Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.

Currently, Graph Neural Networks have been extended to the field of speech signal processing. It is the more compact and flexible way to represent speech sequences by graphs. However, the structures of the relationships in recent studies are tend to be relatively uncomplicated. Moreover, the graph convolution module exhibits limitations that impede its adaptability to intricate application scenarios. In this study, we establish the speech-graph using feature similarity and introduce a novel architecture for graph neural network that leverages an LSTM aggregator and weighted pooling. The unweighted accuracy of 65.39% and the weighted accuracy of 71.83% are obtained on the IEMOCAP dataset, achieving the performance comparable to or better than existing graph baselines. This method can improve the interpretability of the model to some extent, and identify speech emotion features effectively.

Orthogonal frequency division multiplexing (OFDMA) offers great spectrum efficiency and flexible frequency allocation to users without intra-cell interferences in LTE system. However, the cell edge users will experience high interferences from neighbouring cells. Many frequency reuse schemes have been proposed for improving the Signal to Interference and Noise Ratio (SINR) performance for cell edge users, most of them dividing available frequencies into groups for cell centre and cell edge users. In this research, we combine the traditional frequency scheme with novel semi-smart antennas and learning algorithm - Genetic Algorithm (GA). The semi-smart antennas can produce flexible coverage patterns for base stations (BSs) and the learning algorithm can coordinate the coverage patterns between BSs to minimise interferences for mobile units (MUs). A system level simulation contains 25 BSs and 1000 MUs has been developed. Simulation results show that the proposed scheme improves the total traffic load and reduces antenna propagation power for all cells compares to system with fixed coverage patterns.

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications.

Sentiment analysis using Large Language Models (LLMs) has gained significant attention in recent research due to its outstanding performance and ability to understand complex texts. However, popular LLMs, such as ChatGPT, are typically closed-source and come with substantial API costs, posing challenges for resource-limited scenarios and raising concerns about privacy. To address this, our study evaluates the feasibility of using small LLMs (sLLMs) as alternatives to GPT for aspect-based sentiment analysis in Chinese healthcare reviews.We compared several Chinese sLLMs of varying sizes with GPT-3.5, using GPT-4o’s results as the benchmark, and assessed their classification accuracy by computing F1 scores for each individual aspect as well as an overall F1 score. Additionally, we examined sLLMs’ instruction-following capabilities, VRAM requirements, generation times, and the impact of temperature settings on the performance of top-performing sLLMs. The results demonstrate that several sLLMs can effectively follow instructions and even surpass GPT-3.5 in accuracy. For instance, InternLM2.5 achieved an F1 score of 0.85 with zero-shot prompting, while the smaller Qwen2.5-3B model performed well despite its minimal size. Prompt strategies significantly influenced smaller and older models like Qwen2.5-1.5B and ChatGLM3.5 but had limited impact on newer models. Temperature settings showed minimal effect, while older models generated responses faster, and newer models offered higher accuracy. This study underscores the potential of sLLMs as resource-efficient, privacy-preserving alternatives to closed-source LLMs in specialized domains. Our work demonstrates versatility, with potential applications across domains such as finance and education, and tasks like sentiment analysis, credit risk assessment, and learning behavior analysis, offering valuable insights for real-world use cases.

Engineering design optimization problems are often characterized by high dimensionality, complex constraints, and multimodal search landscapes, which pose significant challenges to conventional metaheuristic algorithms. Although the Whale Optimization Algorithm (WOA) has demonstrated competitive performance, it still suffers from premature convergence, limited population diversity, and an imbalanced exploration-exploitation mechanism in complex optimization scenarios. To overcome these limitations, this paper proposes a Geometric Whale Optimization Algorithm (ESTGWOA), in which multiple geometric strategies are systematically embedded into the canonical WOA framework to enhance population initialization, search guidance, and position update behaviors. By incorporating geometric-based mechanisms, ESTGWOA effectively improves search space coverage and strengthens the coordination between global exploration and local exploitation. Comprehensive experiments on 23 benchmark functions demonstrate that ESTGWOA, with an overall effectiveness of 97.10%, outperformed other algorithms on benchmark functions with different dimensions of 30, 50 and 100 dimensions. And the simulations on a series of constrained engineering design problems demonstrate that ESTGWOA consistently outperforms selected state-of-the-art metaheuristic algorithms. Quantitative results show that ESTGWOA achieves superior average fitness values and lower standard deviations in most cases, with statistical significance verified by Wilcoxon rank-sum and Friedman tests. Furthermore, qualitative analyses of search history, population diversity, and convergence behavior confirm the robustness and stability of the proposed approach. These results indicate that ESTGWOA is a reliable and effective optimization algorithm for complex continuous engineering design problems.