Speech emotion recognition plays a crucial role in analyzing psychological disorders, behavioral decision‐making, and human‐machine interaction applications. However, the majority of current methods for speech emotion recognition heavily rely on data‐driven approaches, and the scarcity of emotion speech datasets limits the progress in research and development of emotion analysis and recognition. To address this issue, this study introduces a new English speech dataset specifically designed for emotion analysis and recognition. This dataset consists of 5503 voices from over 60 English speakers in different emotional states. Furthermore, to enhance emotion analysis and recognition, fast Fourier transform (FFT), short‐time Fourier transform (STFT), mel‐frequency cepstral coefficients (MFCCs), and continuous wavelet transform (CWT) are employed for feature extraction from the speech data. Utilizing these algorithms, the spectrum images of the speeches are obtained, forming four datasets consisting of different speech feature images. Furthermore, to evaluate the dataset, 16 classification models and 19 detection algorithms are selected. The experimental results demonstrate that the majority of classification and detection models achieve exceptionally high recognition accuracy on this dataset, confirming its effectiveness and utility. The dataset proves to be valuable in advancing research and development in the field of emotion recognition.

Illegal construction not only seriously affects economic consumption and urban infrastructure (including monitoring equipment, 5G infrastructure, 6G devices, and other consumer electronics) but also hinders the development of smart cities. However, current illegal building detection methods have low detection accuracy and high calculation costs. Therefore, a method for detecting illegal building activities by identifying illegal building-related objects is proposed. A lightweight and highprecision detector and a dataset including 14,038 images of 29 categories of illegal building objects are proposed. The proposed detector is an enhanced version of YOLOv4. MobileNetV3 is utilized as the backbone to extract features, which greatly improves the detection accuracy. Further, depthwise separable convolution (DSC) is introduced to optimize the model structure and lower calculation costs and parameters. Furthermore, Mish was utilized to enhance the identification precision, generalization, and robustness of the detector. Experimental results revealed that the mean average precision (mAP) value of the detector is 88.79 with the dataset. Compared to other state-of-the-art (SOTA) detectors, this detector exhibited superior performance in identifying illegal construction objects, which can detect and prevent illegal building activities accurately and timely, enhancing the development of smart cities. Our project is released at https://github.com/QvQKing/YMMNet.git.

Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors.