The study of the spread of pandemics, including COVID-19, is an emerging concern to promote self-care management through social distancing, using state-of-the-art tools and technologies. Existing technologies provide many opportunities to acquire and process large volumes of data to monitor user activities from various perspectives. However, determining disease hotspots remains an open challenge considering user activities and interactions; providing related recommendations to susceptible individuals requires attention. In this article, we propose an approach to determine disease hotspots by modeling users’ activities from both cyber- and real-world spaces. Our approach uniquely connects cyber- and physical-world activities to predict hazardous regions. The availability of such an exciting data set is a non-trivial task; therefore, we produce the data set with much hard work and release it to the broader research community to facilitate further research findings. Once the data set is generated, we model it as a directed multi-attributed and weighted graph to apply classical machine learning and graph neural networks for prediction purposes. Our contribution includes mapping user events from cyber- and physical-world aspects, knowledge extraction, dataset generation, and reasoning at various levels. Within our unique graph model, numerous elements of lifestyle parameters are measured and processed to gain deep insight into a person’s status. As a result, the proposed solution enables the authorities of any pandemic, such as COVID-19, to monitor and take measurable actions to prevent the spread of such a disease and keep the public informed of the probability of catching it.

The Ground-Air Frequency Domain Electromagnetic Method (GAFDEM) is a fast and effective semi-airborne electromagnetic exploration method for subsurface anomaly targets. Based on the depth-focused transmission waveform, this method can realize the high-resolution detection of underground targets at specific depths. However, due to the high inductance and resistance parameters of the transmitting load in GAFDEM exploration, the transmission current of the depth-focused waveform decays rapidly in the middle and high-frequency bands, which restricts the detection signal intensity. To solve this problem, a broadband resonant circuit and its parameter design method are proposed. According to the typical transmission frequency range and load, the parameters are designed, and the circuit model is simulated and tested. The results show that the designed broadband resonant circuit can increase the transmission active power of the depth-focused waveform by more than 490%, reduce the reactive power by more than 37%, and increase the transmission current intensity of the target frequency by 2.64 times. Moreover, this circuit has good robustness. It can achieve a good resonance effect within the error range of ±10% of capacitor. This design provides an effective way for GAFDEM to enhance the intensity of high-frequency detection signals and improve the shallow exploration effect.

In this study, we focus on evaluating the false-positive probability of the Demirci-Selçuk meet-in-the-middle attack, particularly within the context of configuring precomputed tables with multisets. During the attack, the adversary effectively reduces the size of the key space by filtering out the wrong keys, subsequently recovering the master key from the reduced key space. The false-positive probability is defined as the probability that a wrong key will pass through the filtering process. Due to its direct impact on the post-filtering key space size, the false-positive probability is an important factor that influences the complexity and feasibility of the attack. However, despite its significance, the false-positive probability of the multiset-based Demirci-Selçuk meet-in-the-middle attack has not been thoroughly discussed, to the best of our knowledge. We generalize the Demirci-Selçuk meet-in-the-middle attack and present a sophisticated method for accurately calculating the false-positive probability. We validate our methodology through toy experiments, demonstrating its high precision. Additionally, we propose a method to optimize an attack by determining the optimal format of precomputed data, which requires the precise false-positive probability. Applying our approach to previous attacks on AES and ARIA, we have achieved modest improvements. Specifically, we enhance the memory complexity and time complexity of the offline phase of previous attacks on 7-round AES-128/192/256, 7-round ARIA-192/256, and 8-round ARIA-256 by factors ranging from 20.56 to 23. Additionally, we have improved the overall time complexity of attacks on 7-round ARIA-192/256 by factors of 20.13 and 20.42, respectively.

This paper presents MDX-Mixer, which improves music demixing (MDX) performance by leveraging source signals separated by multiple existing MDX models. Deep-learning-based MDX models have improved their separation performances year by year for four kinds of sound sources: “vocals,” “drums,” “bass,” and “other”. Our research question is whether mixing (i.e., weighted sum) the signals separated by state-of-the-art MDX models can obtain either the best of everything or higher separation performance. Previously, in singing voice separation and MDX, there have been studies in which separated signals of the same sound source are mixed with each other using time-invariant or time-varying positive mixing weights. In contrast to those, this study is novel in that it allows for negative weights as well and performs time-varying mixing using all of the separated source signals and the music acoustic signal before separation. The time-varying weights are estimated by modeling the music acoustic signals and their separated signals by dividing them into short segments. In this paper we propose two new systems: one that estimates time-invariant weights using 1×1 convolution, and one that estimates time-varying weights by applying the MLP-Mixer layer proposed in the computer vision field to each segment. The latter model is called MDX-Mixer. Their performances were evaluated based on the source-to-distortion ratio (SDR) using the well-known MUSDB18-HQ dataset. The results show that the MDX-Mixer achieved higher SDR than the separated signals given by three state-of-the-art MDX models.

Highly conflicting evidence that may lead to the counter-intuitive results is one of the challenges for information fusion in Dempster-Shafer evidence theory. To deal with this issue, evidence conflict is investigated based on belief divergence measuring the discrepancy between evidence. In this paper, the pignistic probability transform belief χ2 divergence, named as BBχ2 divergence, is proposed. By introducing the pignistic probability transform, the proposed BBχ2 divergence can accurately quantify the difference between evidence with the consideration of multi-element sets. Compared with a few belief divergences, the novel divergence has more precision. Based on this advantageous divergence, a new multi-source information fusion method is devised. The proposed method considers both credibility weights and information volume weights to determine the overall weight of each evidence. Eventually, the proposed method is applied in target recognition and fault diagnosis, in which comparative analysis indicates that the proposed method can realize the highest accuracy for managing evidence conflict.

In academic presentation, the structure design of presentation is critical for making the presentation logical and understandable. However, it is difficult for novice researchers to construct required academic presentation structure due to the flexibility in structure creation. To help novice researchers revise and improve their presentation structure, we propose an academic presentation structure modification support system based on structural elements of the presentation slides. In the proposed system, we build a presentation structural elements model (PSEM) that represents the essential structural elements and their relations to clarify the ideal structure of academic presentation. Based on the PSEM, we also designed two evaluation indices to evaluate the academic presentation structure. To evaluate the proposed system with real-world data, we construct a web application that generates evaluation and feedback to academic presentation slides. The experimental results demonstrate the effectiveness of the proposed system.

We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.

With the advent of the Internet of Things (IoT), short packet transmissions will dominate the future wireless communication. However, traditional coherent demodulation and channel estimation schemes require large pilot overhead, which may be highly inefficient for short packets in multipath fading scenarios. This paper proposes a novel pilot-free short packet structure based on the association of modulation on conjugate-reciprocal zeros (MOCZ) and tail-biting convolutional codes (TBCC), where a noncoherent demodulation and decoding scheme is designed without the channel state information (CSI) at the transceivers. We provide a construction method of constellation sets and demodulation rule for M-ary MOCZ. By deriving low complexity log-likelihood ratios (LLR) for M-ary MOCZ, we offer a reasonable balance between energy and bandwidth efficiency for joint coding and modulation scheme. Simulation results show that our proposed scheme can attain significant performance and throughput gains compared to the pilot-based coherent modulation scheme over multipath fading channels.

We characterized prion disease by comparing brain functional connectivity network (BFCN), which were constructed by 16-ch scalp-recorded electroencephalograms (EEGs). The connectivity between each pair of nodes (electrodes) were computed by synchronization likelihood (SL). The BFCN was applied to graph theory to discriminate prion disease patients from healthy elderlies and dementia groups.

Device-to-device (D2D) relay enhances the capacity of a mobile network. If the channel quality of a user equipment (UE) is bad, the UE asks a neighbor to get its data from the base station and forward the data to it by using D2D communication. Since cellular and D2D communication can share spectrum resources, the spectral efficiency will rise. As UEs are owned by self-interested users, they may not provide relay services gratis. Thus, some incentive methods let UEs exchange tokens to buy and sell relay services. However, they assume that each relay service is worth one token and offers a fixed data rate, which lacks flexibility. Through the law of supply and demand, this paper proposes an economy aware token-based incentive (EAT-BI) strategy. A supplier (i.e., the service provider) charges different prices for its relay service with different rates. A consumer (i.e., the service requestor) takes different policies to choose a supplier based on its tokens and may bargain with suppliers to avoid starvation. Simulation results show that EAT-BI can efficiently promote D2D relay use and increase throughput under different mobility models of UEs.

Design of distributed energy management systems composed of several agents such as factories and buildings is important for realizing smart cities. In addition, demand response for saving the power consumption is also important. In this paper, we propose a design method of distributed energy management systems with real-time demand response, in which both electrical energy and thermal energy are considered. Here, we use ADMM (Alternating Direction Method of Multipliers), which is well known as one of the powerful methods in distributed optimization. In the proposed method, demand response is performed in real-time, based on the difference between the planned demand and the actual value. Furthermore, utilizing a blockchain is also discussed. The effectiveness of the proposed method is presented by a numerical example. The importance of introducing a blockchain is pointed out by presenting the adverse effect of tampering the actual value.

Laser Doppler Vibrometers (LDVs) enable the acquisition of remote speech signals by measuring small-scale vibrations around a target. They are now widely used in the fields of information acquisition and national security. However, in remote speech detection, the coherent measurement signal is subject to environmental noise, making detecting and reconstructing speech signals challenging. To improve the detection distance and speech quality, this paper proposes a highly accurate real-time speech measurement method that can reconstruct speech from noisy coherent signals. First, the I/Q demodulation and arctangent phase discrimination are used to extract the phase transformation caused by the acoustic vibration from coherent signals. Then, an innovative smoothness criterion and a novel phase difference-based dynamic bilateral compensation phase unwrapping algorithm are used to remove any ambiguity caused by the arctangent phase discrimination in the previous step. This important innovation results in the highly accurate detection of phase jumps. After this, a further innovation is used to enhance the reconstructed speech by applying an improved waveform-based linear prediction coding method, together with adaptive spectral subtraction. This removes any impulsive or background noise. The accuracy and performance of the proposed method were validated by conducting extensive simulations and comparisons with existing techniques. The results show that the proposed algorithm can significantly improve the measurement of speech and the quality of reconstructed speech signals. The viability of the method was further assessed by undertaking a physical experiment, where LDV equipment was used to measure speech at a distance of 310m in an outdoor environment. The intelligibility rate for the reconstructed speech exceeded 95%, confirming the effectiveness and superiority of the method for long-distance laser speech measurement.

This paper proposes individual computation processes of the partial demodulation reference signal (DM-RS) sequence in a synchronization signal (SS)/physical broadcast channel (PBCH) block to be used to detect the radio frame timing based on SS/PBCH block index detection for New Radio (NR) initial access. We present the radio frame timing detection probability using the proposed partial DM-RS sequence detection method that is applied subsequent to the physical-layer cell identity (PCID) detection in five tapped delay line (TDL) models in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. Computer simulation results show that by using the proposed method, the radio frame timing detection probabilities of almost 100% and higher than 90% are achieved for the LOS and NLOS channel models, respectively, at the average received signal-to-noise power ratio (SNR) of 0dB with the frequency stability of a local oscillator in a set of user equipment (UE) of 5ppm at the carrier frequency of 4GHz.

We propose a non-invasive and cost-effective method to automatically detect dementia by utilizing solely speech audio data. We extract paralinguistic features for a short speech segment and use Gated Convolutional Neural Networks (GCNN) to classify it into dementia or healthy. We evaluate our method on the Pitt Corpus and on our own dataset, the PROMPT Database. Our method yields the accuracy of 73.1% on the Pitt Corpus using an average of 114 seconds of speech data. In the PROMPT Database, our method yields the accuracy of 74.7% using 4 seconds of speech data and it improves to 80.8% when we use all the patient's speech data. Furthermore, we evaluate our method on a three-class classification problem in which we included the Mild Cognitive Impairment (MCI) class and achieved the accuracy of 60.6% with 40 seconds of speech data.

We propose a spectrum regeneration and demodulation method for multiple direct RF undersampled real signals by using a new algorithm. Many methods have been proposed to regenerate the RF spectrum by using undersampling because of its simple circuit architecture. However, it is difficult to regenerate the spectrum from a real signal that has a band wider than a half of the sampling frequency, because it is difficult to include complex conjugate relation of the folded spectrum into the linear algebraic equation in this case. We propose a new spectrum regeneration method from direct undersampled real signals that uses multiple clocks and an extended algorithm considering the complex conjugate relation. Simulations are used to verify the potential of this method. The validity of the proposed method is verified by using the simulation data and the measured data. We also apply this algorithm to the demodulation system.

To realize an information-centric networking, IPFS (InterPlanetary File System) generates a unique ContentID for each content by applying a cryptographic hash to the content itself. Although it could improve the security against attacks such as falsification, it makes difficult to realize a similarity search in the framework of IPFS, since the similarity of contents is not reflected in the proximity of ContentIDs. To overcome this issue, we propose a method to apply a locality sensitive hash (LSH) to feature vectors extracted from contents as the key of indexes stored in IPFS. By conducting experiments with 10,000 random points corresponding to stored contents, we found that more than half of randomly given queries return a non-empty result for the similarity search, and yield an accurate result which is outside the σ confidence interval of an ordinary flooding-based method. Note that such a collection of random points corresponds to the worst case scenario for the proposed scheme since the performance of similarity search could improve when points and queries follow an uneven distribution.

In March 2020, the 5th generation mobile communication system (5G) was launched in Japan. Frequency bands of 3.7GHz, 4.5GHz and 28GHz were allocated for 5G services, and the 5G use cases fall into three broad categories: Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). The use cases and services that take advantage of the characteristics of each category are expected to be put to practical use, and experiments of practical use are underway. This paper introduces and demonstrates a touchless gate that can identify, authenticate and allow passage through the gate by using these features and 5G beam tracking to estimate location by taking advantage of the low latency of 5G and the straightness of the 28GHz band radio wave and its resistance to spreading. Since position estimation error due to reflected waves and other factors has been a problem, we implement an algorithm that tracks the beam and estimates the user's line of movement, and by using an infrared sensor, we made it possible to identify the gate through which the user passes with high probability. We confirmed that the 5G touchless gate is feasible for gate passage. In addition, we demonstrate that a new service based on high-speed high-capacity communication is possible at gate passage by taking advantage of the wide bandwidth of the 28GHz band. Furthermore, as a use case study of the 5G touchless gate, we conducted a joint experiment with an airline company.

This paper proposes out-of-bound signal demapping for lattice reduction-aided iterative linear receivers in overloaded MIMO channels. While lattice reduction aided linear receivers sometimes output hard-decision signals that are not contained in the modulation constellation, the proposed demapping converts those hard-decision signals into binary digits that can be mapped onto the modulation constellation. Even though the proposed demapping can be implemented with almost no additional complexity, the proposed demapping achieves more gain as the linear reception is iterated. Furthermore, we show that the transmission performance depends on bit mapping in modulations such as the Gray mapping and the natural mapping. The transmission performance is confirmed by computer simulation in a 6 × 2 MIMO system, i.e., the overloading ratio of 3. One of the proposed demapping called “modulo demapping” attains a gain of about 2 dB at the packet error rate (PER) of 10-1 when the 64QAM is applied.

We address analysis and design problems of aggregate demand response systems composed of various consumers based on controllability to facilitate to design automated demand response machines that are installed into consumers to automatically respond to electricity price changes. To this end, we introduce a controllability index that expresses the worst-case error between the expected total electricity consumption and the electricity supply when the best electricity price is chosen. The analysis problem using the index considers how to maximize the controllability of the whole consumer group when the consumption characteristic of each consumer is not fixed. In contrast, the design problem considers the whole consumer group when the consumption characteristics of a part of the group are fixed. By solving the analysis problem, we first clarify how the controllability, average consumption characteristics of all consumers, and the number of selectable electricity prices are related. In particular, the minimum value of the controllability index is determined by the number of selectable electricity prices. Next, we prove that the design problem can be solved by a simple linear optimization. Numerical experiments demonstrate that our results are able to increase the controllability of the overall consumer group.

This paper proposes an optimization model under uncertain traffic demands to design the backup network to minimize the total capacity of a backup network to protect the primary network from multiple link failures, where the probability of link failure is specified. The hose uncertainty is adopted to express uncertain traffic demands. The probabilistic survivability guarantee is provided by determining both primary and backup network routing, simultaneously. Robust optimization is introduced to provide probabilistic survivability guarantees for different link capacities in the primary network model under the hose uncertainty. Robust optimization in the proposed model handles two uncertain items: uncertain failed primary link with different capacities and uncertain traffic demands. We formulate an optimization problem for the proposed model. Since it is difficult to directly solve it, we introduce a heuristic approach for the proposed model. By using the heuristic approach, we investigate how the probability of link failure affects both primary and backup network routing. Numerical results show that the proposed model yields a backup network with lower total capacity requirements than the conventional model for the link failure probabilities examined in this paper. The results indicate that the proposed model reduces the total capacity of the backup network compared to the conventional model under the hose uncertainty. The proposed model shares more effectively the backup resources to protect primary links by determining routing in both primary and backup networks.