Low Complexity 2D Research Articles

Parallel Complementary Virtual Arrays Algorithm for Direction of Arrival (DOA) Estimation

This Paper discusses the challenges faced by previous method 2D Direction of Arrival (DOA) systems, such as low degrees of freedom, poor resolution, and significant estimation errors in scenarios with small snapshots. In response to these issues, the present method proposes a low-complexity 2D Direction of Arrival (DOA) estimation algorithm based on a parallel complementary virtual array.
 The algorithm utilizes two mutually parallel complementary linear arrays to generate a virtual array, addressing the limitations of traditional parallel arrays. It constructs an extended matrix with enhanced 2D angular degrees of freedom using covariance and cross-covariance matrices. The final step involves obtaining automatic matching 2D angle estimates through Singular Value Decomposition (SVD) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT).
 In comparison to traditional 2D DOA estimation methods, the proposed algorithm better exploits the information from the array's received data. It can identify more incoming signals, offering high resolution without the need for 2D linear search or angle parameter matching. Importantly, it demonstrates effective estimation even in scenarios with low Signal-to-Noise Ratio (SNR) and small snapshots. Experimental simulation results validate the effectiveness and reliability of the proposed algorithm.

Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error

Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

Low complexity 2D adaptive image processing algorithm and its hardware implementation

Digital image processing (DIP) algorithms used in consumer electronics products have high computational complexities. Therefore, in this paper, we propose a novel low complexity 2D adaptive DIP algorithm. The proposed algorithm reduces computational complexities of 2D DIP algorithms by exploiting pixel correlations in input image without reducing quality of output image. We also designed a low energy 2D adaptive DIP hardware implementing the proposed algorithm. The proposed hardware is verified to work correctly on an FPGA board. It has significantly less energy consumption than original 2D DIP hardware. Therefore, it can be used especially in portable consumer electronics products.

Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays

We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), and field experiments using a Voxnet system validate the proposed method and demonstrate a significant performance enhancement at a low computation burden.

Interactive Multiview Video System With Low Complexity 2D Look Around at Decoder

Multiview video with interactive 2D look around at the receiver is a challenging application with several issues in terms of effective use of storage and bandwidth resources, reactivity of the system, quality of the viewing experience and system complexity. The impression of 3D immersion is highly dependent on the smoothness of the navigation and thus on the number of 2D viewpoints. The classical decoding system for generating virtual views first projects a reference or encoded frame to a given viewpoint and then fills in the holes due to potential occlusions. This last step still constitutes a complex operation with specific software or hardware at the receiver and requires a certain quantity of information from the neighboring frames for ensuring consistency between the virtual images. In this work we propose a new approach that shifts most of the burden due to interactivity from the decoder to the encoder, by anticipating the navigation of the decoder and sending auxiliary information that guarantees temporal and interview consistency. This leads to an additional cost in terms of transmission rate and storage, which we minimize by using optimization techniques based on the user behavior modeling. We show by experiments that the proposed system represents a valid solution for interactive multiview systems with classical decoders.

A Low Complexity 2D Pattern Synthesis Algorithm for Cylindrical Array

This paper proposes a 2D pattern synthesis algorithm for cylindrical array. According to the geometric characteristic of cylinder, we can regard a cylindrical array as an equivalent linear array whose elements are identical circular subarrays. Therefore, the beam pattern can be obtained by the product of the array factor of linear array and beam pattern of circular subarray. Then, the 2D beamforming can be realized by two 1D beamforming processes. We can prove that the complex excitation vector of a cylindrical array is the Kronecker product of linear array’s weight vector and circular array’s weight vector. By this algorithm of decomposition and reconstruction, the computational complexity of 2D beamforming could be significantly reduced. Finally, simulation results further illustrate the validity of the proposed method.

Low-complexity 2D behavioural model for concurrent dual-band power amplifiers

A complexity-efficient two-dimensional (2D) behavioural model for concurrent dual-band power amplifiers (PAs) is proposed based on the (k+1)th-order cross-band modulation basis function. The main advantages of the proposed model are lower complexity and comparable modelling performance compared to the state-of-the-art. Measurements were carried out for two types of dual-band PAs. The performance of the proposed model has been proved to be effective.

A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation

摘要: 该文提出了一种通用的低复杂度二维隐Markov模型，推导了前向算法和后向算法的递归形式。文中没有使用前人为了降低时间复杂度而提出的相邻图像块间条件独立性假设，使提出的模型更加通用，并且可以根据需要调节水平和竖直两个方向信息的权重，具有更高的灵活性。将该模型应用于图像分割，实验结果证明了模型的有效性。 关键词: 图像分割; 二维隐Markov模型; 解码问题

Low-complexity 2D coherently distributed sources decoupled DOAs estimation method

The existing directions-of-arrival (DOAs) estimation methods for two-dimensional (2D) coherently distributed sources need one- or two-dimensional search, and the computational complexities of them are high. In addition, most of them are designed for special angular signal distribution functions. As a result, their performances will degenerate when deal with different sources with different angular signal distribution functions or unknown angular signal distribution functions. In this paper, a low-complexity decoupled DOAs estimation method without searching using two parallel uniform linear arrays (ULAs) is proposed for coherently distributed sources, as well as a novel parameter matching method. It can resolve the problems mentioned above efficiently. Simulation results validate the effectiveness of our approach.

A Low-Complexity and High-Performance 2D Look-Up Table for LDPC Hardware Implementation

In this paper, we propose a low-complexity, high-efficiency two-dimensional look-up table (2D LUT) for carrying out the sum-product algorithm in the decoding of low-density parity-check (LDPC) codes. Instead of employing adders for the core operation when updating check node messages, in the proposed scheme, the main term and correction factor of the core operation are successfully merged into a compact 2D LUT. Simulation results indicate that the proposed 2D LUT not only attains close-to-optimal bit error rate performance but also enjoys a low complexity advantage that is suitable for hardware implementation.

Comments on "A Separable Low Complexity 2D HMM with Application to Face Recognition

The separable low complexity 2D HMM proposed in [1] builds on the assumption of conditional independence in the relationship between adjacent blocks. The authors view this assumption as the key assumption to reduce the complexity. In this communication, we show that this key assumption is entirely unnecessary.

A separable low complexity 2d hmm with application to face recognition

In this paper, we propose a novel low-complexity separable but true 2D Hidden Markov Model (HMM) and its application to the problem of Face Recognition (FR). The proposed model builds on an assumption of conditional independence in the relationship between adjacent blocks. This allows the state transition to be separated into vertical and horizontal state transitions. This separation of state transitions brings the complexity of the hidden layer of the proposed model from the order of (N/sup 3/T) to the order of (2N/sup 2/T), where N is the number of the states in the model and T is the total number of observation blocks in the image. The system performance is studied and the impact of key model parameters, i.e., the number of states and of kernels of the state probability density function, is highlighted. The system is tested on the facial database of AT&T Laboratories Cambridge and the more complex facial database of the Georgia Institute of Technology where recognition rates up to 100 percent and 92.8 percent have been achieved, respectively, with relatively low complexity.

Low complexity 2D preprocessing for discrete multiwavelet transform of image signals

A low complexity 2D approximation-based preprocessing scheme for GHM (Geronimo, Hardin and Massopust) multiwavelet transform of 2D signals is proposed. The proposed scheme reduces the preprocessing computational complexity of the recent 1D approximation-based method by 65% while maintaining a similar energy compaction capability.