The intrusion detection system (IDS) is a promising technology for ensuring security against cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and feature extraction are performed by two different models. In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used later to detect anomalies. Then, the trained AE model is employed again to extract useful low-dimensional features for anomalous data without the need for a feature extraction training stage, which is required by other methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). After that, the extracted features are used by a machine learning (ML) or deep learning (DL) classifier to determine the type of attack (multi-classification). The performance of the proposed unified approach was evaluated on real IoT datasets called N-BaIoT and MQTTset, which contain normal and malicious network traffics. The proposed AE was compared with other popular anomaly detection techniques such as one-class support vector machine (OC-SVM) and isolation forest (iForest), in terms of performance metrics (accuracy, precision, recall, and F1-score), and execution time. AE was found to identify attacks better than OC-SVM and iForest with fast detection time. The proposed feature extraction method aims to reduce the computation complexity while maintaining the performance metrics of the multi-classifier models as much as possible compared to their counterparts. We tested the model with different ML/DL classifiers such as decision tree, random forest, deep neural network (DNN), conventional neural network (CNN), and hybrid CNN with long short-term memory (LSTM). The experiment results showed the capability of the proposed model to simultaneously detect anomalous events and reduce the dimensionality of the data.

Microbial fuel cells (MFCs) have attracted much interest as an alternative energy conversion technology and as a system for recovering and treating wastewater. MFC is a powerful technique for generating energy from various sources, including natural organic matter and renewable biomass. It has several possible applications, including power generation for many small electronic devices, wastewater treatment, and biosensors. However, the restricted power output of MFCs is the most significant impediment to their widespread use and up-scaling in practical applications. The anode electrode is the most critical component of an MFC, where poor anode electrode performance leads to poor MFC efficiency. Therefore, efforts have been made to modify electrodes to improve their performance. While power density is an essential metric in determining MFC efficiency, other parameters such as Coulombic efficiency, current density, cell voltage, and the removal rate of chemical oxygen demand (COD) should also be considered to evaluate the performance of MFC. This study reviews the most recent electrode modification techniques through anode treatments with metal oxides, conductive polymers, carbon nanotubes, and other chemical compounds as well as through cathode modifications. Different modified MFCs are compared in terms of their power density and the type of bacteria and membrane used.

Speech emotion recognition aims to automatically identify and classify emotions from speech signals. It plays a crucial role in various applications such as human-computer interaction, affective computing, and social robotics. Over the years, researchers have proposed different approaches for speech emotion recognition, leveraging various classifiers and features. However, despite the advancements, existing methods in speech emotion recognition still have certain limitations. Some approaches rely on handcrafted features that may not capture the full complexity of emotional information present in speech signals, while others may suffer from a lack of robustness and generalization when applied to different datasets. To address these challenges, this paper proposes a speech emotion recognition method that combines Mel spectrogram with Short-Term Fourier Transform (Mel-STFT) and the Improved Multiscale Vision Transformers (MViTv2). The Mel-STFT spectrograms capture both the frequency and temporal information of speech signals, providing a more comprehensive representation of the emotional content. The MViTv2 classifier introduces multi-scale visual modeling with different stages and pooling attention mechanisms. MViTv2 incorporates relative positional embeddings and a residual pooling connection to effectively model the interactions between tokens in the space-time structure, preserve essential information, and improve the efficiency of the model. Experimental results demonstrate that the proposed method generalizes well on different datasets, achieving an accuracy of 91.51% on the Emo-DB dataset, 81.75% on the RAVDESS dataset, and 64.03% on the IEMOCAP dataset.

The flight path of the Unmanned Aerial Vehicle (UAV) relay can significantly affect the performance of the wireless relay communication system. In this paper, we investigate the optimization of the UAV flight path in a wireless relay communication system when a jamming or interference signal is present. The UAV flight path is optimized to maximize the signal-to-interference-plus-noise ratio (SINR) and required parameters are estimated. Simulation results show that the proposed method can achieve performance close to an ideal SINR optimization and also highlight the benefits of optimizing the SINR instead of the signal-to-noise ratio (SNR) when a jamming or interference signal is present.

Microbial Fuel Cell (MFC) is a promising technology for harnessing energy from organic compounds. However, the low power generation of MFCs remains a significant challenge that hinders their commercial viability. In this study, we reported three distinct modifications to the stainless-steel mesh (SSM), carbon cloth, and carbon felt electrodes using carbon powder (CP), a mixture of CP and ferrum, and a blend of CP with sodium citrate and ethanol. The MFC equipped with an SSM and CP anode showed a notable power density of 1046.89 mW.m-2. In comparison, the bare SSM anode achieved a maximum power density of 145.8 mW m-2. Remarkably, the 3D-modified SSM with a CP anode (3D-SSM-CP) MFC exhibited a substantial breakthrough, attaining a maximum power density of 1417.07 mW m-2. This achievement signifies a significant advancement over the performance of the unaltered SSM anode, underscoring the effectiveness of our modification approach. Subsequently, the 3D-SSM-CP electrode was integrated into single-chamber MFCs, which were used to power a LoRaWAN IoT device through a power management system. The modification methods improved the MFC performance while involving low-cost and easy fabricating techniques. The results of this study are expected to contribute to improving MFC's performance, bringing them closer to becoming a practical source of renewable energy.

Vision Transformers, known for their innovative architectural design and modeling capabilities, have gained significant attention in computer vision. This paper presents a dual-path approach that leverages the strengths of the Multi-Axis Vision Transformer (MaxViT) and the Improved Multiscale Vision Transformer (MViTv2). It starts by encoding speech signals into Constant-Q Transform (CQT) spectrograms and Mel Spectrograms with Short-Time Fourier Transform (Mel-STFT). The CQT spectrogram is then fed into the MaxViT model, while the Mel-STFT is input to the MViTv2 model to extract informative features from the spectrograms. These features are integrated and passed into a Multilayer Perceptron (MLP) model for final classification. This hybrid model is named the “MaxViT and MViTv2 Fusion Network with Multilayer Perceptron (MaxMViT-MLP).” The MaxMViT-MLP model achieves remarkable results with an accuracy of 95.28% on the Emo-DB, 89.12% on the RAVDESS dataset, and 68.39% on the IEMOCAP dataset, substantiating the advantages of integrating multiple audio feature representations and Vision Transformers in speech emotion recognition.