An improved soft-input soft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation. Compared with the traditional mismatched detection which uses the channel estimate as its exact value, the signal model of the proposed detector is more accurate and the influence of channel estimation error (CEE) can be effectively mitigated. Simulations indicate that the proposed scheme can improve the bit error rate (BER) performance with fewer pilot symbols.

Utilizing available channels to improve the network performance is one of the most important targets for the cognitive MAC protocol design. Using antenna technologies is an efficient way to reach this target. Therefore, in this paper, we propose a novel cognitive MAC protocol, called Polarization-based Long-range Communication Directional MAC Protocol (PLRC-DMAC), for Cognitive Ad Hoc Networks (CAHNs). The proposed protocol uses directional antennas to acquire better spatial reuse and establish long-range communication links, which can support more nodes to access the same channel simultaneously. Moreover, the PLRC-DMAC also uses polarization diversity to allow nodes in the CAHN to share the same channel with Primary Users (PUs). Furthermore, we also propose a Long-range Orientation (LRO) algorithm to orient the long-range nodes. Simulation results show that the LRO algorithm can accurately orient the long-range nodes, and the PLRC-DMAC can significantly increase the network throughput as well as reduce the end-to-end delay.

Conventional features for Automatic Speech Recognition and Sound Event Recognition such as Mel-Frequency Cepstral Coefficients (MFCCs) have been shown to perform poorly in noisy conditions. We introduce an auditory feature based on the gammatone filterbank, the Selective Gammatone Envelope Feature (SGEF), for Robust Sound Event Recognition where channel selection and the filterbank envelope is used to reduce the effect of noise for specific noise environments. In the experiments with Hidden Markov Model (HMM) recognizers, we shall show that our feature outperforms MFCCs significantly in four different noisy environments at various signal-to-noise ratios.

In this letter, we propose a Partially Observable Markov Decision Process (POMDP) based Distributed Adaptive Opportunistic Spectrum Access (DA-OSA) Strategy for Cognitive Ad Hoc Networks (CAHNs). In each slot, the source and destination choose a set of channels to sense and then decide the transmission channels based on the sensing results. In order to maximize the throughput for each link, we use the theories of sequential decision and optimal stopping to determine the optimal sensing channel set. Moreover, we also establish the myopic policy and exploit the monotonicity of the reward function that we use, which can be used to reduce the complexity of the sequential decision.

In this letter, we propose a Throughput-aimed MAC Protocol with Quality of Service (QoS) provision (T-MAC) for cognitive Ad Hoc networks. This protocol operates based on the Time Division Multiple Access (TDMA) slot assignments and the power control mechanism, which can improve the QoS provision and network throughput. Our simulation results show that the T-MAC protocol can efficiently increase the network throughput and reduce the access delay.

Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.

Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (MEG), and construct a complete secondary spectrum market. The market based on the MEG algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the MEG algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoffs with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the MEG algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The MEG algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the MEG algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.

Multiple antenna is introduced into spectrum sensing in cognitive radios recently. However, conventional multiple antenna spectrum sensing schemes exploited only space diversity. In this paper, we propose a new multiple antenna sensing scheme based on space and time diversity (MASS-BSTD). First, the primary user signal to be sensed is over-sampled at each antenna, and signal samples collected at the same time instant from different antennas are stacked into a column vector. Second, each column vector is utilized to estimate space correlation matrix that exploits space diversity, and two consecutive column vectors are utilized to estimate time correlation matrix that exploits time diversity. Third, the estimated space correlation matrix and time correlation matrix are combined and analyzed using eigenvalue decomposition to reduce information redundancy of signals from multiple antennas. Lastly, the derived eigenvalues are utilized to construct the test statistic and sense the presence of the primary user signal. Since the proposed MASS-BSTD exploits both space diversity and time diversity, it achieves performance gain over the counterparts that only exploit space diversity. Furthermore, the proposed MASS-BSTD requires no prior information on the primary user, the channel between primary user transmitter and secondary user receiver, and is robust to noise uncertainty. Theoretical analysis and simulation results show that the proposed MASS-BSTD can sense the presence of primary user signal reliably.

Source-follower-based (SFB) continuous-time low-pass filters (LPF) have the advantages of low power and high linearity over other filter topologies. The second-order SFB filter cells, which are key building blocks for high-order SFB filters, are often realized by composite source follower with positive feedback. For a single branch 2nd-order SFB cell, the linearity drops severely at high frequencies in the pass band because its slew-rate is restricted by the Q factor and the pole frequency. The folded 2nd-order SFB cell provides higher linearity because it has two DC branches, and hence has another freedom to increase the slew rate. However, because of the positive feedback, the folded and unfolded 2nd-order SFB cells, especially those with high Q factors, tend to be unstable and act as relaxation oscillators under given circuit parameters. In order to obtain higher Q factor, a new topology for the 2nd-order SFB cell without positive feedback is proposed in this paper, which is unconditionally stable and can provide high linearity. Based on the folded 2nd-order SFB cell and the proposed high-Q SFB cell, a 264 MHz sixth-order LPF with 3 stages for ultra wideband (UWB) applications is designed in 0.18 µm CMOS technology. Simulation results show that the LPF achieves an IIP3 of above 12.5 dBm in the whole pass band. The LPF consumes only 4.1 mA from a 1.8 V power supply, and has a layout area of 200 µm 150 µm.