In emergency communication systems research, aerial wireless relay networks (AWRNs) using multicopter unmanned aerial vehicles (UAVs) have been proposed. The main issue of the AWRNs is how to minimize the delay time of packet transmissions since it is not easy to supply many multicopters to cover a wide area. In this paper, we review the flight schemes and their delay time for the AWRNs. Furthermore, the network has specific issues such as multicopters' drops due to their battery capacity depletion and inclination of moving multicopters. The inclination of multicopters affects the received power, and the communication range changes based on the inclination as well. Therefore, we clarify the effect of these issues on the delay time.

Autonomous mobility machines, such as self-driving cars, transportation robots, and automated construction machines, are promising to support or enrich human lives. To further improve such machines, they will be connected to the network via wireless links to be managed, monitored, or remotely operated. The autonomous mobility machines must have self-status based on their positioning system to safely conduct their operations without colliding with other objects. The self-status is not only essential for machine operation but also it is valuable for wireless link quality management. This paper presents self-status-based wireless link quality prediction and evaluates its performance by using a prototype mobility robot combined with a wireless LAN system. The developed robot has functions to measure the throughput and receive signal strength indication and obtain self-status details such as location, direction, and odometry data. Prediction performance is evaluated in offline processing by using the dataset gathered in an indoor experiment. The experiments clarified that, in the 5.6 GHz band, link quality prediction using self-status of the robot forecasted the throughput several seconds into the future, and the prediction accuracies were investigated as dependent on time window size of the target throughput, bandwidth, and frequency gap.

Since wild animals are causing more accidents and damages, it is important to safely detect them as early as possible. In this paper, we propose two battery-powered wild animal detection nodes based on deep learning that can automatically detect wild animals; the detection information is notified to the people concerned immediately. To use the proposed nodes outdoors where power is not available, we devise power saving techniques for the proposed nodes. For example, deep learning is used to save power by avoiding operations when wild animals are not detected. We evaluate the operation time and the power consumption of the proposed nodes. Then, we evaluate the energy consumption of the proposed nodes. Also, we evaluate the detection range of the proposed nodes, the accuracy of deep learning, and the success rate of communication through field tests to demonstrate that the proposed nodes can be used to detect wild animals outdoors.

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

The impedance expansion method (IEM) is a circuit-modeling technique for electrically small devices based on the method of moments. In a previous study, a circuit model of a wireless power transfer (WPT) system was developed by utilizing the IEM and eigenmode analysis. However, this technique assumes that all the coupling elements (e.g., feeding loops and resonant coils) are in the absence of neighboring scatters (e.g., bodies of vehicles). This study extends the theory of the IEM to obtain the circuit model of a WPT system in the vicinity of a perfectly conducting scatterer (PCS). The numerical results show that the proposed method can be applied to the frequencies at which the dimension of the PCS is less than approximately a quarter wavelength. In addition, the yielded circuit model is found to be valid at the operating frequency band.

In a multiple-user MIMO system in which numerous users simultaneously communicate in a cell, the channel matrix properties depend on the parameters of the individual users in such a way that they can be modeled as points randomly moving within the cell. Although these properties can be simulated by computer, they need to be expressed analytically to develop MIMO systems with diversity. Given a small area with an equivalent multi-path, we assume that a user u is at a certain “user point” $P^u(\lambda _p^u,\xi _p^u)$ in a cell, or (radius $\lambda _p^u$ from origin, angle $\xi _p^u)$ and that the user moves with movement $M^u(f_{max}^u, \xi_v^u)$ around that point, or (Doppler frequency $f_{max}^u$, direction $\xi_v^u$). The MU-MIMO channel model consists of a multipath environment, user parameters, and antenna configuration. A general formula of the correlation $\rho_{i - j,i' - j'}^{u - u'} (bm)$ between the channel matrix elements of users u and u' and one for given multipath conditions are derived. As a feature of the MU-MIMO channel, the movement factor $F^{u - u'}(\gamma^u,\xi_n ,\xi_v^u)$, which means a fall coefficient of the spatial correlation calculated from only the user points of u and u', is also derived. As the difference in speed or direction between u and u' increases, $F^{u - u'}(\gamma^u,\xi_n ,\xi_v^u)$ becomes smaller. Consequently, even if the path is LOS, $\rho_{i - j,i' - j'}^{u - u'} (bm)$ becomes low enough owing to the movement factor, even though the correlation in the single-user MIMO channel is high. If the parameters of u and u' are the same, the factor equals 1, and the channels correspond to the users' own channels and work like SU-MIMO channel. These analytical findings are verified by computer simulation.

Air discharge immunity testing for electronic equipment is specified in the standard 61000-4-2 of the International Eelectrotechnical Commission (IEC) under the climatic conditions of temperature (T) from 15 to 35 degrees Celsius and relative humidity (RH) from 30 to 60%. This implies that the air discharge testing is likely to provide significantly different test results due to the wide climatic range. To clarify effects of the above climatic conditions on air discharge testing, we previously measured air discharge currents from an electrostatic discharge (ESD) generator with test voltages from 2kV to 15kV at an approach speed of 80mm/s under 6 combinations of T and RH in the IEC specified range and non-specified climatic range. The result showed that the same absolute humidity (AH), which is determined by T and RH, provides almost the identical waveforms of the discharge currents despite different T and RH, and also that the current peaks at higher test voltages decrease as the AH increases. In this study, we further examine the combined effects of air discharges on test voltages, T, RH and AH with respect to two different approach speeds of 20mm/s and 80mm/s. As a result, the approach speed of 80mm/s is confirmed to provide the same results as the previous ones under the identical climatic conditions, whereas at a test voltage of 15kV under the IEC specified climatic conditions over 30% RH, the 20mm/s approach speed yields current waveforms entirely different from those at 80mm/s despite the same AH, and the peaks are basically unaffected by the AH. Under the IEC non-specified climatic conditions with RH less than 20%, however, the peaks decrease at higher test voltages as the AH increases. These findings obtained imply that under the same AH condition, at 80mm/s the air discharge peak is not almost affected by the RH, while at 20mm/s the lower the RH is, the higher is the peak on air discharge current.

The spreading technique can improve system performance since it mitigates the influence of deeply faded subcarrier channels. Proposals for implementing orthogonal frequency division multiplexing (OFDM) systems include frequency symbol spreading (FSS) based on the Walsh-Hadamard transform (WHT) and the discrete Fourier transform (DFT). In a single carrier frequency division multiplexing (SC-FDMA), good performance is obtained by the interleaved subcarrier allocation. Moreover, in a multiple-input multiple-output (MIMO), interleaving the operation of the different transmit antennas is also effective. By combining these techniques, in this paper, we propose the different antenna interleaved allocation with the full and divided WHT/DFT spreading for a high time resolution carrier interferometry (HTRCI) MIMO-OFDM.

This paper investigates the resource allocation problem for the downlink of non-orthogonal multiple access (NOMA) networks. A novel resource allocation method is proposed to deal with the problem of maximizing the system capacity while taking into account user fairness. Since the optimization problem is nonconvex and intractable, we adopt the idea of step-by-step optimization, decomposing it into user pairing, subchannel and power allocation subproblems. First, all users are paired according to their different channel gains. Then, the subchannel allocation is executed by the proposed subchannel selection algorithm (SSA) based on channel priority. Once the subchannel allocation is fixed, to further improve the system capacity, the subchannel power allocation is implemented by the successive convex approximation (SCA) approach where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. To ensure user fairness, the upper and lower bounds of the power allocation coefficients are derived and combined by introducing the tuning coefficients. The power allocation coefficients are dynamically adjustable by adjusting the tuning coefficients, thus the diversified quality of service (QoS) requirements can be satisfied. Finally, simulation results demonstrate the superiority of the proposed method over the existing methods in terms of system performance, furthermore, a good tradeoff between the system capacity and user fairness can be achieved.

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

Low Earth Orbit (LEO) satellite networks serve as a powerful complement to the terrestrial networks because of their ability to provide global coverage. In LEO satellite networks, the network is prone to congestion due to several reasons. First, the terrestrial gateways are usually located within a limited region leading to congestion of the nodes near the gateways. Second, routing algorithms that merely adopt shortest paths fail to distribute the traffic uniformly in the network. Finally, the traffic input may exceed the network capacity. Therefore, rate control and load-balancing routing are needed to alleviate network congestion. Moreover, different kinds of traffic have different Quality of Service (QoS) requirements which need to be treated appropriately. In this paper, we investigate joint rate control and load-balancing routing in LEO satellite networks to tackle the problem of network congestion while considering the QoS requirements of different traffic. The joint rate control and routing problem is formulated with the throughput and end-to-end delay requirements of the traffic taken into consideration. Two routing schemes are considered which differ in whether or not different traffic classes can be assigned different paths. For each routing scheme, the joint rate control and routing problem is formulated. A heuristic algorithm based on simulated annealing is proposed to solve the problems. Besides, a snapshot division method is proposed to increase the connectivity of the network and reduce the number of snapshots by merging the links between satellites and gateways. The simulation results show that compared with methods that perform routing and rate control separately, the proposed algorithm improves the overall throughput of the network and provides better QoS guarantees for different traffic classes.