Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce the number of required light-emitting diodes (LEDs), advanced methods commonly exploit LED shape features for positioning. Although interesting, they are typically restricted to a single LED geometry, leading to failure in heterogeneous LED-shape scenarios. To address this challenge, this paper investigates Lamé curves as a unified representation of common LED shapes and proposes a generic VLP algorithm using Lamé curve-shaped LEDs, termed LC-VLP. In the considered system, multiple ceiling-mounted Lamé curve-shaped LEDs periodically broadcast their curve parameters via visible light communication, which are captured by a camera-equipped receiver. Based on the received LED images and curve parameters, the receiver can estimate the camera pose using LC-VLP. Specifically, an LED database is constructed offline to store the curve parameters, while online positioning is formulated as a nonlinear least-squares problem and solved iteratively. To provide a reliable initialization, a correspondence-free perspective-n-points (FreePnP) algorithm is further developed, enabling approximate CPE without any pre-calibrated reference points. The performance of LC-VLP is verified by both simulations and experiments. Simulations show that LC-VLP outperforms state-of-the-art methods in both circular- and rectangular-LED scenarios. Compared to a perspective arcs algorithm, LC-VLP can achieve reductions of both over 30% in average position and rotation errors. Experiments further show that LC-VLP can achieve an average position accuracy of less than 4 cm.

The Internet of Things (IoT) is a new heterogeneous system integrated by the various end users (sensors and terminals) with different technologies. However, the limiting factor is bandwidth in the IoT due to the exploding end users and the network bandwidth requirements. A novel IoT model, which integrates the power-line carrier (PLC) and the wireless network (WN), is proposed to solve the bandwidth problem from the architecture, especially in the areas lacking network facilities. In addition, we exploit an effective virtual layer (EVL) which allows the different end users to access the system model seamlessly. Then, the attractor selection algorithm based on Markov chain (MASA) is employed to select an optimal path among the PLC or WN. The simulation results demonstrate that the proposed system model has the smaller average queuing delay than other algorithms and makes the model more stable and robust.

In massive multiple-input multiple-output (MIMO) uplink systems, phase noise introduced by oscillators can cause severe performance loss. It leads to common phase error and inter-carrier interference in massive MIMO-OFDM uplink. To solve the issue, a novel phase noise cancellation scheme based on polarization modulation is proposed. We first introduce the polarization modulation (PM) exploited in massive MIMO-OFDM uplink. Then, by exploiting zero-forcing detection, we analyze ICI and the distribution of the transformed noise. Furthermore, we demonstrate that phase noise can be asymptotically cancelled and only the transformed additive white Gaussian noise exists as the number of antennas at the base station is very lager. Moreover, we derive the instantaneous signal-to-noise ratio (SNR) on each subcarrier and analyze the ergodic capacity. The simulation results show that the proposed scheme can effectively mitigate phase noise and achieve a higher ergodic capacity.

In full duplex (FD) multiuser MIMO system, multiple mobile stations (MSs) with FD transmission simultaneously access a given base station (BS), significant interuser interferences exist, limiting FD system performance. Existing methods addressing in the inter-user interference cancellation mostly need to know the interference channel state information and to decode and subtract the interference from the receiver. In this paper, we propose a blind polarization oblique projection based inter-user interference cancellation method without any prior information of the interference channel. This method merely utilizes the polarization state (PS) information and the autocorrelation matrix information of received signals at the downlink MS receivers to construct an oblique projection operator, which can be used to project the inter-user interference to the null space of the operator. Numerical results demonstrate that the proposed method can cancel the inter-user interference effectively when compared to the system with inter-user interference existing, and it performs more robust when the signal to noise ratio (SNR) and the receive antennas number is small.

To cancel phase noise which causes the bit error rate of OFDM systems degradation, a novel orthogonal-polarization-based phase noise self- cancellation (OP-PNSC) scheme in the polarization dependent loss (PDL) channel is proposed. In the proposed scheme, the orthogonal polarizations are utilized to transmitted orthogonally polarized signals which are added together at the receiver to cancel phase noise. These orthogonally polarized signals transmitted in the same-frequency channel enable that the proposed scheme has the advantages of more efficient in cancelling phase noise and the spectral efficiency (SE) improvement. Considering the realistic wireless channel, the distortion of PDL is investigated and a PDL pre-compensated OP- PNSC (PPC-OP-PNSC) scheme is proposed to mitigate the power imbalance caused by PDL. Then, the signal- to-interference-plus-noise ratio (SINR) and SE performances are analyzed to evaluate the performance of the OP-PNSC scheme. Finally, the numerical results show that the OP-PNSC scheme achieves performance close to that of OFDM system without phase noise in the PDL channel.

In dual-polarized channel, the BER performance of polarization modulation (PM) can be decreased by cross-polarization discrimination (XPD) effect which can introduce cross power leakage. Therefore, a XPD compensation algorithm for PM is proposed. Based on the analyses about the effect of XPD on PM, the compensation factor is obtained by the channel state information. Then by compensating the state of polarization of the received signals, the cross power leakage between two orthogonal components of the states of polarization is mitigated, and the constellation distortion of PM is reduced. The analyses reveal that the compensation algorithm can effectively improve the BER performance of PM affected by XPD. Finally, simulation results show that when the XPD is a definite value, to achieve equivalent BER performance of 2PM, the SNR needed is 2.5dB reduction after compensation.

To improve the energy efficiency of power amplifier in OFDM systems, an adaptive polarization-QAM modulation scheme is proposed considering the data rate requirement variation at the transmitter. Since QAM and polarization modulation have different energy efficiency performance in the linear and nonlinear region of power amplifier respectively, thus the optimal energy efficiency can be obtained by combining the two modulation schemes together and adjusting their modulation order adaptively to fulfill the varying data rate requirement. The power series model of power amplifier is used to analyze the effects of both the in-band distortions and the out-of-band emissions on OFDM signals. Based on above, the proposed scheme can be formulated as an optimization problem regarding the output back off value as well as the modulation order of polarization modulation and QAM. Simulations under the same symbol error rate threshold and data rate requirement show that, the energy efficiency of the proposed scheme is nearly twice as that of the traditional QAM scheme in OFDM systems.

Full duplex systems are more spectrally efficient than conventional half duplex systems if the self-interference (SI) can be significantly mitigated. Digital cancellation is one of the lowest complexity SI cancellation techniques in full duplex systems. However, its mitigation capability is mainly limited by the power amplifier (PA) nonlinear distortion. In this paper, to cancel the SI induced by the PA nonlinear distortion, a SI cancellation scheme based on polarization mismatch (PMC) is proposed. The proposed scheme takes advantage of the characteristic that the polarization state of the SI is immune to the PA nonlinearity. And it cancels the SI in the digital baseband by a polarization mismatch matrix which has an orthogonal polarization state to that of the SI. Analysis and numerical results demonstrate that the proposed scheme can cancel the nonlinear SI induced by the PA efficiently. Besides, the desired signal to interfere and noise (SINR) gain will not deteriorate even if the power of the SI is high compared with the existing cancellation scheme. Further, the achievable rate of it can reach more than two times compared with the half duplex when the similarity coefficient of the polarization states is greater than 0.5.

The spatial and rank (SR) orderings of samples play a critical role in most signal processing algorithms. The recently introduced fuzzy ordering theory generalizes traditional, or crisp, SR ordering concepts and defines the fuzzy (spatial) samples, fuzzy order statistics, fuzzy spatial indexes, and fuzzy ranks. Here, we introduce a more general concept, the fuzzy transformation (FZT), which refers to the mapping of the crisp samples, order statistics, and SR ordering indexes to their fuzzy counterparts. We establish the element invariant and order invariant properties of the FZT. These properties indicate that fuzzy spatial samples and fuzzy order statistics constitute the same set and, under commonly satisfied membership function conditions, the sample rank order is preserved by the FZT. The FZT also possesses clustering and symmetry properties, which are established through analysis of the distributions and expectations of fuzzy samples and fuzzy order statistics. These properties indicate that the FZT incorporates sample diversity into the ordering operation, which can be utilized in the generalization of conventional filters. Here, we establish the fuzzy weighted median (FWM), fuzzy lower-upper-middle (FLUM), and fuzzy identity filters as generalizations of their crisp counterparts. The advantage of the fuzzy generalizations is illustrated in the applications of DCT coded image deblocking, impulse removal, and noisy image sharpening.

The rank information of samples is widely utilized in nonlinear signal processing algorithms. Recently developed fuzzy transformation theory introduces the concept of fuzzy ranks, which incorporates sample spread (or sample diversity) information into the sample ranking framework. Thus, the fuzzy rank reflects a sample's rank, as well as its similarity to the other sample (namely, joint rank order and spread), and can be utilized to improve the performance of the conventional rank-order-based filters. In this paper, the well-known lower-upper-middle (LUM) filters are generalized utilizing the fuzzy ranks, yielding the class of fuzzy rank LUM (F-LUM) filters. Statistical and deterministic properties of the F-LUM filters are derived, showing that the F-LUM smoothers have similar impulsive noise removal capability to the LUM smoothers, while preserving the image details better. The F-LUM sharpeners are capable of enhancing strong edges while simultaneously preserving small variations. The performance of the F-LUM filters are evaluated for the problems of image impulsive noise removal, sharpening and edge-detection preprocessing. The experimental results show that the F-LUM smoothers can achieve a better tradeoff between noise removal and detail preservation than the LUM smoothers. The F-LUM sharpeners are capable of sharpening the image edges without amplifying the noise or distorting the fine details. The joint smoothing and sharpening operation of the general F-LUM filters also showed superiority in edge detection preprocessing application. In conclusion, the simplicity and versatility of the F-LUM filters and their advantages over the conventional LUM filters are desirable in many practical applications. This also shows that utilizing fuzzy ranks in filter generalization is a promising methodology.

The paper presents a new method using fuzzy filtering to remove the coding artifacts in compressed video. The method takes the interlaced video format into consideration and processes each field separately. For deblocking, a 1D fuzzy filter with different window sizes is used to remove the horizontal and vertical blocking artifacts respectively. For deringing, each 8/spl times/8 block in a field is first classified into one of the four categories, i.e., strong edge, weak edge, texture and smooth blocks. According to each block's type and the neighboring block's type, the spread parameter of a 2D fuzzy filter is adaptively decided and the filter is applied To speed up the process, the fuzzy filter weights are generated using a piecewise linear membership function instead of the conventional Gaussian function. Experimental results show that the proposed method has better detail preservation and lower computational costs than our previous method. It achieves comparable deblocking and superior deringing performance to the MPEG-4 standard method at similar computation costs.

In this paper, a simple and effective fast block matching algorithm (BMA) is proposed, called adaptive irregular pattern search with matching prejudgment (AIPS-MP). In the AIPS-MP, a dynamic search pattern, adaptive irregular pattern (AIP), is constructed for each block based on its spatial and temporal neighboring motion vectors (MVs). The AIP is used to quickly identify the best center point for the follow-up refined search to effectively reduce unnecessary intermediate searches and avoid the search trapping at local-minimum matching error position. The construction of the AIP jointly takes the tradeoff of rate and distortion into consideration and favors the differential coding of MVs. The matching prejudgment (MP) is imbedded in the AIPS and exploits an adaptive threshold determined for each block based on the prediction error of the temporal neighboring block. The simulation results show that the proposed AIPS-MP consistently achieves very close, and sometimes even higher, peak signal-to-noise ratio (PSNR) than the full search, while achieving 167.6-871.5 times of speed-up or computational gain, measured in terms of the average number of search points per MV generation. It also greatly outperforms two well-referenced fast BMAs, the diamond search and the motion vector field adaptive search technique (MVFAST), adopted in the MPEG-4 Verification Model and the Optimization Model, respectively, on the average PSNR and the search speed.

Fuzzy ranks are a component of fuzzy ordering theory developed to incorporate sample spread information into sample ranking. In this paper, the well-known lower-upper-middle (LUM) filters are generalized by utilizing the fuzzy ranks, which is called fuzzy LUM (F-LUM) filters. We develop the statistical and deterministic properties of the F-LUM filters and present their performance in image denoising and enhancing applications. As shown by the experimental results, the F-LUM filters inherit the simplicity and versatility of the LUM filters such that they can perform signal smoothing, sharpening and outlier rejection using a single filter structure, while having great improvement in detail preservation and robustness to noise.

The work presents a new adaptive approach for blocking and ringing artifact reduction. In order to avoid smearing of the image details, the proposed method first performs visual artifact detection and then applies adaptive filtering to the corrupted blocks. Both visual artifact detection and filtering are guided by an edge map which is constructed based on local features. A fuzzy identity filter is used for image de-ringing. Since it possesses a good edge preserving property and the filtering operation is applied to the edge blocks only (smooth and textured blocks are unaltered), the proposed method shows great effectiveness of both artifact reduction and detail preservation. Experiments demonstrate better results compared with other methods.

In this paper, we introduce the basic concepts of the fuzzy transformation (FZT) theory and their applications in image denoising. FZT is a data-adaptive nonlinear transformation, where the transformation matrix is a dynamic real-valued spatial-rank (SR) matrix (i.e., fuzzy SR matrix) generated by using an order invariant membership function. The clustering property is the essential property of FZT, and is exploited (through the re suiting fuzzy samples and fuzzy ranks) in two image denoising applications, namely, DCT coded image deblocking and impulsive noise elimination. Our proposed methods demonstrate superior performance than the conventional methods in all the simulations, showing that FZT is a powerful tool for image processing.

In this paper, the concept of fuzzy transformation is developed based on fuzzy ordering theory. The clustering property of the fuzzy transformation is investigated by deriving the distribution function of the fuzzy samples and is exploited to address the DCT coded image deblocking problem. In this application, the fuzzy transformation is actually a highly data-adaptive smoothing and sharpening process. The spread parameter of the fuzzy transformation controls the smoothing level and can be optimized to fit images at different compression levels. The simulation results show that the fuzzy transformation can effectively remove various blocking artifacts and restore the original image monotone surfaces as well as edges with low computational complexity.

A novel fast block matching algorithm, called adaptive irregular pattern search with zero-motion prejudgement (AIPS-ZMP), is proposed in this paper. The algorithm effectively exploits the spatial and temporal correlation existed among the motion vectors (MVs). In AIPS-ZMP, an adaptive irregular pattern (AIP) is dynamically formed for each block according to its spatial and temporal neighboring MVs to identify the most promising search center so that unnecessary intermediate searches and the risk of being trapped into local-minimum matching-error point are both avoided. The refined search is then performed from this center to find out the target MV. To further speed up the search, zero-motion prejudgement (ZMP) technique is embedded in the AIPS's framework to detect static blocks before invoking any search. Consequently, fairly large amount of computational gain is obtained with little degradation on picture quality. Compared with the fast BMA adopted in MEG-4 Verification Model (VMS) - diamond search (SD), our proposed AIPS-ZMP method improves the average peak signal-to-noise ratio (PSNR) by up to 0.67 dB with computational gain in the range of 2.12/spl sim/5.14 times.

Combining spatial and rank (SR) order information into filtering methods has been exploited in recent nonlinear filtering algorithms. Fuzzy ordering theory incorporates sample spread information into the SR ranking framework and develops the concepts of fuzzy ranking and fuzzy order statistic. In this paper, fuzzy concepts are utilized to generalize weighted median filters and optimization of the filter parameters is discussed. Simulation results show that the proposed fuzzy weight median (FWM) filter significantly outperforms its crisp counterpart and existing FWM techniques in the literature in image denoising and image zooming.

In this paper, we propose a novel and simple fast block-matching algorithm (BMA), called adaptive rood pattern search (ARPS), which consists of two sequential search stages: 1) initial search and 2) refined local search. For each macroblock (MB), the initial search is performed only once at the beginning in order to find a good starting point for the follow-up refined local search. By doing so, unnecessary intermediate search and the risk of being trapped into local minimum matching error points could be greatly reduced in long search case. For the initial search stage, an adaptive rood pattern (ARP) is proposed, and the ARP's size is dynamically determined for each MB, based on the available motion vectors (MVs) of the neighboring MBs. In the refined local search stage, a unit-size rood pattern (URP) is exploited repeatedly, and unrestrictedly, until the final MV is found. To further speed up the search, zero-motion prejudgment (ZMP) is incorporated in our method, which is particularly beneficial to those video sequences containing small motion contents. Extensive experiments conducted based on the MPEG-4 Verification Model (VM) encoding platform show that the search speed of our proposed ARPS-ZMP is about two to three times faster than that of the diamond search (DS), and our method even achieves higher peak signal-to-noise ratio (PSNR) particularly for those video sequences containing large and/or complex motion contents.