We introduce a wearable-based system for real-time ECG anomaly detection and contextual interpretation within a mobile-health framework. Twenty-four-hour Holter ECG data are synchronized over wireless/mobile networks with e.g. Apple Health streams (iPhone + iWatch), including activity states (walking, running, resting, sleeping) and heart rate history. A hybrid preprocessing pipeline extracts instantaneous frequency (Hilbert), spectral entropy, and RMS energy, concatenated into fixed-length multichannel tensors for deep-learning models deployed via edge or cloud SaaS. The model detects critical cardiac anomalies correlating each with user activity and exertion context. This multimodal approach distinguishes physiological deviations during motion from pathological events at rest or sleep and suppresses motion artifacts. Experiments with subjects wearing both Holter and Apple devices demonstrate improved sensitivity and specificity versus ECG-only baselines. Our system exemplifies wearable computing, mobile health, ML-enabled mobile systems, and edge/cloud mobile analytics. Fig. 1 shows a complete system for recording and classifying ECG signals, including a Holter ECG with electrodes, a smartphone and a smartwatch [1].

Damp walls are a significant problem that affects not only historical buildings. The effects of moisture inside walls include premature degradation of the structure and paintwork as well as health hazards for people staying inside the rooms (fungi, microorganisms, allergens). To effectively remove moisture, it is necessary to identify the areas where it occurs. Tomography is the only nondestructive method that allows for imaging the interior of walls. It is not common due to the low image resolution [1]. The aim of the research presented is to present a new concept of impedance tomography, taking into account many measurement sequences at different frequencies of electric current. A neural network with LSTM (Long Short-Term Memory) layers was used to transform the measurements into images. A comparison of the results of the new approach proves the advantage of the multi-frequency method over the traditional method, which brings closer the breakthrough moment in the dissemination of tomography as the main method of imaging moisture in walls.

The study investigates the effectiveness of a differential neural network architecture in ultrasonic tomography (UST) for industrial applications. The proposed model employs a dual-branch structure, where each branch independently processes identical input data before passing the outputs to a differential layer. This approach enhances the model's ability to capture residual components, improving the reconstruction of tomographic images. Experiments were conducted using a tomographic system with 16 transducers, generating training and validation datasets. Comparative analysis between a differential LSTM-based network and a standard LSTM model demonstrated that the differential architecture achieved superior reconstruction quality. The results confirm that this approach enhances accuracy, sharpness, and overall image clarity, making it a promising solution for improving UST image reconstruction.

This paper introduces the development of a beamforming defectoscope tailored for advanced non-invasive techniques. The device employs beamforming technology to enhance the detection and characterization of defects in a range of materials. Through the use of sophisticated signal processing algorithms, the defectoscope increases both the resolution and accuracy of non-invasive inspections. The study underscores the superior performance of the beamforming method when compared to traditional approaches, demonstrating notable improvements in diagnostic capabilities. This cutting-edge tool holds promising applications across multiple industries, such as aerospace, civil engineering, and manufacturing, where accurate defect detection is essential.

The main goal of the research was to develop an effective, highresolution tomographic apparatus capable of non-invasively capturing real-time internal images of industrial tank reactors. For this purpose, a prototype of an ultrasonic tomograph (UST) was developed, which combines innovative design solutions and modern algorithmic techniques. A special feature of the presented solution is the use of a neural network with an unusual architecture. A deep, multi-branch neural network consisting of two inputs was used. The first input is a 120-element vector (sequence) of raw measurements. The third input consists of three sequences obtained as a result of the transformation of raw measurements: instantenous frequency (IF), approximation coefficients (Ca), and detail coefficients (Cd). The prototype was tested on a real model. The tomographic reconstructions obtained using the innovative neural architecture were compared with images obtained using a standard neural network. The results clearly confirm the high effectiveness of the presented approach.

This study presents a novel technique for reconstructing the internal structures of industrial tank reactors using electrical impedance tomography (EIT). The method uses three different measurement vectors, each corresponding to different electrical frequencies---100 kHz, 50 kHz, and 10 kHz---to improve the accuracy and reliability of EIT reconstructions. The goal was to get the most out of both the resistive and reactive data from the EIT system by using machine learning methods that took frequency-specific data into account. This data was shown as complex numbers. To process the multi-frequency data collected from the measurements, an LSTM network was used. The results show that the multi-frequency model significantly outperforms single-frequency methods in terms of reconstruction accuracy.

Ultrasound computer tomography (USCT) represents a medical imaging modality designed to visualize alterations in the speed of ultrasonic waves. The primary objective of the study presented was to devise a lightweight, portable, and cost-effective tomographic device capable of non-invasively capturing internal images of the human brain in real-time. To achieve this aim, a prototype ultrasonic tomograph was developed, comprising a lightweight head hoop integrated with ultrasonic transducers and a tomograph unit. Ultrasonic measurements were transformed into images using a heterogeneous convolutional neural network (CNN). The USCT system was engineered to facilitate wireless communication between the sensors embedded within the wearable head cap and the tomographic apparatus.

The primary objective of the research is the development of a lightweight and cost-effective headband-style tomographic apparatus capable of non-invasively capturing real-time internal cerebral images. A prototype of an ultrasonic tomograph was engineered, comprising a lightweight cranial band synergized with ultrasonic transducers and the tomographic system. Ultrasonic measurements were transmuted into visualizations via a heterogeneous convolutional neural network (CNN). The Ultrasonic Computed Tomography (USCT) architecture was conceived to facilitate untethered data interchange between the head-worn sensor array and the tomographic machinery.

This study aims to improve the resolution of reconstructed images from industrial ultrasonic tomography (UST) by determining the most effective neural network structure for solving the inverse problem based on the measurements taken. The study analyzed three types of neural networks: Artificial Neural Networks, Convolutional Neural Networks (CNNs) and a hybrid of CNNs and Long Short-Term Memory networks (LSTM). After evaluating the reconstructions and quality indicators, the CNN-LSTM combination provided the most accurate image reconstructions of the industrial ultrasound tomography, highlighting the importance of selecting an appropriate neural network to improve the resolution of the reconstructed images.

Wireless sensor networks, made so that objects and people can be found without devices, are an important part of our high-tech world. This poster aims to show how heterogeneous convolutional neural networks can be used to improve a radio tomographic imaging system that can find people indoors precisely. In addition to original algorithmic solutions, the system's advantages include using properly designed and integrated devices---radio probes---whose task is to emit Wi-Fi waves and measure the received signal strength. Thanks to the use of the two-stage approach, the sensitivity, resolution, and accuracy of imaging have increased. Furthermore, our solution works well for radio tomography and other types of tomography because it is easy to understand and can be used in many ways.

Damp walls of buildings are a serious problem due to the social and economic consequences. Moisture causes accelerated wear of facades, paint coatings, weakening of the wall structure, and high maintenance and renovation costs. The growth of fungi and bacteria worsens the indoor microclimate [1]. Effective identification of moisture inside the walls enables effective preventive actions. The paper presents an algorithmic concept that increases the quality of tomographic images showing the distribution of moisture inside the walls. The method solves the problem of monitoring the dampness of historical buildings and walls susceptible to moisture. The research focuses on solving the inverse problem of converting electrical measurements into spatial images. The study used a proprietary electrical impedance tomography system with specially designed electrodes. The measurement vector is converted to images in two stages. In the first stage, the Long Short-Term Memory (LSTM) neural network was used, which generates raw reconstructions. The task of the second LSTM network is to convert the raw images obtained in the first stage into enhanced images. The application of the presented method is not limited to one type of narrow-sphere tomography. The two-stage approach can be easily adapted to, e.g., medical and industrial or process tomography. Therefore, it is a generic, universal method with great implementation potential, which is its great advantage.

The aim of the research is to develop a system enabling effective and efficient tracking of people inside buildings using radio waves. The presented concept uses radio tomography imaging (RTI) as a passive analysis of radio wave interference as well as active connections with transmitting and receiving devices---mainly smartphones. A long short-term memory (LSTM) neural network was used to solve the inverse tomographic problem of converting measurements into images. The presented concept uses a proprietary design of transducers, which are transmitting and receiving devices that can exchange information with each other and establish connections with other devices. The novelty is the hybrid nature of the people location system, using both device-free and device-based methods. Another new approach is using the LSTM network to solve the inverse problem in RTI. Both solutions make the location system much more flexible, which makes imaging much more accurate and reliable.

The study aims to develop a measurement system for acquiring ultrasonic transmission tomography data. Along building the automated measurement system we test the algorithm which use incorporation of image compression into the process of reconstruction what speed up computation and simplifies regularization of the solution to the inverse problem of tomography. The reconstructive algorithm used in transmission tomography is based on the Discrete Cosine Transformation method. The algorithm applies image compression techniques for each block of the image separately, according to the conducted test such a solution seems to be much more robust than classical methods used for ultrasonic tomography. Presented work is a part of the research that aim for applying ultrasonic technology for analyse and visualise technological processes in distributed systems and in the next step for development of wearable equipment for monitoring in medicine.

This paper presents the results of research on the identification of moisture inside the walls of buildings with the use of non-invasive electrical impedance tomography (EIT). The novelty and contribution of this research is the development of an original algorithmic method to solve the ill posedness, inverse problem. Since the new algorithm optimizes the method for each pixel of the tomographic image, taking into account a specific measurement vector, regardless of what and how many homogeneous methods are included in the algorithm, the obtained results are more accurate than those obtained with the use of homogeneous methods. As part of the research, prototypes of the EIT tomograph and electrodes for examining walls were designed and manufactured.

The following paper discusses the development of a risk-based cost estimation model for completing non-standard manufacturing orders.The model in question is a hybrid of Monte Carlo Simulation (MCS), which constitutes the main module of the applied model.Vector of order risk probability, which is the input data for the MCS module, is highly difficult to assess and is burdened to a considerable degree with subjectivity, therefore it was resolved that it should be generated with the application of artificial intelligence.Depending on the accessibility of historical data, the model incorporates fuzzy logic or artificial neural networks methods.The presented model could provide support to managers responsible for cost estimation, and moreover, after slight modification also in setting deadlines for non-standard manufacturing orders.I.