In an ultrahigh frequency (UHF) radio frequency identification (RFID) system, the throughput can be greatly improved by collision resolution on a physical layer when tags collide, and high‐performance coding technology can improve the bit error rate (BER) performance of the physical‐layer separation. Most of the traditional physical collision resolutions focus on the code with a single subcarrier. This paper pays more attention to Miller code with multiple subcarriers and proposes a novel physical‐layer separation method based on the Miller code. In this method, the separated collision signals are multiplied by clock signals with the same frequency as the subcarrier to complete the frequency shift. And then, a coherent demodulation and a low‐pass filter are used to remove high‐frequency separation noise. In the simulation, the Miller code with more subcarriers has lower BER than FM0 code with a single carrier. Especially when Miller 8 is selected, the separation efficiency and BER performance of the proposed method are 4 dB higher than those of the traditional XOR method at lower SNR.

Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introduces a state-space model with confounds, from a conventional hemodynamic model. In this model, a successive state estimation method requires a state value vector, an error covariance, an innovation covariance, and a cross covariance to be re-derived. Thus, a confounds square-root cubature Kalman smoothing (CSCKS) algorithm is proposed in this paper. We use a Balloon-Windkessel model to generate simulation data and add confounds signals to evaluate the performance of the proposed algorithm. The experiment results show that when the signal-to-interference ratio is less than 21 dB, the CSCKS proposed in this paper reduced estimation error to 16%, whereas the traditional algorithm reduced it to only 73%.

How to estimate the number of passive radio frequency identification (RFID) tags and the occurrence probability of capture effect is very important for a dynamic frame length Aloha RFID system with capture effect. The estimation would relate to setting an optimal frame length, which makes tag identification achieve higher efficiency. Under large-scale tags identification environment, the number of tags may be much greater than an initial frame length. In this scenario, existing estimates do not work well. In this letter, we propose a novel estimation method for the large-scale tags identification. The proposed method could adjust the initial frame length matched to the number of tags from only the first several slots in the frame. The advantage of the proposed method is to work better even when the number of tags is much greater. Numerical results show that, the proposed method has lower estimation errors under the large-scale tag identification. After setting an optimal frame length from the estimated results of the proposed method, furthermore, we could obtain higher identification efficiency.

In order to enhance the efficiency of radio frequency identification (RFID) and lower system computational complexity, this paper proposes three novel tag anticollision protocols for passive RFID systems. The three proposed protocols are based on a binary tree slotted ALOHA (BTSA) algorithm. In BTSA, tags are randomly assigned to slots of a frame and if some tags collide in a slot, the collided tags in the slot will be resolved by binary tree splitting while the other tags in the subsequent slots will wait. The three protocols utilize a dynamic, an adaptive, and a splitting method to adjust the frame length to a value close to the number of tags, respectively. For BTSA, the identification efficiency can achieve an optimal value only when the frame length is close to the number of tags. Therefore, the proposed protocols efficiency is close to the optimal value. The advantages of the protocols are that, they do not need the estimation of the number of tags, and their efficiency is not affected by the variance of the number of tags. Computer simulation results show that splitting BTSA's efficiency can achieve 0.425, and the other two protocols efficiencies are about 0.40. Also, the results show that the protocols efficiency curves are nearly horizontal when the number of tags increases from 20 to 4,000.

In this paper, we propose a novel efficient frame slotted aloha (EFSA) protocol for radio frequency identification (RFID) tag anticollision in this paper. After successfully identifying each tag, the EFSA protocol will allocate the tag a slot number, which signifies when the tag could be identified during a read cycle. When no tags arrive and leave, idle slots and collision slots will not be produced in subsequent read cycles. In addition, if there is a collision slot in the EFSA protocol, colliding tags in the collision slot will be resolved byQ-ary splitting whereQis equal to the estimated number of colliding tags, while the other unidentified tags is in a waiting state until the colliding tags are successfully resolved. Since allocation of tags to slots is not random in the EFSA protocol, conventional tag quantity estimates are not suitable. Therefore, we also propose a novel tag quantity estimate for the EFSA protocol. Simulation results show that the EFSA protocol outperforms conventional protocols, in term of time slots of reidentifying tags, and the proposed estimate error is less than the conventional estimates in the EFSA protocol.

In a dynamic framed slotted aloha RFID system, the key technique can be divided into two parts: precisely estimating tag quantity and determining an optimal frame length. For estimating tag quantity, this paper uses three risk functions to propose three Bayesian estimates, and improves the estimates to reduce computational complexity. For determining an optimal frame length, this paper derives an optimal frame length, which can make the system achieve maximum channel usage efficiency under the condition that the durations of an idle, a collision and a successful slot are not identical. In our simulations, comparison with several conventional tag estimates shows that the proposed Bayesian tag estimates have less error. In addition, the improved estimates have lower computational complexity and their estimate performance is not reduced. The simulations results also indicate that the derived optimal frame length guarantees the maximum channel usage efficiency.