Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest neighbor

The Journal of Supercomputing - In this article, the authors suggested a rotation-invariant local binary pattern-based weighted generalized closest neighbor (RILBP-WGCN) method for HSI...     

KLT-CRKCN: Hyperspectral Image Classification via Karhunen Loeve Transformation and Collaborative Representation-Based K Closest Neighbor

Nowadays, there is a great demand for ultra-high data rate (UHDR) transmission on most 5th generation wireless networks. In this concern, the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is used on a large scale to achieve UHDR transmission with reduced inter-symbol interference (ISI) and inter-carrier interference (ICI). Discrete wavelet transform-based OFDM (DWT-OFDM) provides better orthogonality due to presence of orthogonal wavelets, which mitigates the effects caused by ISI and ICI. Also, it has extended bandwidth than the traditional OFDM systems. But a major drawback in this system is that it suffers from down sampling. The down-sampling effect reduces the actual size of the input bit streams. As a result, the system performance is degraded. For solving this problem, a multilevel redundant discrete wavelet transform (ML-RDWT) is used instead of DWT to achieve improved spectral performance. Here, complex down-sampling operation is eliminated. From the simulation outcomes, it is clearly viewed that effects caused by ICI, ISI and BER are mitigated by improving the performance of CIR. The proposed method employs optimal red deer algorithm (ORDA) to locate the optimized weights for the ICI cancellation system. This algorithm enhances the spectral efficiency by achieving high CIR with reduced BER, ISI and ICI. The BER in the proposed MIMO-ML-RDWT-OFDM-ORDA method is 68%, 76%, 38% and 75%, which is very low when compared to the BER in the existing techniques like MIMO-DWT-OFDM-RDA, MIMO-RNS-OFDM-PNMA, MIMO-OFDM-BMA and MIMO-OFDM-ICIMA. The ISI in the proposed method is 94%, 91%, 95% low when compared to the ISI in the existing techniques. The ICI in the proposed work is 71%, 57%, 73% and 86% low when compared to the ICI in the existing techniques. Therefore, the general performance of the proposed MIMO-ML-RDWT-OFDM-ORDA method is improved in an efficient way with less complexity, error rate and processing delay.

     

A Deep Learning-Based Intelligent Decision Support System for Hyperspectral Image Classification Using Manifold Batch Structure in Internet of Things (IoT)

Wireless Personal Communications - In the modern era of Internet of Things (IoT), deep-learning-based systems are exhibited encouraging performance in hyperspectral image (HSI) classification,...     

Localized Multi‐Component Delivery Platform Generates Local and Systemic Anti‐Tumor Immunity

As tumors employ complementary overlapping and/or independent mechanisms to evade immune surveillance, many emerging cancer immunotherapies attempt to target multiple pathways to eradicate malignant cells. Although modulation of independent pathways by simultaneous administration of multiple immune modulators (e.g., checkpoint inhibitors, cytokines, and growth factors) has shown great promise, the clinical impact remains limited due to severe toxicity associated with high systemic levels of many of these drugs. Therefore, novel platforms for efficient delivery of multi‐component therapies at lower effective doses would be enabling. Here, a drug delivery platform called immunomodulatory molecule delivery system (iMods), which provides sustained extracellular delivery of a checkpoint inhibitor (anti‐PD‐L1) and simultaneously, targeted intracellular delivery of a tumor antigen (OVA) along with adjuvant (poly(I:C)), and the indoleamine deoxygenase inhibitor 1‐MT is described. In melanoma tumor‐bearing mice, combinatorial delivery of these factors with iMods leads to regression of both treated and untreated (contralateral) melanoma tumors and 100% survival. These promising therapeutic outcomes are attributed to significantly enhanced ratios of anti‐tumor CD8 T‐cell/tumor‐protective regulatory T‐cell (Treg) in tumors and tumor draining lymph nodes. Overall, the iMods delivery platform described here represents a promising advance in multi‐factor cancer immunotherapy.