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Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients' conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack attention to local features, leading to the loss of detailed information in tiny lesion regions. To address these issues, we propose a multi-scale feature fusion network, ECF-Net, based on channel enhancement. Specifically, we leverage the learning capabilities of both CNN and Transformer architectures to design parallel channel extraction blocks in three different ways, effectively capturing diverse lesion features. Additionally, to minimize irrelevant information in the high-dimensional feature space and focus the network on useful and critical information, we develop adaptive feature generation blocks. Lastly, a bidirectional pyramid-structured feature fusion approach is introduced to integrate features at different levels, enhancing the diversity of feature representations and improving segmentation accuracy for lesions of various scales. The proposed method is tested on four COVID-19 datasets, demonstrating mIoU values of 84.36%, 87.15%, 83.73%, and 75.58%, respectively, outperforming several current state-of-the-art methods and exhibiting excellent segmentation performance. These findings provide robust technical support for medical image segmentation in clinical practice. [ABSTRACT FROM AUTHOR]

Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m2 and the other measuring 38 m2, with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method. [ABSTRACT FROM AUTHOR]

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target's location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization. [ABSTRACT FROM AUTHOR]

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information. [ABSTRACT FROM AUTHOR]