Time Code Research Articles

A novel differential chaos shift keying system based on joint time slot and code index of carrier phase modulation

This paper proposes a novel differential chaos shift keying (DCSK) system that utilizes carrier phase modulation in combination with time slot and code index modulation, referred to as CP-TCIM-DCSK system, to achieve high data rate transmission. In the proposed CP-TCIM-DCSK system, the carrier modulated by the carrier phase is used to transmit the information-bearing signal. In addition to physically transmitted information bits, extra information bits are conveyed through time slot and Walsh code modulation, as well as carrier phase modulation, making full use of the benefits of multidimensional modulation. To successfully estimate carrier phase bits and code index bits, an effective joint detection algorithm for carrier phase and code index tailored for the CP-TCIM-DCSK system is introduced. The theoretical Bit Error Rate (BER) expressions for the CP-TCIM-DCSK scheme are derived for both Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels. Furthermore, a comparison of data rates, energy efficiency, and complexity between the CP-TCIM-DCSK system and the most advanced systems in the same category at present is conducted. The results validate the accuracy of theoretical analysis through simulation and demonstrate that the proposed scheme outperforms its competitive systems in terms of BER performance.

Radar Target Complex High-Resolution Range Profile Modulation by External Time Coding Metasurface

Radar Target Complex High-Resolution Range Profile Modulation by External Time Coding Metasurface

Postpartum Emergency Care Visits Among North Carolina Medicaid Beneficiaries, 2013-2019.

Objective: To describe the rate, timing, and primary diagnosis codes for emergency care visits up to 8 weeks (56 days) after live birth among Medicaid beneficiaries in North Carolina (NC). Materials and Methods: Using a linked dataset of Medicaid hospital claims and certificates of live birth, which included Medicaid beneficiaries who had a live-born infant in NC between January 1, 2013, and November 4, 2019, and met inclusion criteria (n = 321,879), we estimated week-specific visit rates for emergency care visits that did not result in hospital admission (outpatient) and those that did (inpatient). We assessed the 10 leading diagnosis code categories for emergency care visits and described the characteristics of people with 0, 1, or ≥2 outpatient emergency care visits. Results: One in eight (12.4%) Medicaid beneficiaries had an emergency care visit that did not result in inpatient hospital admission during the first 8 weeks postpartum. Visit rates peaked in postpartum week 2. Diagnosis codes for nonspecific symptoms and substance use were the two leading diagnosis code categories for outpatient emergency care visits. Respiratory concerns and gastrointestinal concerns were the two leading diagnosis code categories for inpatient emergency care visits. Compared with those with zero outpatient emergency care visits, a greater proportion of people with ≥2 visits had less than a high school education, used tobacco during pregnancy, had Medicaid insurance outside of pregnancy, had mental health as a medical comorbidity, and/or had ≥2 medical comorbidities. Conclusions: These findings support scheduling health care visits early in the postpartum period, when emergency care visits are most frequent, and point to unmet needs for substance use support.

Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders.

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

Real Time Collaborative Code Editor

Abstract: The main goal of our project is to provide an online platform especially for group coding interviews. This platform provides a comprehensive environment for seamless communication between interviewers and candidates, with the goal of addressing the challenges faced during remote technical assessments. The creation ofa feature-rich web platform is the goal of our project. Thisincludes an adaptable rich text editor that enables coding in real time, a mechanism for instantaneous code evaluation via execution and compilation, integrated video conferencing for efficient communication, and the abilityto set up interview rooms to hold several interviews at once. We're designing a user intuitive interface using technology and strong programming frameworks. The main objectives of the platform are to ensure real time code execution and a seamless interview process. This is achieved by incorporating code compilation engines implementing editing tools and utilizing WebRTC for video conferencing. By creating a user-friendly and productive environment that benefits both interviewers and candidates, our platform seeks to expedite the remote technical interview process. This means reducing logistical obstacles, making thorough coding evaluations possible, and creating a positive interview environment.

Opportunistic Interference Alignment in Cognitive Radio Networks with Space–Time Coding

Opportunistic Interference Alignment in Cognitive Radio Networks with Space–Time Coding

A New Validated Approach for Identifying Childhood Immunizations in Electronic Health Records in the United Kingdom.

Routinely collected electronic health records (EHR) offer a valuable opportunity to carry out research on immunization uptake, effectiveness, and safety, using large and representative samples of the population. In contrast to other drugs, vaccines do not require electronic prescription in many settings, which may lead to ambiguous coding of vaccination status and timing. We propose a comprehensive algorithm to identifying childhood immunizations in routinely collected EHR. In order to deal with ambiguous coding, over-recording, and backdating in EHR, we suggest an approach combining a wide range of medical codes in combination to identify vaccination events and using appropriate wash-out periods and quality checks. We illustrate this approach on a cohort of children born between 2006 and 2014 followed up to the age of five in the Clinical Practice Research Datalink (CPRD) Aurum, a UK primary care dataset of EHR, and validate the results against national estimates of vaccine coverage by NHS Digital and Public Health England. Our algorithm reproduced estimates of vaccination coverage, which are comparable to official national estimates and allows to approximate the age at vaccination. Electronic prescription data only do not cover vaccination events sufficiently. Our new proposed method could be used to provide a more accurate estimation of vaccination coverage and timing of vaccination for researchers and policymakers using EHR. As with all observational research using real-world data, it is important that researchers understand the context of the used dataset used and the clinical practice of recording.

ABDGAN: Arbitrary Time Blur Decomposition Using Critic-Guided TripleGAN.

Recent studies have proposed methods for extracting latent sharp frames from a single blurred image. However, these methods still suffer from limitations in restoring satisfactory images. In addition, most existing methods are limited to decomposing a blurred image into sharp frames with a fixed frame rate. To address these problems, we present an Arbitrary Time Blur Decomposition Triple Generative Adversarial Network (ABDGAN) that restores sharp frames with flexible frame rates. Our framework plays a min-max game consisting of a generator, a discriminator, and a time-code predictor. The generator serves as a time-conditional deblurring network, while the discriminator and the label predictor provide feedback to the generator on producing realistic and sharp image depending on given time code. To provide adequate feedback for the generator, we propose a critic-guided (CG) loss by collaboration of the discriminator and time-code predictor. We also propose a pairwise order-consistency (POC) loss to ensure that each pixel in a predicted image consistently corresponds to the same ground-truth frame. Extensive experiments show that our method outperforms previously reported methods in both qualitative and quantitative evaluations. Compared to the best competitor, the proposed ABDGAN improves PSNR, SSIM, and LPIPS on the GoPro test set by 16.67%, 9.16%, and 36.61%, respectively. For the B-Aist++ test set, our method shows improvements of 6.99%, 2.38%, and 17.05% in PSNR, SSIM, and LPIPS, respectively, compared to the best competitive method.

Groundwater contamination modelling in Ayad River Basin, Udaipur

Groundwater, a vital freshwater resource catering to agricultural, domestic, and industrial needs, faces a pressing challenge of contamination due to escalating human activities. This study focuses on the Ayad River Basin in the Udaipur district of Rajasthan, employing the FEFLOW simulation code for the first time. A steady-state numerical model and a groundwater contaminant prediction model for total dissolved solids (TDS), nitrate, and fluoride were developed, simulating trends over the next five years with an accuracy exceeding 95%. The results reveal an eastward increase in TDS, nitrate, and fluoride concentrations, attributed to contamination from two waste disposal sites-Titadi and Baleecha. Titadi, operational for four decades until closure in 2010, retains residual waste over 32 thousand m2. The initiation of a new dumping ground at Baleecha by the Udaipur Municipal Corporation post-2010 exacerbates regional contamination. Nitrate contamination is particularly high in agricultural zones with excessive chemical fertilizer usage. Of the 27 scenarios tested, 23 support using the water for irrigation but would require treatment before using it for drinking. Recommendations include deploying a chemical sensor network for real-time data input into the web enabled FEFLOW model, real-time monitoring and alerts, and a mobile application providing personalized guidance on water usage and health risks in case of contamination. This study can be beneficial to decision-makers, who work on the policy and groundwater management strategies.

Working memory involvement in action planning does not include timing initiation structure.

A fundamental limitation in the type of information that can be retained in working memory is identified in this theoretical / review article. The analysis is based on studies of skilled motor performance that were not initially conceived in terms of working memory. Findings from a long history of experimentation involving reaction time (RT) prior to making a brief motor response indicate that although the parameters representing the goal to be achieved by the response can be retained in working memory, the control code that implements timing of action components cannot. This lack of working memory requires that the "timing code" must be compiled immediately prior to the moment that it is to be utilized; it is not possible to be fully ready to respond earlier. This compiling process increases RT and may also underlie both the psychological refractory period effect and the difficulty of generating concurrent motor actions with independent timing. These conclusions extend, but do not conflict with, other models of working memory.

Reconstruction algorithm of Gm-APD LiDAR based on synchronized pseudo-random time coding

Efficient photon counting imaging of long-range targets is challenging, especially in low signal-to-back ratio (SBR) situations. In this paper, a synchronized pseudo-random time coding detection method for Geiger-mode avalanche photodiode (Gm-APD) light detection and ranging (LiDAR) is proposed, which realizes Gm-APD LiDAR long-range detection by combining pseudo-random time coding and time of flight, and the multiple peaks are used to de-noise the image at the image level. Scenes of building with different SBR are simulated with Monte Carlo methods, and the effect of coding length on algorithm performance is discussed and compared with the performance of non-coding algorithms. The high performance of the proposed algorithm is verified by field experiments in low SBR scenes. Moreover, the proposed algorithm improves the target reconstruction correctness ratio by 70% over the other non-coding algorithms to 83% in the scene of building with SBR of 0.015, and has the smallest root mean square error (RMSE) of the distance. It improves the target integrity and provides the possibility of practical application of LiDAR target detection in very low SBR and long-range conditions, and even more support for target feature extraction and target recognition.

Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring the multifaceted properties of novel oxide-based perovskites ABO3 (A=Nd and B[dbnd]Lr, Y): A DFT study

The full-potential linear augmented plane wave with local orbital (FP-LAPW) technique is now used in this approach to better understand the structural, electronic, optical and mechanical properties of simple cubic oxide perovskite. NdLrO3 and NdYO3 compounds are studied by employing the density functional theory (DFT) with CASTEP code for the first time. The lattice parameters are found 4.2462 and 4.2301 Å for NdLrO3 and NdYO3 respectively. Both the compounds have 1.9 eV and 1.4 eV band gap showing the direct band gap nature of both materials. The exchange-correlation (XC) energy has been selected during this investigation with the Local Density Approximation (LDA) approach. We optimize as well as clarify the elastic constants Cij, the bulk modulus B, elasticity modulus G, Young's modulus of stretch Y, and the Poisson ratio v. Every substance shows anisotropic behavior. NdLrO3 and NdYO3 compounds exhibit a direct band-gap nature, as revealed by the electronic band structure computations. These compounds were all classified as semiconductors. To quantify the number of localized electrons in various bands, partial density of states (PDOS) and total density of states (TDOS) were deployed. Both compounds are computed by fitting the dispersion relation of the imagined component. Optical properties showed that these compounds were excellent absorbers of incident radiation. Therefore, it may be assumed that these compounds could be employed in magnetic sensors because of anisotropic magnetic behavior and to capture the ultraviolet range of solar radiations.

Enhancing Underwater Wireless Sensor Networks With Flexible Communication and Positioning

An innovative approach centred around underwater wireless communication and positioning in Underwater Wireless Sensor Networks, addressing limited coverage by proposing a dual-hop system combining optical fiber and wireless links. Time frame design and Code Division Multiplexing Access enhance multi-user signal transmission efficiency. Proof-of-concept experiments validate feasibility, with the introduction of the hybrid fish eye routing protocol further enhancing performance by intelligently combining fish-eye routing and traditional protocols for improved coverage and reliability. Moreover, the hybrid fish eye protocol incorporates adaptive routing algorithms that dynamically adjust to changes in underwater conditions, ensuring robustness and adaptability in challenging environments. Additionally, the hybrid fish eye protocol optimizes energy consumption by minimizing unnecessary transmissions, thereby prolonging the lifespan of underwater sensor networks. With full-duplex communication, precise positioning, and an extended transmission range, convergent system demonstrates significant potential for supporting high-capacity transmission among mobile nodes in forthcoming underwater scenarios

The time code of online consumption: Exploring the impact of work hours on online consumption in the retail industry

The growth of e-commerce has led to a rise in online consumption, while the online environment usually muddles consumers’ sense of time, which easily brings about rising on time during consumption. Building on the Beckerian model of consumption, we find that increasing work hours may constrain employees’ online consumption. Using 15,529 employee samples from China Family Panel Studies (CFPS), this paper examines the effect of work hours on consumers’ online consumption in the retail industry. The IV-Heckman method and the stimulus-organism-response (S–O-R) framework have been applied. Empirical findings show that increasing work hours significantly reduces employees’ online consumption. This study reveals the causal effect of work hours on online consumption both theoretically and empirically.

Multi-laser source interference suppression using the time-coding method for SPAD-based flash DToF systems

Flash-type direct time-of-flight (DToF) image sensors use an in-pixel successive approximation register time-to-digital converter (SAR TDC) for time quantization. However, in a scene where multiple DToF systems exist simultaneously, different laser signals from multiple sources will produce mutual signal interference between DToF systems, causing the DToF system’s incorrect measurement. In this paper, we present a method called time coding, which inserts delay time bins between different working periods to suppress the interference laser together with the SAR TDC. The time-coding method is designed using a 110 nm complementary metal oxide semiconductor (CMOS) technology and verified by behavioral model and circuit simulation. Regardless of traditional systems or systems equipped with time coding, DToF systems with certain patterns of time coding can reduce interference noise by at least 95%, maintaining a measurement accuracy of 99% or higher at long distances.

Single Spike Neural Network Model for Superficial Environment Classification for Mobile Robot Navigation

In this work, a single Semi-Recurrent Spike Neural Network (SRSNN) supervised learning method based on time coding is proposed to classify visual terrain encountered by the mobile robot. To this end, the features are extracted using the Local Binary Pattern (LBP) method. Then, the SRSNN is trained to classify multi-class of terrains. This training is used to classify six classes of terrain: hydrop, gravel, asphalt, grass, mud, and sand. The proposed training algorithm is based on adaptive synaptic weights that reach the threshold value. The feature extracted method and SRSNN form the proposed Intelligent structure. This structure effectively evaluates the accuracy, precision, recall, and F1 score. The simulation results achieve good performance in minimizing the mean square error in the training phase and maximizing the overall accuracy to 87.22%, especially in dangerous terrain (i.e. hydrop). The effectiveness of the proposed model is proved by the efficiency of the training algorithm which can learn fast with accurate results.

Are we missing an opportunity? Prehospital delay in patients with acute ischemic stroke and known atrial fibrillation

Introduction and ObjectivesThe follow-up of patients with atrial fibrillation (AF) presents an opportunity to alert patients and their families on how to recognize and act in the event of stroke. Our aim was to compare stroke recognition-to-door time and prehospital stroke code activation in patients with known AF (KAF) and AF detected after stroke (AFDAS). MethodsWe performed a retrospective cohort study of consecutive patients receiving acute recanalization treatment for acute ischemic stroke between January 2016 and August 2022, with AF as a potential stroke cause. Patients were divided into KAF and AFDAS, and stroke recognition-to-door time and prehospital stroke code activation were compared. In the KAF subgroup, we assessed whether the use of preadmission anticoagulation was associated with the studied prehospital parameters. ResultsWe included 438 patients, 290 female (66.2%), mean age 79.3±9.4 years. In total, 238 patients had KAF (54.3%) and 200 (45.7%) had AFDAS. Of those with KAF, 114 (48.1%) were pretreated with anticoagulation. Patients with KAF and AFDAS had no differences in stroke recognition-to-door time (74.0 [55.0–101.0] vs. 78.0 [60.0–112.0] min; p=0.097) or prehospital stroke code activation [148 (64.6%) vs. 128 (65.3%); p=0.965]. In the KAF subgroup, preadmission anticoagulation did not influence stroke recognition-to-door time or mode of hospital admission. ConclusionStroke recognition-to-door time and prehospital stroke code activation were similar between patients with known or newly diagnosed AF. Preadmission anticoagulation treatment also did not affect the studied parameters. Our findings highlight a missed opportunity to promote stroke knowledge in patients followed due to AF.

Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs.

Purkinje cell (PC) synapses onto cerebellar nuclei (CbN) neurons allow signals from the cerebellar cortex to influence the rest of the brain. PCs are inhibitory neurons that spontaneously fire at high rates, and many PC inputs are thought to converge onto each CbN neuron to suppress its firing. It has been proposed that PCs convey information using a rate code, a synchrony and timing code, or both. The influence of PCs on CbN neuron firing was primarily examined for the combined effects of many PC inputs with comparable strengths, and the influence of individual PC inputs has not been extensively studied. Here, we find that single PC to CbN synapses are highly variable in size, and using dynamic clamp and modeling we reveal that this has important implications for PC-CbN transmission. Individual PC inputs regulate both the rate and timing of CbN firing. Large PC inputs strongly influence CbN firing rates and transiently eliminate CbN firing for several milliseconds. Remarkably, the refractory period of PCs leads to a brief elevation of CbN firing prior to suppression. Thus, individual PC-CbN synapses are suited to concurrently convey rate codes and generate precisely timed responses in CbN neurons. Either synchronous firing or synchronous pauses of PCs promote CbN neuron firing on rapid time scales for nonuniform inputs, but less effectively than for uniform inputs. This is a secondary consequence of variable input sizes elevating the baseline firing rates of CbN neurons by increasing the variability of the inhibitory conductance. These findings may generalize to other brain regions with highly variable inhibitory synapse sizes.

Towards automatically identifying the co‐change of production and test code

AbstractIn software evolution, keeping the test code co‐change with the production code is important, because the outdated test code may not work and is ineffective in revealing faults in the production code. However, due to the tight development time, the production and test code may not be co‐changed immediately by developers. For example, we analysed the top 1003 popular Java projects on GitHub and found that nearly 9.3% of cases (i.e., 464,417) did not update their production and test code at the same time, that is, the production code is updated first, and then the test code is updated at intervals. The result indicates that much test code will not be updated in time. In this paper, we propose a novel approach, Jtup, to remind developers to co‐change the production code and test code in time. Specifically, we first define the co‐changed production and test code as a positive instance, while unchanged test code (i.e., production code changed and test code unchanged) as a negative instance. Then, we extract multidimensional features from the production code to characterize the possibility of their co‐change, including code change features, code complexity features, and code semantic features. Finally, several machine learning‐based methods are employed to identify the co‐changed production and test code. We conduct comprehensive experiments on 20 datasets, and the results show that the Accuracy, Precision, and Recall achieved by Jtup are 76.7%, 78.1%, and 77.4%, which outperforms the state‐of‐the‐art method.