Intersection Detector Research Articles

Application of the Beating Method to Define the Electroencephalogram α−Rhythm Frequency Content

Among plenty of the brain oscillation electrical patterns, the spindle oscillations in the alpharhythm band has main functional significance, in particular in emotional, memory, motivation and attention processes. At the same time, signal processing techniques well developed in radio engineering are not used in electroencephalogram (EEG) processing. For the first time the beating method was applied to electroencephalograms. It is shown that the beating method allows the determination of α−rhythm frequencies, which are not always resoluted using Furrier transform. EEG is non-stationary process that’s why instantaneous frequency is a very important characteristics of EEG. The calculation of instantaneous frequency values was carried out based on the Hilbert transform and intersection detector method. A comparative analysis of electroencephalograms frequency content obtained by Furrier transform, Hilbert transform and Zero-level intersection detector method was carried out.

Increasing the accuracy of loop detector counts using adaptive neural fuzzy inference system and genetic programming

ABSTRACTLoop detectors are devices that are most commonly used for obtaining data at intersections. Multiple detectors are usually required to monitor a location, and this reduces the accuracy of detectors for collecting traffic volumes. The purpose of this paper is to increase the accuracy of loop detector counts using Adaptive Neural Fuzzy Inference System (ANFIS) and Genetic Programming (GP) based on detector volume and occupancy. These methods do not need microscopic analysis and are easy to employ. Four approaches for one intersection are used in a case study. Results show that the models can improve intersection detector counts significantly. Results also show that ANFIS produces more accurate counts compared to regression and GP.

Traffic Flow Data Recovery Algorithm Based on Gray Residual GM(1, N) Model

Because of the basic data support and continuous motive force for the intelligent transportation systems (ITS), the quality of the raw traffic data detected from traffic sensors directly affect the follow-up benefits of the entire system. In view of the widespread failure problems of collected traffic data, the paper takes the traffic flow data of intersection detector as the research object. A traffic flow data recovery algorithm based gray residual GM(1, N) model is proposed to effectively improve the quality of traffic flow data. First, the grey relational analysis is conducted on the traffic flow of four links at an intersection. Then a grey model GM(1, N) is developed for the estimated recovery of failure data. The residual modification is used to improve the accuracy of the repaired data. The results indicate that the proposed traffic flow data recovery algorithm is feasible. It is able to solve the post-processing difficulties due to data failure and it serves as a good method for failure data recovery in other areas as well.

Multiple-symbol M-bound intersection detector for differential unitary space–time modulation

The multiple symbol differential detection (MSDD) can narrow the 3 dB gap between differential detection and coherent detection, but its complexity grows exponentially with the blocksize and also with the number of transmit antennas and the data rate. Since the current low-complexity MSDD detection still have the problems such as high complexity, difficult to parallel processing and random latency etc, this study proposes a new algorithm called M-bound intersection detector (M-BID) which derived from the BID incorporated with the M algorithm. A new bounding approach and a simplified BID combining with the M algorithm are presented in M-BID to reduce the computational complexity and obtain a relative stable latency. The numerical and simulation results show that the proposed M-BID algorithm achieves an overall better performance-complexity trade-off over the M algorithm and the BID. Moreover, it possesses the advantages of parallel processing and stable latency which are facilitated for hardware implementation.

Maximum likelihood detection for differential unitary space-time modulation with carrier frequency offset

Can conventional differential unitary space time modulation (DUSTM) be applied when there is an unknown carrier frequency offset (CFO)? This paper answers this question affirmatively and derives the necessary maximum likelihood (ML) detection rule. The asymptotic performance of the proposed ML rule is analyzed, leading to a code design criterion for DUSTM by using the modified diversity product. The resulting proposed decision rule is a new differential modulation scheme in both the temporal and spatial domains. Two sub-optimal multiple-symbol decision rules with improved performance are also proposed. For the efficient implementation of these, we derive a modified bound intersection detector (BID), a generalization of the previously derived optimal BID for the conventional DUSTM. The simulation results show that the proposed differential modulation scheme is more robust against CFO drifting than the existing double temporal differential modulation.

On Multiple Symbol Detection for Diagonal DUSTM Over Ricean Channels

This letter considers multiple symbol differential detection for multiple-antenna systems over flat Ricean-fading channels when partial channel state information (CSI) is available at the transmitter. Using the maximum likelihood (ML) principle, and assuming perfect knowledge of the channel mean, we derive the optimal multiple symbol detection (MSD) rule for diagonal differential unitary space-time modulation (DUSTM). This rule is used to develop a sphere decoding bound intersection detector (SD-BID) with low complexity. A suboptimal MSD based decision feedback DD (DF-DD) algorithm is also derived. The simulation results show that our proposed MSD algorithms reduce the error floor of conventional differential detection and that the computational complexity of these new algorithms is reasonably low.