Path Delay Research Articles

CASTELO: Convex Approximation based Solution To Elliptic Localization with Outliers

This short communication considers mitigating the negative effects of possibly unreliable path delay measurements acquired in non-line-of-sight (NLOS) environments on the positioning performance, a problem deserving further investigation within the expanding research area of elliptic localization. We present CASTELO, a Convex Approximation based Solution To Elliptic Localization with Outliers, to achieve such a goal. Our proposal corresponds to a mixed semidefinite (SD)/second-order cone (SOC) programming formulation derived from an error-mitigated nonlinear least squares (LS) location estimator, presenting itself as a remedy for the neglect of positivity of NLOS biases suffered by the majority of currently fashionable outlier-handling approaches. In terms of analytical discussions, we provide rationales supporting the incorporation of the SOC constraints, which serve to tighten the problem obtained after SD relaxation, and conduct a complexity analysis for the ultimate mixed SD/SOC programming formulation. Simulations are carried out to confirm the strong ability of CASTELO to attain reliable elliptic localization in the presence of NLOS outliers.

A Novel Adaptive ANC Algorithm for Removal of Background Noise in Speech Applications

Noise is an unsafe mechanical toxin that causes serious hearing misfortune in the working environment of every nation. The working people in the military, mining, development, printing, and saw factories tend to lose their hearing performance due to the adverse effects of noise generated by the machines. They undergo elevated levels of noise, with various machinery producing greater levels of noise measured in decibels. These sounds may cause major health problems that may not allow the person to work in such conditions. Algorithms like Least Mean Square (LMS), Normalized Least Mean Squared (NLMS), Filtered-x Least Mean Squared (FxLMS) and Filtered-x Normalized Least Mean Squared (FxNLMS) are frequently being used for noise cancellation. Moreover, these filters have instability and poor noise reduction; slow convergence also requires a greater number of filter taps and less performance to identify the unknown system in the Active Noise Canceller (ANC). In this paper, a Précised FxNLMS (P-FxNLMS) algorithm is introduced for an ANC. This algorithm consists of dual adaptive filters, an updated Variable Step Size (VSS), a delay in the primary path, a slight improvement in the on-line secondary path, and a modified filter step size when compared to an existing ANC system, with the purpose of minimizing the demerits of existing algorithms. Initially, the P-FxNLMS algorithm was tested with Additive White Gaussian Noise (AWGN) and later tested with real noises from the NOISEUS dataset to check the noise reduction performance. The increase in Signal to Noise Ratio (SNR) segmentation for P-FxNLMS is around 1.45 dB to 4.07 dB and 38.46 % to 73.68 % of the Mean Square Error (MSE) as compared to the algorithms available for different sounds with different SNR input levels. From the performance results of MSE and SNR improvement (SNRi), we found improvements compared with existing algorithms

Characteristic Analysis and Modeling of Train‐to‐Train Wireless Channel in Station Scenario

In this paper, ray tracing (RT) is used to analyze the characteristics of train‐to‐train (T2T) wireless channel in 2.1 GHz frequency band, including path loss, multipath clustering, delay spread, Rice factor, angular spread, and channel nonstationarity. It is found that the change of environment has a great influence on the characteristics of the wireless channel, which not only causes the birth and death of multipath clusters but also makes the channel appear nonstationary characteristics. In order to characterize the nonstationarity of the T2T wireless channel, we introduce the time correlation coefficient (TPCC) of the power delay profile (PDP), analyze the nonstationarity of the T2T wireless channel, and give the quasi‐stationarity distance. The results are basically consistent with the measured data.

A novel multipath QUIC protocol with minimized flow complete time for internet content distribution

The rapid growth of network services and applications has led to an exponential increase in data flows on the internet. Given the dynamic nature of data traffic in the realm of internet content distribution, traditional TCP/IP network systems often struggle to guarantee reliable network resource utilization and management. The recent advancement of the Quick UDP Internet Connect (QUIC) protocol equips media transfer applications with essential features, including structured flowcontrolled streams, quick connection establishment, and seamless network path migration. These features are vital for ensuring the efficiency and reliability of network performance and resource utilization, especially when network hosts transmit data flows over end-to-end paths between two endpoints. QUIC greatly improves media transfer performance by reducing both connection setup time and transmission latency. However, it is still constrained by the limitations of single-path bandwidth capacity and its variability. To address this inherent limitation, recent research has delved into the concept of multipath QUIC, which utilizes multiple network paths to transmit data flows concurrently. The benefits of multipath QUIC are twofold: it boosts the overall bandwidth capacity and mitigates flow congestion issues that might plague individual paths. However, many previous studies have depended on basic scheduling policies, like round-robin or shortest-time-first, to distribute data transmission across multiple paths. These policies often overlook the subtle characteristics of network paths, leading to increased link congestion and transmission costs. In this paper, we introduce a novel multipath QUIC strategy aimed at minimizing flow completion time while taking into account both path delay and packet loss rate. Experimental results demonstrate the superiority of our proposed method compared to standard QUIC, Lowest-RTT-First (LRF) QUIC, and Pluginized QUIC schemes. The relative performance underscores the efficacy of our design in achieving efficient and reliable data transfer in real-world scenarios using the Mininet simulator.

Digit-Serial DA-Based Fixed-Point RNNs: A Unified Approach for Enhancing Architectural Efficiency.

The next crucial step in artificial intelligence involves integrating neural network models into embedded and mobile systems. This requires designing compact and energy-efficient neural network models in silicon for optimized performance. This article introduces a unified approach for enhancing the architectural efficiency of long short-term memory (LSTM) recurrent neural networks (RNNs). Precisely, two new structures (I and II) based on the two's complement (TC) digit-serial distributed arithmetic (DSDA) technique are presented. The block-circulant matrix-vector multiplications (MVMs) and element-wise multiplications (EWMs) are formulated using TC DSDA. In addition, a fixed-point (FxP) training procedure for quantized LSTM RNNs is considered and validated for speech recognition tasks. Both structures leverage the circular rotation of weights and generate partial products with input digit slices. A new partial-product generator (PPG) and partial-product selector (PPS) designed to work with both unsigned and signed digits is introduced. In Structure I, a nonpipelined MVM is realized with a few PPGs and PPSs, followed by a shift-accumulate unit (SAU). Conversely, in Structure II, a suitably chosen depth-pipelined MVM is achieved with multiple PPGs and PPSs, followed by a shift-to-add tree (SAT). A critical path delay (CPD) analysis for both the proposed structures is also presented. Compared with previous works, post-synthesis results on 28 -nm fully depleted silicon-on-insulator (FDSOI) technology reveal that for a model size of 128 × 128 , Structures I and II provide 39.87% , 95.63% , and 30.95% , 91.18% more area and energy efficiencies, respectively.

Impact Analysis of Global Warming on Wet Path Delay Over the Satellite Altimetry Era (1993–2022)

Impact Analysis of Global Warming on Wet Path Delay Over the Satellite Altimetry Era (1993–2022)

Weak and Strong Non-Robust Tests for Functionally Possible Path Delay Faults

Weak and Strong Non-Robust Tests for Functionally Possible Path Delay Faults

Analysis of Parallel Prefix Adders

Abstract: Parallel prefix adders are essential components of contemporary digital arithmetic circuits and are found in many different devices, including digital signal processors and microprocessors. In order to improve the effectiveness and performance of parallel prefix adders, this research investigates their design and optimization. This paper analyses in detail the state of the parallel prefix adder architectures and provide new designs that minimize power consumption and critical path delays. The speed and area efficiency benefits of the parallel prefix designs with thorough simulations and comparisons is reviewed. This research have ramifications for high-performance computing systems and point to interesting avenues to further the state of the art in parallel prefix adder design.

Logical Resolving-Based Methodology for Efficient Reliability Analysis.

With the CMOS technology downscaling to the deep nanoscale, the aging effects of devices degrade circuit performance and even lead to functional failure. The stress analysis is critical to evaluate the influence of aging effects on digital circuits. Some related analytical work has recently focused on reliability-aware circuit analysis. Nevertheless, the aging dependence among different devices is not considered, which will induce errors of degradation evaluation in the digital circuit. In order to improve the accuracy of reliability-aware static timing analysis, an improved analytical method is proposed by employing logical resolving. Experimental results show that the proposed method has a better evaluation accuracy of aging path delay than traditional strategies. For aging timing evaluation on aging paths, excessive pessimism can be reduced by employing the proposed method. And, a 378× speedup is achieved while having a 0.56% relative error compared with precise SPICE simulation. Moreover, the circuit performance sacrifice of an aging-aware synthesis flow with the proposed method can be decreased. Due to the high efficiency and high accuracy, the proposed method can meet the speed demands of large-scale digital circuit reliability analysis while achieving transistor simulation accuracy.

A novel intelligent approach for flight delay prediction

Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.

Spectrum-Aware Transitive On-Demand Routing Protocol for Military Cognitive Radio Ad Hoc Networks

The advancement of wireless technology is increasing the demand for scarce spectrum. Cognitive radio ad hoc networks (CRAHNs) were proposed as a solution to spectrum scarcity which is also deployed in combat. However, military cognitive radio ad-hoc networks (MCRAHNs) are subject to destruction and frequent link breakages. The challenge with MCRAHNs routing is the timing-out of packets. This paper proposes the spectrum-aware transitive multi-cast on-demand distance vector (SAT-MAODV), optimised for throughput and delay. The relay nodes are selected based on zonal data and mobility. This is achieved through handshaking and sharing of location data. The nodes are expected to store location data of nodes encountered, which is used in routing and in determining the movement of the node. The mobility of military nodes is organised and structured, which simplifies routing. The SAT-MAODV was evaluated in network simulator 2 and the results show that the scheme is effective. UsingSAT-MAODV instead of xWCETT reduced routing path and node relay delay by at least 65% and 13% respectively, and increased achievable throughput by 31%. It also improved the delivery ratio by 9% while reducing latency by 27% in comparison with MARSA.

On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation

The HY-2D satellite was successfully launched in 2022, which marks the first phase of the HY-2 satellite constellation. In order to reduce the deviation of wet path delay (WPD) between different satellites in the HY-2 satellite constellation and increase precision in the correction microwave radiometer (CMR) products, on-orbit calibration must be performed to the brightness temperature (BT) of the CMR in this constellation. This study describes the principle and process of on-orbit calibration for CMR in detail. For the three satellites of the HY-2 satellite constellation, after cross-matching with each other within a limited spatio-temporal range, the HY-2B satellite with sounding on the global ocean is selected to the calibration source, calibrating BT from the CMR of the HY-2C and HY-2D satellites to the BT dimension of the HY-2B satellite CMR. To check on-orbit calibration, a retrieval algorithm is built using atmospheric profile data from ECMWF and BT data, obtained from the CMR of the HY-2B satellite; this is used to calculate the atmospheric water vapor (AWV) and WPD from the HY-2 satellite constellation. After on-orbit calibration to the CMRs of the HY-2 satellite constellation, the deviation between the CMR products of different satellites is significantly reduced by over 20%, and the RMS of WPD for the same type of products from the Jason-3 satellite is less than 1 cm. It may be concluded that on-orbit calibration improves the accuracy of AWV and WPD by normalizing the BT dimension for CMRs of the HY-2 satellite constellation, so this calibration method is effective and credible for enhancing the quality of altimeter products in the HY-2 satellite constellation.

An optimized FPGA architecture for machine learning applications

FPGAs are currently the most suitable hardware accelerators to implement and accommodate the non-stop growth of machine learning applications. This paper presents an FPGA architecture with added posit multipliers that outweigh the current IEEE-754 multipliers in terms of delay and area. Since machine learning algorithms involve a lot of expensive mathematical operations, having such powerful multipliers in the proposed FPGA architecture will execute the needed operations with high efficiency, which will stand out for machine learning applications without compromising other FPGA applications. Experimental results using Verilog to Routing (VTR) on both machine learning and non-machine learning benchmarks have demonstrated that our proposed architecture consumes 10% less area than the Stratix IV FPGA. Furthermore, it consumes less power compared to both the Stratix IV and Stratix 10 FPGAs, with reductions of 22% and 10%, respectively. Additionally, our architecture outperforms the Stratix IV and Stratix 10 FPGAs in terms of critical path delay, with improvements of 25.5% and 43%, respectively.

Novel Architecture of Fir Lattice Filter with Reduced Interconnect Delay Impacts Towards Reduced Power-delay Product using Pipelining and Parallel Processing for Medical Applications

Three important parameters area, speed and power are important. In VLSI design, the speed is determined by the critical path delay. Delay is depending on the data path taken for processing in the VLSI design. By minimizing data-path delay using efficient architecture the speed of the design of system can be improved in turn to result in better performance. In this new era, data-path delay is the dominating one compared to logic delay. So, it is very essential to concentrate more towards path delay in any architecture design. Even though the speed is important it is having in trade of with power. This data-path delay can be reduced by different technique like pipelining, parallel processing, register retiming. Also, by constructing this architecture in an innovative way the power is having trade of with delay. So the power-delay product can be taken as the reduced one. In this paper, various architecture of delay-product optimized FIR filter and Lattice filter architecture is being surveyed and implemented in order to get minimum delay-power product

The Impact of Charge-Neutral Atmospheric Propagation Path on the Altimetry Performance of Interferometric Radar Altimeter

Abstract The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA’s performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA’s swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors.

BPath-RO: A Performance- and Area-Efficient In Situ Delay Measurement Scheme for Digital IC

Circuit delays are increasingly sensitive to process, voltage, temperature, and aging (PVTA) variations, severely impacting circuit performance. Accurate measurement of circuit delay is essential. However, the additional hardware structures for measuring circuit delay add to the critical path delay. To address this issue, this paper proposes a bypass-based ring oscillator (BPath-RO) that reduces the impact on the critical path delay by moving the added measurement control structures to the bypass. The proposed measurement scheme requires only two transistors inserted into the critical path, which is more conducive to engineering change order (ECO). Measurement simulation experiments were performed on representative critical paths of the ISCAS’89 s298 and ITC’99 b15 benchmark circuits. The experimental results show that, in comparison with the existing architectures, the Bpath-RO delay measurement scheme improves the circuit performance by an average of 13.81% (s298) and 3.47% (b15) and reduces the hardware overhead by up to 70% for each path.

An analysis of a feedback active noise control system using the remote microphone technique

This paper examines strategies in virtual sensing feedback active noise control systems and compares the noise reduction performance of three strategies: using physical microphones for direct feedback control, using virtual microphones for direct feedback control, and using physical microphones for indirect virtual feedback control. For a primary sound field with incident waves in a single direction, simulation results indicate that employing a physical microphone for indirect virtual feedback control consistently yields superior noise reduction performance than that using a physical microphone for direct feedback control. However, if the causal requirement between physical and virtual microphones is not satisfied, the increase in the equivalent secondary path delay deteriorates noise reduction performance while utilizing physical microphones for indirect virtual feedback control. For a primary sound field containing waves from multiple directions, the simulation results show that the noise reduction performance is affected by both the coherence and causality between the physical and virtual microphone signals.

Adaptive analog and digital active noise control

Digital adaptive controllers are widely used for active noise control. The secondary path delay, including the anti-aliasing, sampling, and reconstruction effects, must be shorter than that in the primary path to maintain good broadband performance. To eliminate the added delay of the sampling, a mixed analog and digital adaptive feedforward controller is developed. The analog controller is based on a state-filtered adaptive linear combiner, while the digital one uses an adaptive finite-impulse-response filter. The adaptation of the analog controller is derived based on a sampled version of the normalized projection algorithm, while the digital controller adaptation is based on the filtered-reference least-mean-squares algorithm. Two configurations are considered for the analog combiner. One aims to minimize the electrical noise by introducing individual state filters, all driven by the same reference signal. The other gives a superior bandwidth by driving each component filter with the previous filter output. The performance of the proposed controller is assessed and compared with separate analog or digital controllers. The results highlight that the suggested controller obtains significant attenuation levels close to the causality limit using the analog controller but also allows long impulse responses using the sampled controller.

An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation

Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramér–Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.

Stability optimization of time-delay systems with zero-location constraints applied to non-collocated vibration suppression

We present an active control method for achieving non-collocated vibration absorption, implemented on a lumped mass system. The task of suppressing vibrations at a number of frequencies, at a target mass is recast into a problem of assigning zeros of the transfer function from the external excitation force to the position of the target mass. The controller design involves computing the controller parameters by utilizing the available output feedback to directly assign a set of zeros on the imaginary axis while simultaneously preserving the overall stability with a sufficient margin. The design also takes into account delays in the feedback path. The presence of delays leads to an infinite dimensional system for which, there exists in general, infinitely many characteristic roots. The requirement of achieving sufficient damping in the resulting closed-loop system together with suppressing vibrations of the desired frequency leads to a constrained optimization problem of minimizing the spectral abscissa function subject to zero-location constraints. These constraints exhibit polynomial dependence on the controller parameters. We present two approaches for controller parameterization for the single and multiple input case. Both approaches are based on constraint elimination and exploit the property that the constraints are affine in a selection of the gain parameters. Finally, the methodology is verified by simulating on a spring–mass–damper system following which, it is then validated experimentally on a laboratory setup.