Novel industrial wireless applications require wideband, real-time channel characterization due to complex multipath propagation. Rapid machine motion leads to fast time variance of the channel's ...reflective behavior, which must be captured for radio channel characterization. Additionally, inhomogeneous radio channels demand highly flexible measurements. Existing approaches for radio channel measurements either lack flexibility or wide-band, real-time performance with fast time variance. In this paper, we propose a correlative channel sounding approach utilizing a software-defined architecture. The approach enables real-time, wide-band measurements with fast time variance immune to active interference. The desired performance is validated with a demanding industrial application example.
The steadily growing use of license-free frequency bands require reliable coexistence management and therefore proper wireless interference identification (WII). In this work, we propose a WII ...approach based upon a deep convolutional neural network (CNN) which classifies multiple IEEE 802.15.1, IEEE 802.11 b/g and IEEE 802.15.4 interfering signals in the presence of a utilized signal. The generated multi-label dataset contains frequency- and time-limited sensing snapshots with the bandwidth of 10 MHz and duration of 12.8 \(\mu\)s, respectively. Each snapshot combines one utilized signal with up to multiple interfering signals. The approach shows promising results for same-technology interference with a classification accuracy of approximately 100 % for IEEE 802.15.1 and IEEE 802.15.4 signals. For IEEE 802.11 b/g signals the accuracy increases for cross-technology interference with at least 90 %.
The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference ...identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the ...state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resources to wireless systems. To ensure a conflict-free resource utilization, it is useful to predict the prospective medium utilization before resources are allocated. This paper presents a self-learning concept, which is based on reinforcement learning. A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios. The evaluation of the double deep Q-network showed that a prediction accuracy of at least 98 % can be reached in all investigated scenarios.
Real-time industrial wireless systems sharing a crowded spectrum band require active coexistence management measures. Identification of wireless interference is a key issue for this purpose. We ...propose an efficient implementation of a wireless interference identification (WII) approach called neuro-fuzzy signal classifier (NFSC). The implementation in Matlab / SIMULINK is based upon the wideband software defined radio Ettus USRP N210. The implementation is evaluated in six selected heterogeneous and harsh industrial scenarios within the license-free 2.4-GHz-ISM radio band with variously combined standard wireless technologies IEEE 802.11g-based WLAN and Bluetooth. The evaluation of the NFSC was performed with a binary classification test with the statistical measurement metrics sensitivity and specificity.
Automation in industrial production is getting more important, so the need for high data rate, low latency and low power wireless communication is growing. This requires efficient use of bandwidth, ...in particular for unlicensed bands. In this paper, we present a hardware architecture for analysing the channel utilisation, which is needed in real-time coexistence management systems. Additionally, we compare the solution to a software implementation with respect to performance and real-time capability. The hardware implementation supports real-time channel utilisation sensing, calculating Fast-Fourier-Transform and logarithm. As shown in the results chapter, the hardware solution exceeds software approaches by more than five orders of magnitude in terms of throughput, calculating the Fast-Fourier-Transform and logarithm of a 1024 value wide vector in 17.26 μs at a clock frequency of 60 MHz. The FFT size can be dynamically reconfigured using control signals driven by software, which enables reuse e.g. in multi carrier systems such as GFDM, when coexistence management is not active.
Critical industrial applications in embedded field devices require reliability and consistency. Cloudbased
services have been gaining attraction in embedded field devices for monitoring, ...optimization,
predictive maintenance, and other supporting use cases. A significant challenge persists in enabling
cloud-connection to the embedded field devices. The central issues on this matter are diversity, resource
constraint, and the critical applications of these devices. This paper proposes a novel concept
for enabling cloud connection to these devices. A dedicated software module, μConnector, has been
introduced for cloud-related activities. It operates on Zephyr RTOS. The purpose of μConnector is to
create a separation between critical and cloud related applications within the embedded field devices.
μConnector is designed to be application-agnostic while being independent of vendor selection for
hardware components. The scientific contribution of the paper lies in the introduction of μConnector.
The presented concept addresses the challenges associated with cloud connectivity for embedded field
devices. Its primary objective is to define architectural decisions guiding the implementation of the
proposed software module.
Islet transplantation is a feasible therapeutic alternative for metabolically labile patients with type 1 diabetes. The primary therapeutic target is stable glycemic control and prevention of ...complications associated with diabetes by reconstitution of endogenous insulin secretion. However, critical shortage of donor organs, gradual loss in graft function over time, and chronic need for immunosuppression limit the indication for islet transplantation to a small group of patients. Here we present a promising approach to address these limitations by utilization of a macrochamber specially engineered for islet transplantation. The s.c. implantable device allows for controlled and adequate oxygen supply and provides immunological protection of donor islets against the host immune system. The minimally invasive implantable chamber normalized blood glucose in streptozotocin-induced diabetic rodents for up to 3 mo. Sufficient graft function depended on oxygen supply. Pretreatment with the growth hormone-releasing hormone (GHRH) agonist, JI-36, significantly enhanced graft function by improving glucose tolerance and increasing β-cell insulin reserve in rats thereby allowing for a reduction of the islet mass required for metabolic control. As a result of hypervascularization of the tissue surrounding the device, no relevant delay in insulin response to glucose changes has been observed. Consequently, this system opens up a fundamental strategy for therapy of diabetes and may provide a promising avenue for future approaches to xenotransplantation.
Background. Postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in ...children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory. Objective. To explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis (DKA) or severe hypoglycemia (SH)) or hemoglobin (Hb) A1c trajectory. Methods: We included children <21 years with T1D and ≥1 HbA1c prior to cohort entry, which was defined as COVID-19 (positive diagnostic test or diagnosis code for COVID-19, multisystem inflammatory syndrome in children, or PASC) or a randomly selected negative test for those who were negative throughout the study period (Broad Cohort). A subset with ≥1 HbA1c value from 28 to 275 days after cohort entry (Narrow Cohort) was included in the trajectory analysis. Propensity score-based matched cohort design followed by weighted Cox regression was used to evaluate the association of COVID-19 with healthcare utilization ≥28 days after cohort entry. Generalized estimating equation (GEE) models were used to measure change in HbA1c in the Narrow Cohort. Results. From March 01, 2020 to June 22, 2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow Cohorts, respectively. The hazard ratio for utilization was (HR 1.45 (95% CI: 0.97, 2.16)). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91–180 days after cohort entry for those with COVID-19 (0.138 vs. −0.002, p=0.172). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (−0.104 vs. 0.008 per month, p=0.024). Conclusion. While a trend toward worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.
Cardiac complications, particularly myocarditis and pericarditis, have been associated with SARS-CoV-2 (the virus that causes COVID-19) infection (1-3) and mRNA COVID-19 vaccination (2-5). ...Multisystem inflammatory syndrome (MIS) is a rare but serious complication of SARS-CoV-2 infection with frequent cardiac involvement (6). Using electronic health record (EHR) data from 40 U.S. health care systems during January 1, 2021-January 31, 2022, investigators calculated incidences of cardiac outcomes (myocarditis; myocarditis or pericarditis; and myocarditis, pericarditis, or MIS) among persons aged ≥5 years who had SARS-CoV-2 infection, stratified by sex (male or female) and age group (5-11, 12-17, 18-29, and ≥30 years). Incidences of myocarditis and myocarditis or pericarditis were calculated after first, second, unspecified, or any (first, second, or unspecified) dose of mRNA COVID-19 (BNT162b2 Pfizer-BioNTech or mRNA-1273 Moderna) vaccines, stratified by sex and age group. Risk ratios (RR) were calculated to compare risk for cardiac outcomes after SARS-CoV-2 infection to that after mRNA COVID-19 vaccination. The incidence of cardiac outcomes after mRNA COVID-19 vaccination was highest for males aged 12-17 years after the second vaccine dose; however, within this demographic group, the risk for cardiac outcomes was 1.8-5.6 times as high after SARS-CoV-2 infection than after the second vaccine dose. The risk for cardiac outcomes was likewise significantly higher after SARS-CoV-2 infection than after first, second, or unspecified dose of mRNA COVID-19 vaccination for all other groups by sex and age (RR 2.2-115.2). These findings support continued use of mRNA COVID-19 vaccines among all eligible persons aged ≥5 years.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
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