INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet’s performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m × 5m and 10m × 10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors.

Block-Sparse Recovery with Optimal Block Partition

This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.

Boosted PTS Method with Mu-Law Companding Techniques for PAPR Reduction in OFDM Systems

Perpendicular rate of recurrence splitting up a group of numeral television or radio channels that are mixed together for broadcast Orthogonal Frequency Division Multiplexing which can be a potential diffusion method for elevating the transmission capacity of the communication systems. In spite of the significance of OFDM, the primary issue of the peak-to-average power ratio (PAPR) which augments communication system complications, reduces the effectiveness of the communication system, resulting in low performance of bit-error-rate (BER), and making OFDM perceptive toward non-linear distortion within a broadcast. Various techniques were projected for treating PAPR issues, inclusive of partial transmit sequence (PTS) which captivated great interest. Thus, this paper proposed a hybrid method inclusive of a boosted PTS scheme with Mu-law compressing and expanding approach. The PTS approach was boosted through boosting its sub-block partitioning scheme, the place where the aggrandized partitioning scheme consolidated a conventional interleaved partitioning into an adjacent partitioning scheme. The present merger concerning Mu-Law characteristic in time domain for PAPR reduction in OFDM fundamentally boosts PAPR diminution performance. Accordingly, though the simulated pseudorandom sub-block partition method improved PAPR diminution supplementary further than other sub-block partition schemes appertaining to conventional PTS, while maintaining low computational complexity. The findings show that the boosted PTS scheme with Mu-law expanding approach, whilst upholding low computational complexity, achieves considerably superior to the pseudorandom partitioning PTS with regard to various type of modulation format and subcarriers.

A Novel Asymmetric Hyperchaotic Image Encryption Scheme Based on Elliptic Curve Cryptography

Most of the image encryption schemes based on chaos have so far employed symmetric key cryptography, which leads to a situation where the key cannot be transmitted in public channels, thus limiting their extended application. Based on the elliptic curve cryptography (ECC), we proposed a public key image encryption method where the hash value derived from the plain image was encrypted by ECC. Furthermore, during image permutation, a novel algorithm based on different-sized block was proposed. The plain image was firstly divided into five planes according to the amount of information contained in different bits: the combination of the low 4 bits, and other four planes of high 4 bits respectively. Second, for different planes, the corresponding method of block partition was followed by the rule that the higher the bit plane, the smaller the size of the partitioned block as a basic unit for permutation. In the diffusion phase, the used hyperchaotic sequences in permutation were applied to improve the efficiency. Lots of experimental simulations and cryptanalyses were implemented in which the NPCR and UACI are 99.6124% and 33.4600% respectively, which all suggested that it can effectively resist statistical analysis attacks and chosen plaintext attacks.

Block-Sparse Recovery with Optimal Block Partition

This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.

Block-Sparse Recovery with Optimal Block Partition

This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.