Speech emotion recognition (SER) is a challenging task in human–computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the promising results of recent studies, they generally do not leverage advanced fusion algorithms for the generation of effective representations of emotional features in speech utterances. To address this problem, we describe the fusion of spatial and temporal feature representations of speech emotion by parallelizing convolutional neural networks (CNNs) and a Transformer encoder for SER. We stack two parallel CNNs for spatial feature representation in parallel to a Transformer encoder for temporal feature representation, thereby simultaneously expanding the filter depth and reducing the feature map with an expressive hierarchical feature representation at a lower computational cost. We use the RAVDESS dataset to recognize eight different speech emotions. We augment and intensify the variations in the dataset to minimize model overfitting. Additive White Gaussian Noise (AWGN) is used to augment the RAVDESS dataset. With the spatial and sequential feature representations of CNNs and the Transformer, the SER model achieves 82.31% accuracy for eight emotions on a hold-out dataset. In addition, the SER system is evaluated with the IEMOCAP dataset and achieves 79.42% recognition accuracy for five emotions. Experimental results on the RAVDESS and IEMOCAP datasets show the success of the presented SER system and demonstrate an absolute performance improvement over the state-of-the-art (SOTA) models.

In this paper, an antenna with a multiple-input, multiple-output (MIMO) configuration is demonstrated for mm-wave 5G-based Internet of Things (IoT) applications. The two antenna elements are arranged next to each other to form a two-port antenna system such that significant field decorrelation is achieved. Moreover, a dielectric layer is backed by an eventual multiport system to amend and analyze the radiation characteristics. The overall size of the MIMO configuration is 14 mm × 20 mm, and the operation bandwidth achieves ranges from 16.7 to 25.4 GHz, considering the −10 dB criterion with a maximum isolation of more than −30 dB within the operating band. The peak gain offered by the antenna system is nearly 5.48 dB, and incorporating a dielectric layer provides an increase in the gain value to 8.47 dB. Within the operating band, more than 80% total efficiency is observed, and analysis shows several MIMO performance metrics with favorable characteristics. The compactness of the proposed design with high isolation, improved gain, and wideband features make it a suitable candidate for mm-wave-based 5G applications.

Leakage current is a very important parameter in the performance evaluation and monitoring of outdoor insulators. This paper presents experimental results of leakage current under different contaminated conditions of silicone rubber insulators. HTV silicone rubber sheets were utilized for tests and pollution was coated artificially in laboratory. The effect of temperature, humidity and pollution severity on leakage current of silicone rubber insulators were investigated. It was observed that leakage current magnitude increases with an increase in relative humidity, temperature, NSDD, and level of pollution severity. A discrete wavelet transforms (DWT) technique based on multi-resolution analysis (MRA) was used to study the time-frequency characteristics of leakage current. DWT-MRA technique can be used to detect frequency bands corresponding to different pollution severity levels and location of dry bands. These results can be used to predict dry band arcing activity on the surface of outdoor polymeric insulators.

Cadmium-free buffer layers are pivotal for the sustainable development of thin-film photovoltaics. This work numerically investigates SnS₂ as a high-performance, environmentally benign alternative to CdS for antimony selenosulfide (Sb₂(S,Se)₃) solar cells using AFORS-HET software. The SnS₂/Sb₂(S,Se)₃ heterojunction exhibits a significantly lower conduction band offset (CBO ≈ 0.23 eV) than its CdS counterpart (CBO ≈ 0.49 eV), which is identified as the primary factor for suppressed interface recombination and enhanced electron injection efficiency. A comprehensive optimization strategy is presented: tuning the S content in Sb₂(S,Se)₃ to 40% optimizes the trade-off between band gap widening and hole transport barrier at the ETL/absorber interface; adjusting the absorber thickness to 340 nm balances light absorption and carrier collection efficiency; and elevating the SnS₂ carrier concentration to 10²¹ cm⁻³ strengthens the built-in potential and induces a beneficial hole-blocking “spike” at the front contact. The synergistically optimized device achieves a power conversion efficiency (PCE) of 10.39%, a substantial improvement over the 7.56% efficiency of the CdS-based reference cell in our simulation framework.