A Very Low Complexity QRD-M MIMO Detection Based on Adaptive Search Area

We propose a low complexity QR decomposition (QRD)-M multiple input multiple output (MIMO) detection algorithm based on adaptive search area. Unlike the conventional QRD-M MIMO detection algorithm, which determines the next survivor path candidates after searching over the entire constellation points at each detection layer, the proposed algorithm adaptively restricts the search area to the minimal neighboring constellation points of the estimated QRD symbol according to the instantaneous channel condition at each layer. First, we set up an adaptation rule for search area using two observations that inherently reflect the instantaneous channel condition, that is, the diagonal terms of the channel upper triangle matrix after QR decomposition and Euclidean distance between the received symbol vector and temporarily estimated symbol vector by QRD detection. In addition, it is found that the performance of the QRD-M algorithm degrades when the diagonal terms of the channel upper triangle matrix instantaneously decrease. To overcome this problem, the proposed algorithm employs the ratio of each diagonal term and total diagonal terms. Moreover, the proposed algorithm further decreases redundant complexity by considering the location of initial detection symbol in constellation. By doing so, the proposed algorithm effectively achieves performance near to the maximum likelihood detection algorithm, while maintaining the overall average computation complexity much lower than that of the conventional QRD-M systems. Especially, the proposed algorithm achieves reduction of 76% and 26% computational complexity with low signal to noise ratio (SNR) and high SNR, compared with the adaptive QRD-M algorithm based on noise power. Moreover, simulation results show that the proposed algorithm achieves both low complexity and lower symbol error rate compared with the fixed QRD-M algorithms. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.1

Characterization of an Artificial Swine-Origin Influenza Virus with the Same Gene Combination as H1N1/2009 Virus: A Genesis Clue of Pandemic Strain

Pandemic H1N1/2009 influenza virus, derived from a reassortment of avian, human, and swine influenza viruses, possesses a unique gene segment combination that had not been detected previously in animal and human populations. Whether such a gene combination could result in the pathogenicity and transmission as H1N1/2009 virus remains unclear. In the present study, we used reverse genetics to construct a reassortant virus (rH1N1) with the same gene combination as H1N1/2009 virus (NA and M genes from a Eurasian avian-like H1N1 swine virus and another six genes from a North American triple-reassortant H1N2 swine virus). Characterization of rH1N1 in mice showed that this virus had higher replicability and pathogenicity than those of the seasonal human H1N1 and Eurasian avian-like swine H1N1 viruses, but was similar to the H1N1/2009 and triple-reassortant H1N2 viruses. Experiments performed on guinea pigs showed that rH1N1 was not transmissible, whereas pandemic H1N1/2009 displayed efficient transmissibility. To further determine which gene segment played a key role in transmissibility, we constructed a series of reassortants derived from rH1N1 and H1N1/2009 viruses. Direct contact transmission studies demonstrated that the HA and NS genes contributed to the transmission of H1N1/2009 virus. Second, the HA gene of H1N1/2009 virus, when combined with the H1N1/2009 NA gene, conferred efficient contact transmission among guinea pigs. The present results reveal that not only gene segment reassortment but also amino acid mutation were needed for the generation of the pandemic influenza virus

Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI)

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS

Analysis of Distributed Transmit Diversity with Outdated Diversity Weights

Distributed transmit diversity (DTD) technique that combines cooperative communications and diversity techniques is a suitable solution in 5th-generation (5G) systems. In this paper, we investigate the effect of receiver phase compensation (RPC) on the performance of DTD. We introduce new expressions for the average error rate of DTD in the presence of RPC. The derived expressions are useful for a large number of modulation schemes. We obtain further insights by comparing the RPC effect on DTD with different spatially correlated collocated transmit diversity (CTD). The new observations include the following: (1) In the case of feedback delay, the phase compensation (PC) is required on the receiver side in addition to the transmitter side. (2) In DTD with RPC, the system performance is improved by increasing the differences between the channel gain variances. However, this is the opposite of the case of DTD without RPC. (3) The correlated CTD is more sensitive to RPC than DTD. This sensitivity increases by enhancing the correlation between transmit (TX) antennas. (4) In the case where there is no delay for CTD or DTD, RPC does not affect the system’s performance

Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands

LP-FBMC (low peak-to-average power ratio filter bank multicarrier) was recently proposed to ameliorate the high peak-to-average power ratio (PAPR) issue of filter bank multicarrier (FBMC). The previous simulation study showed that LP-FBMC achieves a similar PAPR as that of single carrier frequency division multiple access (SC-FDMA) while being very robust to inter-user timing/frequency offsets. However, the simulation results that were obtained assuming the stereotyped channel model and the simple nonlinearity model of analog circuits substantially differ from the performance results in a real channel with a real transceiver. To address this, the main purpose of this work is to compare the performances of three waveforms, i.e., SC-FDMA, FBMC, and LP-FBMC, in a real uplink indoor channel. We investigate how the bit error rate (BER) performance gaps of three waveforms in the indoor channels change by the system parameters, such as the carrier frequency within sub-3.5 GHz band and the number of sub-carriers or the sub-carrier spacing, which was not found in the previous simulation study. Our investigation confirms that LP-FBMC is a suitable waveform for real indoor applications

Maximum Likelihood Training of Implicit Nonlinear Diffusion Models

Whereas diverse variations of diffusion models exist, expanding the linear diffusion into a nonlinear diffusion process is investigated only by a few works. The nonlinearity effect has been hardly understood, but intuitively, there would be more promising diffusion patterns to optimally train the generative distribution towards the data distribution. This paper introduces such a data-adaptive and nonlinear diffusion process for score-based diffusion models. The proposed Implicit Nonlinear Diffusion Model (INDM) learns the nonlinear diffusion process by combining a normalizing flow and a diffusion process. Specifically, INDM implicitly constructs a nonlinear diffusion on the \textit{data space} by leveraging a linear diffusion on the \textit{latent space} through a flow network. This flow network is the key to forming a nonlinear diffusion as the nonlinearity fully depends on the flow network. This flexible nonlinearity is what improves the learning curve of INDM to nearly MLE training, compared against the non-MLE training of DDPM++, which turns out to be a special case of INDM with the identity flow. Also, training the nonlinear diffusion empirically yields a sampling-friendly latent diffusion that the sample trajectory of INDM is closer to an optimal transport than the trajectories of previous research. In experiments, INDM achieves the state-of-the-art FID on CelebA

Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.

ObjectivesThis study aimed to compare the diagnostic performance of deep learning algorithm trained by single view (anterior-posterior (AP) or lateral view) with that trained by multiple views (both views together) in diagnosis of mastoiditis on mastoid series and compare the diagnostic performance between the algorithm and radiologists.MethodsTotal 9,988 mastoid series (AP and lateral views) were classified as normal or abnormal (mastoiditis) based on radiographic findings. Among them 792 image sets with temporal bone CT were classified as the gold standard test set and remaining sets were randomly divided into training (n = 8,276) and validation (n = 920) sets by 9:1 for developing a deep learning algorithm. Temporal (n = 294) and geographic (n = 308) external test sets were also collected. Diagnostic performance of deep learning algorithm trained by single view was compared with that trained by multiple views. Diagnostic performance of the algorithm and two radiologists was assessed. Inter-observer agreement between the algorithm and radiologists and between two radiologists was calculated.ResultsArea under the receiver operating characteristic curves of algorithm using multiple views (0.971, 0.978, and 0.965 for gold standard, temporal, and geographic external test sets, respectively) showed higher values than those using single view (0.964/0.953, 0.952/0.961, and 0.961/0.942 for AP view/lateral view of gold standard, temporal external, and geographic external test sets, respectively) in all test sets. The algorithm showed statistically significant higher specificity compared with radiologists (p = 0.018 and 0.012). There was substantial agreement between the algorithm and two radiologists and between two radiologists (κ = 0.79, 0.8, and 0.76).ConclusionThe deep learning algorithm trained by multiple views showed better performance than that trained by single view. The diagnostic performance of the algorithm for detecting mastoiditis on mastoid series was similar to or higher than that of radiologists

A Therapeutically Active Minibody Exhibits an Antiviral Activity in Oseltamivir-Resistant Influenza-Infected Mice via Direct Hydrolysis of Viral RNAs

Emerging Oseltamivir-resistant influenza strains pose a critical public health threat due to antigenic shifts and drifts. We report an innovative strategy for controlling influenza A infections by use of a novel minibody of the 3D8 single chain variable fragment (scFv) showing intrinsic viral RNA hydrolyzing activity, cell penetration activity, and epidermal cell penetration ability. In this study, we examined 3D8 scFv’s antiviral activity in vitro on three different H1N1 influenza strains, one Oseltamivir-resistant (A/Korea/2785/2009pdm) strain, and two Oseltamivir-sensitive (A/PuertoRico/8/1934 and A/X-31) strains. Interestingly, the 3D8 scFv directly digested viral RNAs in the ribonucleoprotein complex. scFv’s reduction of influenza viral RNA including viral genomic RNA, complementary RNA, and messenger RNA during influenza A infection cycles indicated that this minibody targets all types of viral RNAs during the early, intermediate, and late stages of the virus’s life cycle. Moreover, we further addressed the antiviral effects of 3D8 scFv to investigate in vivo clinical outcomes of influenza-infected mice. Using both prophylactic and therapeutic treatments of intranasal administered 3D8 scFv, we found that Oseltamivir-resistant H1N1-infected mice showed 90% (prophylactic effects) and 40% (therapeutic effects) increased survival rates, respectively, compared to the control group. The pathological signs of influenza A in the lung tissues, and quantitative analyses of the virus proliferations supported the antiviral activity of the 3D8 single chain variable fragment. Taken together, these results demonstrate that 3D8 scFv has antiviral therapeutic potentials against a wide range of influenza A viruses via the direct viral RNA hydrolyzing activity

Haploidentical transplantation has a superior graft-versus-leukemia effect than HLA-matched sibling transplantation for Ph– high-risk B-cell acute lymphoblastic leukemia

Abstract. Background:. Compared with human leukocyte antigen (HLA)-matched sibling donor (MSD) transplantation, it remains unclear whether haploidentical donor (HID) transplantation has a superior graft-versus-leukemia (GVL) effect for Philadelphia-negative (Ph–) high-risk B-cell acute lymphoblastic leukemia (B-ALL). This study aimed to compare the GVL effect between HID and MSD transplantation for Ph– high-risk B-ALL. Methods:. This study population came from two prospective multicenter trials (NCT01883180, NCT02673008). Immunosuppressant withdrawal and prophylactic or pre-emptive donor lymphocyte infusion (DLI) were administered in patients without active graft-versus-host disease (GVHD) to prevent relapse. All patients with measurable residual disease (MRD) positivity posttransplantation (post-MRD+) or non-remission (NR) pre-transplantation received prophylactic/pre-emptive interventions. The primary endpoint was the incidence of post-MRD+. Results:. A total of 335 patients with Ph– high-risk B-ALL were enrolled, including 145 and 190, respectively, in the HID and MSD groups. The 3-year cumulative incidence of post-MRD+ was 27.2% (95% confidence interval [CI]: 20.2%–34.7%) and 42.6% (35.5%–49.6%) in the HID and MSD groups (P = 0.003), respectively. A total of 156 patients received DLI, including 60 (41.4%) and 96 (50.5%), respectively, in the HID and MSD groups (P = 0.096). The 3-year cumulative incidence of relapse was 18.6% (95% CI: 12.7%–25.4%) and 25.9% (19.9%–32.3%; P = 0.116) in the two groups, respectively. The 3-year overall survival (OS) was 67.4% (95% CI: 59.1%–74.4%) and 61.6% (54.2%–68.1%; P = 0.382), leukemia-free survival (LFS) was 63.4% (95% CI: 55.0%–70.7%) and 58.2% (50.8%–64.9%; P = 0.429), and GVHD-free/relapse-free survival (GRFS) was 51.7% (95% CI: 43.3%–59.5%) and 37.8% (30.9%–44.6%; P = 0.041), respectively, in the HID and MSD groups. Conclusion:. HID transplantation has a lower incidence of post-MRD+ than MSD transplantation, suggesting that HID transplantation might have a superior GVL effect than MSD transplantation for Ph– high-risk B-ALL patients. Trial registration:. ClinicalTrials.gov: NCT01883180, NCT02673008