This paper introduces a new speech enhancement algorithm based on the adaptive threshold of intrinsic mode functions (IMFs) of noisy signal frames extracted by empirical mode decomposition. Adaptive threshold values are estimated by using the gamma statistical model of Teager energy operated IMFs of noisy speech and estimated noise based on symmetric Kullback–Leibler divergence. The enhanced speech signal is obtained by a semisoft thresholding function, which is utilized by threshold IMF coefficients of noisy speech. The method is tested on the NOIZEUS speech database and the proposed method is compared with wavelet-shrinkage and EMD-shrinkage methods in terms of segmental SNR improvement (SegSNR), weighted spectral slope (WSS), and perceptual evaluation of speech quality (PESQ). Experimental results show that the proposed method provides a higher SegSNR improvement in dB, lower WSS distance, and higher PESQ scores than wavelet-shrinkage and EMD-shrinkage methods. The proposed method shows better performance than traditional threshold-based speech enhancement approaches from high to low SNR levels.

In this paper, we propose the selection of most suitable mother wavelet function for Turkish phonemes using discrete wavelet transform. The determination of most similar mother wavelet function to the signal has been a challenge in speech processing. The optimum mother wavelet function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from wavelet coefficients of the phonemes. In this study, 101 potential functions were investigated to determine the most appropriate mother wavelet. Experimental results show that the most appropriate wavelet functions for /ç/ and /ş/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer wavelet functions are the most suitable mother wavelets for all other phonemes.

Recent advances in deep learning have fostered a transition from the traditional, bit-centric paradigm of Shannon’s information theory to a semantic-oriented approach, emphasizing the transmission of meaningful information rather than mere data fidelity. However, black-box AI-based semantic communication lacks structured discretization and remains dependent on analog modulation, which presents deployment challenges. This paper presents a new semantic-aware digital speech communication system, named Hybrid-DeepSCS, a stepping stone between traditional and fully end-to-end semantic communication. Our system comprises the following parts: a semantic encoder for extracting and compressing structured features, a standard transmitter for digital modulation including source and channel encoding, a standard receiver for recovering the bitstream, and a semantic decoder for expanding the features and reconstructing speech. By adding semantic encoding to a standard digital transmission, our system works with existing communication networks while exploring the potential of deep learning for feature representation and reconstruction. This hybrid method allows for gradual implementation, making it more practical for real-world uses like low-bandwidth speech, robust voice transmission over wireless networks, and AI-assisted speech on edge devices. The system’s compatibility with conventional digital infrastructure positions it as a viable solution for IoT deployments, where seamless integration with legacy systems and energy-efficient processing are critical. Furthermore, our approach addresses IoT-specific challenges such as bandwidth constraints in industrial sensor networks and latency-sensitive voice interactions in smart environments. We test the system under various channel conditions using Signal-to-Distortion Ratio (SDR), PESQ, and STOI metrics. The results show that our system delivers robust and clear speech, connecting traditional wireless systems with the future of AI-driven communication. The framework’s adaptability to edge computing architectures further underscores its relevance for IoT platforms, enabling efficient semantic processing in resource-constrained environments.

Heart rate is the main data that shows if the heart is working properly. Therefore, obtaining the heart rate information has a vital importance. There are some methods to measure the heart rate, but the most commonly used one is the Electrocardiography (ECG). However, this method is expensive and non-portable. Therewithal, optical studies have recently been conducted to measure heart rate. Being non-invasive, inexpensive, and safe are the advantages of optical measurements. Laser speckle contrast imaging is an effective and simple technique for imaging heterogeneous environments such as human and animal tissues. By laser speckle contrast analysis, heart rate can be obtained easily. It is the standard technique, but fractal analysis method is also very convenient way to study speckle images because speckle pattern is quite appropriate for studying fractality due to its granular structure. In this paper, we present fractal analysis method for obtaining heart rate information from speckle images. The results of this method for the various in-vivo and in-vitro data were compared with the reference model results of speckle contrast analysis method and it is observed that the proposed analysis method has provided sufficient results.

The segmentation of liver and liver tumors plays a critical role in clinical oncology, radiology, and surgical applications. Manual or semi-automatic segmentation methods are not only time-consuming, but also prone to subjectivity. In contrast, deep learning-based methods offer an automated approach that delivers objective and reproducible results. In this study, we evaluate several deep learning architectures—including established models such as U-Net and ResNet-based methods—and introduce novel segmentation algorithms that have been successfully applied in other imaging contexts yet remain untested on liver images. The Liver Tumor Segmentation Benchmark (LiTS) dataset, with its large number of annotated liver and tumor images, has been selected to ensure a fair and comprehensive evaluation. Segmentation performance is assessed using supervised metrics such as precision, recall, Intersection over Union (IoU), and Dice coefficient. Experimental results suggest that the optimal segmentation method can significantly aid physicians in accurate diagnosis and treatment planning, while also serving as a valuable tool in medical education and clinical decision support systems. This innovative approach not only advances the field of medical image analysis, but also has the potential to enhance clinical workflows and enable personalized treatment strategies. This study compares the performance of various deep learning-based methods for liver and tumor segmentation. Using the LiTS dataset, we evaluated the performance of U-Net, DeepLabV3+, and SegFormer models. IoU and Dice coefficients were utilized to measure segmentation success. The U- Net model demonstrated high performance with limited data, while DeepLabV3+ yielded better results on large datasets. SegFormer showed promising performance, highlighting the effectiveness of the Vision Transformer-based approach.