Radio frequency identification (RFID) enables everyday objects to be identified, tracked, and recorded. The RFID tags are must be extremely simple and of low cost to be suitable for large scale application. An efficient RFID anti-collision mechanism must have low access latency and low power consumption. This paper investigates how to recognize multiple RFID tags within the reader's interrogation ranges without knowing the number of tags in advance by using framed ALOHA. To optimize power consumption and overall tag read time, a combinatory model was proposed to analyze both passive and active tags with consideration on capture effect over wireless fading channels. By using the model, the parameters on tag set estimation and frame size update were presented. Simulations were conducted to verify the analysis. In addition, we come up with a proposal to combat capture effect in deterministic anti-collision algorithms.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.

Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.

For a prime p with p≡3 (mod 4) and an odd number m, the Bentness of the p-ary binomial function fa,b(x)=Tr1n(axpm-1)+Tr12 is characterized, where n=2m, a ∈ F*pn, and b ∈ F*p2. The necessary and sufficient conditions of fa,b(x) being Bent are established respectively by an exponential sum and two sequences related to a and b. For the special case of p=3, we further characterize the Bentness of the ternary function fa,b(x) by the Hamming weight of a sequence.

This paper presents the bit error rate (BER) performance of frequency domain equalization (FDE) using cyclic-shifted code division multiplexing (CDM) pilot signals for single-carrier line-of-sight (LOS) - multiple-input multiple-output (MIMO) multiplexing. We propose applying different cyclic-shift resources of the same Zadoff-Chu sequence to transmission-stream-specific pilot signals that are essential for estimating the channel response for FDE and phase noise in LOS-MIMO. To validate the effectiveness of the cyclic-shifted pilot multiplexing, we use partial low-density parity-check (LDPC) coding with double Gray mapping and collaborative decoding. Simulations show that pilot signal multiplexing using a cyclic-shifted Zadoff-Chu sequence, and frequency domain averaging of the estimated channel response are effective in achieving accurate channel estimation for single-carrier LOS-MIMO. We also show that the required received signal-to-noise power ratio at the BER of 10-7 using partial LDPC coding is decreased by more than 6.6dB compared to that without LDPC coding even for the deep notch depth of -20dB regardless of the relationship between the notch frequencies in the direct and cross links for 2×2 LOS-MIMO in a Rummler fading channel. Therefore, we conclude that the CDM-based pilot signal multiplexing with different cyclic shifts is effective in accurately estimating the channel response specific to the combination sets of transmitter and receiver antennas and in achieving a low pilot-overhead loss for single-carrier LOS-MIMO.

Impulse ultra-wideband (UWB) is an attractive technology for large ad hoc sensor networks due to its precise ranging capacity, multi-path fading robustness and low radiation power. The transient and carrier-less nature of low radiation pulse and harsh multipath channel condition makes it cumbersome to implement carrier sensing. We proposed clear channel assessment (CCA) based on preamble-assisted modulation (PAM) for UWB sensor networks. Preamble symbols are periodically inserted into the frame payload in the time domain to serve as regular feature for reliable CCA. We simulated the CCA performance in the multipath UWB channel model developed by IEEE 802.15.4a. PAM and CCA configurations were optimized for the distributed carrier sense multiple access protocol. PAM was accepted by 802.15.4a group as an optional feature. Furthermore, the multiplexed preamble symbols can be exploited for channel estimation to improve communication and ranging.

Accurate and low-cost sensor localization is critical for deployment of wireless sensor networks. Distance between Ultra-wideband (UWB) sensor nodes can be obtained by measuring round trip flying time through two-way ranging (TWR) transaction. Because of difficulties in synchronization and channel estimate, the response delay of UWB node is the order of milliseconds. Comparing with the nanosecond propagation delay, relative clock offset between UWB nodes introduces big error in TWR. This paper presents the management of relative clock offset in TWR transaction. The relative clock offset between sensors is estimated by comparing the claimed and real frame duration. Simulation in the UWB channel model shows the relative clock offset after compensation can be reduced to less than 2 ppm.

In this paper, we propose a novel confidence measure to improve the performance of spoken term detection (STD). The proposed confidence measure is based on the context consistency between a hypothesized word and its context in a word lattice. The main contribution of this paper is to compute the context consistency by considering the uncertainty in the results of speech recognition and the effect of topic. To measure the uncertainty of the context, we employ the word occurrence probability, which is obtained through combining the overlapping hypotheses in a word posterior lattice. To handle the effect of topic, we propose a method of topic adaptation. The adaptation method firstly classifies the spoken document according to the topics and then computes the context consistency of the hypothesized word with the topic-specific measure of semantic similarity. Additionally, we apply the topic-specific measure of semantic similarity by two means, and they are performed respectively with the information of the top-1 topic and the mixture of all topics according to topic classification. The experiments conducted on the Hub-4NE Mandarin database show that both the occurrence probability of context word and the topic adaptation are effective for the confidence measure of STD. The proposed confidence measure performs better compared with the one ignoring the uncertainty of the context or the one using a non-topic method.

Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.