Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture.

In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge's detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.

A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera.

In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot's observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.

PAPR reduction using SLM-PTS-CT hybrid PAPR method for optical NOMA waveform.

In this article, we focus on optimising the SLM-PTS-CT (selective mapping, partial transmission sequence, circular transformation) hybrid method for optical non-orthogonal multiple access (O-NOMA) waveforms. The goal is to enhance the spectrum performance and practicality of O-NOMA systems while mitigating the PAPR issue through a hybrid approach. The SLM-PTS-CT hybrid method is applicable to O-NOMA waveforms, providing effective PAPR reduction. By dividing the data sequence into sub-blocks, applying phase factors, and rotating the phase of the subcarriers in such a way that the peaks of the signal are distributed more uniformly, the proposed SLM-PTS-CT achieves an optimal PAPR reduction while maintaining the benefits of O-NOMA. The efficiency of the proposed method is analysed by estimating the performance of several parameters, such as bit error rate (BER), PAPR, and power spectral density (PSD), by increasing the number of sub-blocks (S) and phase factor (P). Further, the proposed SLM-PTS-CT is compared with the conventional SLM-PTS, SLM, and PTS. The simulation results demonstrate that the proposed approach efficiently improves spectral efficiency, preserves BER performance, and reduces PAPR as compared with conventional methods.

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification.

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.

Numb exon 9 inclusion regulates Integrinß5 surface expression and promotes breast cancer metastasis.

The endocytic adaptor protein Numb acts as a tumor suppressor through downregulation of oncogenic pathways in multiple cancer types. The identification of splicing alterations giving rise to changes in Numb protein isoform expression indicate that Numb also has tumor promoting activity, though the underlying mechanisms are unknown. Here we report that NUMB exon 9 inclusion, which results in production of a protein isoform with an additional 49 amino acids, is a feature of multiple cancer types including all subtypes of breast cancer and correlates with worse progression-free survival. Specific deletion of exon 9-included Numb isoforms (Exon9in) from breast cancer cells reduced cell growth and prevents spontaneous lung metastasis in a mouse model. Quantitative proteome profiling showed that loss of Exon9in causes downregulation of membrane receptors and adhesion molecules, as well as proteins involved in extracellular matrix organization and the epithelial-mesenchymal transition (EMT) state. In addition, exon 9 deletion caused remodeling of the endocytic network, decreased ITGß5 surface localization, cell spreading on vitronectin and downstream signaling to ERK and SRC. Together these observations suggest that Exon9in isoform expression disrupts the endocytic trafficking functions of Numb, resulting in increased surface expression of ITGß5 as well as other plasma membrane proteins to promote cell adhesion, EMT, and tumor metastasis.