The problem related to energy conservation in a resource‐constrained wireless visual sensor network applied for habitat monitoring, border patrol, battlefield surveillance and so on, where images have to be transmitted over a wireless medium with limited bandwidth, is addressed. The proposed approximation band transform algorithm is unique and novel since it extracts and encodes only approximations of the image using fixed‐point arithmetic without applying highpass analysis filters. This low bit rate image compression algorithm is specifically designed for resource‐constrained low‐power sensors. Its performance is analysed in terms of bit rate (bits per pixel), image quality, processing time and its energy consumption in an Atmel Atmega128 processor. It is shown that this algorithm consumes only 12% of energy needed by joint photographic experts group (JPEG) [independent JPEG group (IJG)] version and gives better results than JPEG at very low bit rates and thus the proposed scheme can significantly enhance the lifetime of low‐power sensors.

The resource-constrained camera integrated Visual Sensor Networks (VSN) have conquered numerous visual aided services from visual surveillance to habitat monitoring. VSN is capable of sensing, processing and communicating visual data wirelessly. These networks are built with inexpensive low power sensor motes with a lightweight processor, limited storage, and bandwidth. The huge amount of redundancy present in the images makes the processing and communication consume more energy than expected. The number of bits must be reduced using energy-efficient compression techniques for efficient transmission. Low computational energy and communication energy are always favored for an increased lifetime of the wireless sensor network. The highly sensitive and self-descriptive nature of images makes security in VSN even more critical. In this work, we propose an energy-efficient low bitrate secured image coder for resource-constrained VSN. Light weight design protocols are highly required in secured image transmission over VSN. Through this communication, we also propose a novel chaotic map using Pascal’s triangle. The system follows a unique interleaved fashion of compression and encryption process to consume less computational resources. A series of tests were carried out to validate the secured image coder’s ruggedness and its suitability in VSN. The performance and the strength of the low bitrate secured image coder are tested with compression efficiency and cryptanalysis tests. Simulations were carried out in Atmel’s ATmega128 processor for energy consumption analysis. The energy consumed by the proposed system for compression, encryption and transmission of an image of size 512 x 512 is 109.364mJ (milli Joules), which is only 4.57 % of the energy consumed by raw image transmission. In addition, the system is implemented in real time image sensor platform based on Arduino Due board integrated with OV7670 camera module for real time verification and the experimental results validated the suitability of the indicated scheme in real time VSN.

Medical imaging provides vital information about the respiratory system for health care diagnosis, counseling, and research purposes. Lung segmentation is essential for diagnosing lung conditions. Currently, deep learning algorithms are commonly used to segment images, assisting doctors in diagnosing lung diseases. However, this process is time-consuming, prone to errors, and requires medical expertise. Therefore, an enhanced two-stage segmentation framework for lung region segmentation is proposed in this research in order to improve the accuracy of lung area segmentation in chest X-rays. In the first phase, this framework utilizes deep learning models, specifically the U-Net and ResU-Net models, for the initial pre-segmentation of chest X-ray images. Segmented mask images obtained from the U-Net and ResU-Net models will undergo a Haar transform and spatial frequency fusion. Following image fusion, an inverse Haar transform will be applied, and typical image processing methods will be used for post-processing and refinement of the final segmented chest X-ray images. This approach aims to enhance the accuracy and quality of segmentation results, facilitating improved analysis and interpretation by medical professionals for diagnostic purposes. The proposed framework has demonstrated high accuracy rates of 98.42%, 98.28%, and 98.99% on JSRT, MC, and Shenzhen datasets, which are publicly available Chest X-Ray datasets.

Speech Disfluency detection and classification are critical in speech therapy because they aid in tracking disfluency progression and are a major tool in technology-assisted speech therapy. Existing methods for detecting disfluency in speech are heavily reliant on annotated data, which can be costly. Machine learning algorithms are rapidly gaining recognition for assessing speech fluency, reducing human error and minimizing therapy delays, making them a preferred method over manual diagnosis. Furthermore, these algorithms do not account for variable-length disfluent speech, limiting the scalability of detection systems. For the automated detection of speech impairment, a Pipelined Deep Learner-Dual Classifier (PDL-DC) is proposed. Individuals’ speech fluency was evaluated in two stages: the classification of speech disfluencies and disorders into several groups such as revisions, prolongations, whole-word repetitions, word-medial repetitions, and filled pauses. The second aspect of classification pertains to the assessment of fluency levels, wherein speakers are classified into three categories: healthy individuals, individuals with stuttering, and individuals with Specific Language Impairment (SLI). The proposed model’s implementation of a pipelined design enables the dual validation of a subject’s fluency. The proposed model demonstrates an average classification accuracy, precision, and recall of 97%.