed by a model in which every register kind and every memory accessing operation are considered. For basic digital signal processing algorithms, the unrolling factor under the LUAEMA is larger than that under the conventional loop unrolling approach. We also provide the opportunity to reduce the number of instructions in a loop during the code transformation of loop unrolling. The experimental results show that the loop unrolling approach proposed in this paper can achieve an average speedup ratio ranging from 1.14 to 1.81 compared with the conventional loop unrolling approach. For some algorithms, the peak speedup ratio is up to 2.11.

Parent-perceived autonomy-supportive experiences and basic psychological needs of people with complex support needs: Development and preliminary psychometric evaluation of two questionnaires.

Background: Understanding and supporting basic psychological needs of persons with complex support needs is important but difficult because of communicative challenges . We developed and tested questionnaires to obtain parents' perspectives on autonomy support and basic psychological needs of autonomy, competence, and relatedness. Method: Two parent-informant questionnaires were developed, administered, and subjected to psychometric property analyses. Participants were 63 Dutch parents of persons diagnosed with severe or profound intellectual and multiple disabilities. Results: Principal component analyses revealed a one-factor structure for the Parental Perceptions on Autonomy-Supportive Experiences questionnaire, while the Parental Perceptions on Basic Psychological Need Signals questionnaire yielded two-factors interpreted as Noticing Signals of Autonomy and Noticing Signals of Competence/Relatedness. Evidence for construct validity was found for both instruments. Conclusions: Preliminary evaluation of the new questionnaires is encouraging, but further validation with a larger sample size is warranted.

Design of Low Power Analog/RF Signal Processing Circuits Using 22 nm Silicon-on-Insulator Schottky Barrier Nano-Wire MOSFET

The gate-all-around (GAA) silicon-on-insulator (SOI) Schottky barrier (SB) nano-wire (NW) MOSFET was recently proposed for low-power and high-frequency analog and radio frequency (RF) circuits. But their use in low-power and high-frequency analog/RF circuits is still under investigation. In this work, basic analog signal processing circuits using gate-all-around SOI-SB-NW MOSFETs are designed for low-power and high-frequency applications. These basic and necessary analog processing circuits are designed for ± 0.3 V and ± 0.25 V power supplies to work on frequencies up to 10 GHz. The analog/RF characteristics of the GAA SOI-SB MOSFET are compared with those of the GAA SB NW MOSFET. The SOI-SB NW MOSFET shows improvements in early voltage, output resistance, and transconductance generation factor. The Silvaco TCAD simulator is used to obtain the results and perform numerical simulations. Simulation results show good analog/RF performance of the analog processing circuits.

Postabortion care service availability, readiness, and access in Burkina Faso: results from linked female-facility cross-sectional data

BackgroundLittle is known about postabortion care (PAC) services in Burkina Faso, despite PAC’s importance as an essential and life-saving component of emergency obstetric care. This study aims to evaluate PAC service availability, readiness, and accessibility in Burkina Faso.MethodsData for this study come from the Performance Monitoring for Action (PMA) Burkina Faso project and the Harmonized Health Facility Assessment (HHFA) conducted by the Institut de Recherche en Sciences de la Santé and the Ministry of Health. PMA data from a representative sample of women aged 15–49 (n = 6,385) were linked via GPS coordinates to HHFA facility data (n = 2,757), which included all public and private health facilities in Burkina Faso. We assessed readiness to provide basic and comprehensive PAC using the signal functions framework. We then calculated distance to facilities and examined percent within 5 kms of a facility with any PAC, basic PAC, and comprehensive PAC overall and by women’s background characteristics.ResultsPAC services were available in 46.4% of health facilities nationwide; only 38.3% and 35.0% of eligible facilities had all basic and comprehensive PAC signal functions, respectively. Removal of retained products of conception was the most common missing signal function for both basic and comprehensive PAC, followed by provision of any contraception (basic) or any LARC (comprehensive). Nearly 85% of women lived within 5 km of a facility providing any PAC services, while 50.5% and 17.4% lived within 5 km of a facility providing all basic PAC and all comprehensive PAC signal functions, respectively. Women with more education, greater wealth, and those living in urban areas had greater odds of living within 5 km of a facility with offering PAC, basic PAC, or comprehensive PAC.ConclusionsResults indicate a need for increased PAC availability and readiness, prioritizing basic PAC services at the primary level—the main source of care for many women—which would reduce structural disparities in access. The current deficiencies in PAC signal a need for broader strengthening of the primary healthcare services in Burkina Faso to reduce the burden of unsafe abortion-related morbidity and mortality while improving maternal health outcomes more broadly.

Comparison of Novel Telemonitoring System Using the Single-lead Electrocardiogram Patch With Conventional Telemetry System.

Although a single-lead electrocardiogram (ECG) patch may provide advantages for detecting arrhythmias in outpatient settings owing to user convenience, its comparative effectiveness for real-time telemonitoring in inpatient settings remains unclear. We aimed to compare a novel telemonitoring system using a single-lead ECG patch with a conventional telemonitoring system in an inpatient setting. This was a single-center, prospective cohort study. Patients admitted to the cardiology unit for arrhythmia treatment who required a wireless ECG telemonitoring system were enrolled. A single-lead ECG patch and conventional telemetry were applied simultaneously in hospitalized patients for over 24 hours for real-time telemonitoring. The basic ECG parameters, arrhythmia episodes, and signal loss or noise were compared between the 2 systems. Eighty participants (mean age 62±10 years, 76.3% male) were enrolled. The three most common indications for ECG telemonitoring were atrial fibrillation (66.3%), sick sinus syndrome (12.5%), and atrioventricular block (10.0%). The intra-class correlation coefficients for detecting the number of total beats, atrial and ventricular premature complexes, maximal, average, and minimal heart rates, and pauses were all over 0.9 with p values for reliability <0.001. Compared to a conventional system, a novel system demonstrated significantly lower signal noise (median 0.3% [0.1-1.6%] vs. 2.4% [1.4-3.7%], p<0.001) and fewer episodes of signal loss (median 22 [2-53] vs. 64 [22-112] episodes, p=0.002). The novel telemonitoring system using a single-lead ECG patch offers performance comparable to that of a conventional system while significantly reducing signal loss and noise. Clinical Research Information Service Identifier: KCT0008176.

Theoretical analysis of co-existing periodic orbits in sparse binary neural networks

A sparse binary neural network is a discrete-time recurrent neural network characterized by local binary connections and signum-type neuron models. The dynamics is described by an autonomous difference equation of binary state variables. Depending on the parameters, the network generates various periodic orbits of binary vectors. Multiple periodic orbits can co-exist and the network exhibits one of them depending on initial condition. Real/potential applications of the periodic orbits include control signals of switching circuits and basic signals for time-series approximation. The network is well suited for theoretical analysis and simple hardware implementation. This paper considers shift-type periodic orbits in the networks. As the main theoretical results, we clarify the number, period, and stability of the periodic orbits for key parameters. As a first step to the applications in the future, we present an FPGA-based hardware prototype and confirm typical periodic orbits experimentally.

Deep learning‐assisted reconfigurable intelligent surface for enhancing 6G mobile networks

AbstractThis article presents the role of Reconfigurable Intelligent Surface (RIS) using the Multi‐Input Multi‐Output (MIMO) technique as the enabling technology to boost the achievable data rate for mobile networks of the sixth generation (6G). The RIS has been adopted to mitigate the interference at the Cell Edge User (CEU) placed where two adjacent cells are separated. That is by reflecting the incident interference signal in the CEU direction out of phase with the basic interference signals coming from the interfering BS towards the CEU. This article adopts an efficient solution for designing the RIS redirecting (reflection) matrix with trivial training overhead using Deep Learning (DL) technology. Whereas a few of the reflecting elements in RIS are chosen to be active (attached to the baseband), whilst the majority are chosen to be passive, in which the active element's channels are known and used as medium indicators and indicate further the positions of the transmitter and receiver. This article illustrated the efficacy of the adopted framework with DL, by changing the training parameters regarding the data rates that can be achieved, the Spectral Energy Efficiency (SEE), and the Satisfaction Rate (SR). As a result, the proposed model improves the achievable data rate by a near average of 97% above the reference model. Furthermore, it enhances the achievable data rate above the baseline model that assumes no RIS used by an average of 115%. Therefore, The DL method demonstrated that the proposed model is promising for enhancing 6G Mobile Networks.

44 Develop A Social Perception Test: The Psychometric Properties of Child's version Reading the Mind in the Eyes Test in Taiwan

Objective:Expertise in social perception, defined as the ability to decode another person's mental states based on basic behavioral signals (Allison et al., 2000; Beauchamp et al., 2008). The Reading the Mind in the Eyes Test (RMET) is a social-perception task of theory of mind (ToM, Meinhardt-Injac et al., 2020) and used to test different clinical disorders, like autism spectrum disorders (ASD, Peñuelas-Calvo et al., 2019). RMET has been used to demonstrate gender, cultural, genetic, and personality trait influences on ToM and elucidate its neurobiological mechanisms (Adams et al., 2010). In Taiwan, there has few sensitive tools to evaluate children's social perception, thus the purpose of this study is to examine psychometric properties of child's version of RMET in Taiwan (RMET-C-TW) and cross-cultural comparisons.Participants and Methods:RMET-C (Baron-Cohen et al., 2001) was used to assess mental state/emotion recognition (Vellante et al., 2013). It consists of photographs of the eye regions of 28 faces. Participants were asked to make a choice between four words presented, choose the one that best described for feel or think. One point was given to each correctly response. RMET-C-TW was double-translation of words to ensure cultural applicability in Taiwan. This study included both a normative sample and criteria sample. The normative sample consisted of 769 (385 male, 384 female) 3 to 9 grades students from northern Taiwan. The normative sample completed both the RMET-C-TW and Chinese Vocabulary Test (CVT) in groups at their own schools, the CVT was to ensure that participants had sufficient vocabulary skills to understand the options provided in REMT-C-TW. The criteria sample were collected from 46 matched, school-aged children with ASD (age mean = 10.52, SD = 1.62; IQ = 108.39, SD = 11.75), and normally developing controls (age mean = 10.66, SD=1.68; IQ = 109.70, SD = 12.12). These two groups were administered the (1) WISC-III (2) CVT (3) RMET-C-TW and (4) ToM Test.Results:The results showed that RMET-C-TW had acceptable test-retest reliability and internal consistency (test-retest reliability = .71, Cronbach α= .40). There were significant gender and age difference in the performance of RMET-C-TW, example female, older participants performed better. Item analysis showed 93% of items in the RMET-C-TW had cross- cultural consistency in the distribution of respondents' choices. In criteria sample, the control group's RMET-C-TW scores significantly better than ASD group. Physician diagnosis (r = .49, p &lt; .01) and high-order ToM's scores (r = .33, p &lt; .01) were significantly associated with RMET-C-TW scores.Conclusions:RMET-C-TW has acceptable reliability and good developmental validity (age-related growth) in three to nine grades, and future can be extended to different age and clinicians to understand the development of social perception. Therefore, RMET-C-TW can be used as an initial screening and cross-cultural tool for ASD. In addition, EF is divided into cold and hot, and hot EF makes a unique contribution to ToM in ASD (Kouklari et al., 2017), thus this tool may also be used in the future to understand the association of hot EF with social perception.

Postabortion care availability, readiness, and accessibility in Niger in 2022: results from linked facility-female cross-sectional data

BackgroundPostabortion care (PAC), which is an essential element of emergency obstetric care, is underresearched in Niger. The study aims to assess the availability, readiness, and accessibility of facility-based PAC services in Niger.MethodsThis study uses female and facility data from Performance Monitoring for Action Niger. The female data include a nationally representative sample of women aged 15–49 (n = 3,696). Using GPS coordinates, these female data were linked to a sample of public and private facilities (n = 258) that are expected to provide PAC. We assessed PAC availability and facility readiness to provide basic and comprehensive PAC using the signal functions framework, overall and by facility type. We then calculated the distance between women and their closest facility and estimated the proportion of women living within five kilometers (5 km) of a facility providing any PAC, basic PAC, and comprehensive PAC, overall and by women’s background characteristics.ResultsOnly 36.4% and 14% of eligible facilities had all basic and comprehensive PAC signal functions, respectively. Oxytocics and laparotomy were the most missing signal function for basic and comprehensive PAC, respectively. Private facilities were the least ready to provide the full range of PAC services. While 47% of women lived within 5 km of a facility providing any PAC services, only 33.4% and 7.9% lived within 5 km of a facility providing all basic and all comprehensive PAC signal functions, respectively. Women who were divorced/widowed, had higher levels of education, and were living in urban areas had increased odds of living within 5 km of a facility with any or basic PAC. Women who were never married had increased odds of living within 5 km of a facility with comprehensive PAC, while urban residence was fully predictive of living within 5 km of a facility with comprehensive PAC.ConclusionsThis study found PAC availability and readiness to be insufficient in Niger, with inadequate and disparate accessibility to facilities providing PAC services. We recommended stakeholders ensure stock of essential commodities and availability of PAC services at primary facilities in order to mitigate the negative maternal health repercussions of unsafe abortion in this setting.

EEG‐based biometric authentication system using convolutional neural network for military applications

AbstractIn this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.

CAV-enabled data analytics for enhancing adaptive signal control safety environment

Given the connected and autonomous vehicle (CAV) generated trajectories as a “floating sensor” data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision.

Sound source localization method based time-domain signal feature using deep learning

Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.