Channel Estimation for Massive MIMO Systems

In MIMO millimeter-wave (mmWave) systems, while the hybrid digital/analog precoding structure provides the ability to increase the reach rate, it also faces the challenge of reducing the channel time limit due to the large number of horns on both sides of the Tx / Rx. . In this paper, channel measurement is done by searching with multiple beams, and a new hierarchical multi-beam search system is proposed, using a pre-designed analog codebook. Performance tests show that, compared to a highperformance system, the proposed system not only achieves a high level of success in getting multiple beams under normal system settings but also significantly reduces channel estimation time Keywords: Massive MIMO, Channel Estimation, precoding

Development and functional significance of the pulmonary surfactant system

Relevance. Prevention of the development of postoperative acute respiratory distress syndrome during operations on the descending thoracic aorta increases the effectiveness of therapy. The study of damage to the surfactant complex during ischemia and reperfusion of the lungs is relevant, since it involves the prophylactic use of the surfactant preparation during operations on the descending part of the thoracic aorta, which are characterized by a high risk of postoperative acute respiratory distress syndrome. Objective: to increase the effectiveness of pharmacological and respiratory therapy of acute respiratory distress syndrome, as well as to identify the role of the surfactant system of the lungs in the onset of inflammation against the background of tuberculosis and the development of regeneration mechanisms that affect the course and outcome of the disease. Materials and Methods. The study involved 24 people, including 14 volunteer patients with a diagnosed respiratory disease in an acute course (while the whole group received the drug from the study as an additional therapy). The sample of 14 people was formed solely due to the compliance of these patients with the criteria that were established before the start of the study of the drug, which had postoperative acute respiratory distress syndrome of various origins in their diagnosis. Results and Discussion. For a comprehensive laboratory determination, an algorithm was used that corresponded to the state standard to identify postoperative acute respiratory distress syndrome. For each participant in the experiments, it was proposed to develop a plan of treatment procedures, taking into account individualization and standardization. Conclusion. Some of the resulting data are collected with respect to the surfactant pulmonary system, which is presented in a compactor model format. A number of basic components are reflected here, which are classified according to cellular and non-cellular factors. At the same time, the surfactant substance helps to reduce the pronounced swelling, which can significantly reduce the process of sticking of the alveolar structures during inhalation. All this added up to the normal system of gas metabolism in the lung structures, including the control of the mucociliary system, which acts as a natural stimulator of the function of alveolar macrophages.

The strong normal system of some compact right topological groups

The aim of the present paper is to characterize the strong normal system of the Ellis groups of a well-known family of dynamical systems on the finite and infinite dimensional tori.

Experimental Study on Thermal Performance of Pre-formed Thin Heating Floor Filled with Shaped PCM

A shape-stabilized phase change material (PCM) with high-density polyethylene was prepared as the supporting material to be added to a pre-fabricated light and dry-type heating floor. A system for underfloor heating experiments was set up in the laboratory to test the effects of average supply and return water temperatures and different temperature differences on the thermal performance of two floor heating systems under 10 different operating conditions, respectively. The results show that the surface heat-flux density of a phase change floor (PCF) is higher than that of an ordinary one at the stable stage. The proportion of heat transfer to the heating room is about 13% higher in the phase change system compared to the normal system, and the heat loss is reduced by more than 10%. At the cooling stage, the surface temperature of PCF decreases slowly, compared to the rapid decrease of the ordinary one.

Nonlinear Dynamic Characteristics of Wind Turbine Gear System Caused by Tooth Crack Fault

Crack in gears impacts the dynamic response of wind turbine multistage gear system, which also influences the safe operation of wind turbine. A translational–torsional nonlinear dynamic model of the multistage gear system is proposed with root crack fault. The model considers the effects of sun gear support, time-varying mesh stiffness, gear backlash and other factors. The mesh stiffness with root crack is analyzed by using potential energy method. Based on the Runge–Kutta method, the system responses are obtained with multiple parameters changing. The nonlinear dynamic features of the cracked and normal system are compared by bifurcation diagram, time series, phase trajectory, Poincaré map, spectrum diagram and corresponding three-dimensional diagrams. The analyses show the effects of input torque, backlash, crack occurrence and evolution on the system dynamic behaviors, and the effect of crack fault on the gear system response is further verified by experiment. The results provide a theoretical basis for the cognition of fault mechanism and fault diagnosis of wind turbine gearbox.

Extended B-polynomial neural network for time-delayed system modeling using sampled data

The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept.

A resilient control strategy for networked multi-agent systems subject to covert attacks

In this paper, a resilient distributed control scheme against covert attacks for multi-agent networked systems subject to input and state constraints is developed. The idea consists in a clever deployment of predictive arguments with a twofold aim: detection of malicious agent behaviors affecting the normal system operations and consequent specific control actions implementation to mitigate as much as possible undesirable knock-on effects resulting from adversary actions. Specifically, the multi-agent system is organized in terms of a grid topology and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to recognize the attacked agent. In essence, the resulting solution relies on the combined use of predictive control and set-invariance ideas that are exploited to generate redundant control sequences randomly selected on the actuator side such that the malicious agent is never aware about the effective control action indeed exploited. As a consequence, countermeasures on the sensor-to-controller channel could lead to significantly erroneous data not complying with the expected evolution of the system modeling. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.

Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal

The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas. It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro) i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers are computed as discrete values depending on time. The used research method is finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.

Predicting Performance Anomalies in Software Systems at Run-time

High performance is a critical factor to achieve and maintain the success of a software system. Performance anomalies represent the performance degradation issues (e.g., slowing down in system response times) of software systems at run-time. Performance anomalies can cause a dramatically negative impact on users’ satisfaction. Prior studies propose different approaches to detect anomalies by analyzing execution logs and resource utilization metrics after the anomalies have happened. However, the prior detection approaches cannot predict the anomalies ahead of time; such limitation causes an inevitable delay in taking corrective actions to prevent performance anomalies from happening. We propose an approach that can predict performance anomalies in software systems and raise anomaly warnings in advance. Our approach uses a Long-Short Term Memory neural network to capture the normal behaviors of a software system. Then, our approach predicts performance anomalies by identifying the early deviations from the captured normal system behaviors. We conduct extensive experiments to evaluate our approach using two real-world software systems (i.e., Elasticsearch and Hadoop). We compare the performance of our approach with two baselines. The first baseline is one state-to-the-art baseline called Unsupervised Behavior Learning. The second baseline predicts performance anomalies by checking if the resource utilization exceeds pre-defined thresholds. Our results show that our approach can predict various performance anomalies with high precision (i.e., 97–100%) and recall (i.e., 80–100%), while the baselines achieve 25–97% precision and 93–100% recall. For a range of performance anomalies, our approach can achieve sufficient lead times that vary from 20 to 1,403 s (i.e., 23.4 min). We also demonstrate the ability of our approach to predict the performance anomalies that are caused by real-world performance bugs. For predicting performance anomalies that are caused by real-world performance bugs, our approach achieves 95–100% precision and 87–100% recall, while the baselines achieve 49–83% precision and 100% recall. The obtained results show that our approach outperforms the existing anomaly prediction approaches and is able to predict performance anomalies in real-world systems.

A Nonfunctioning Pituitary Macroadenoma Presenting as Cyclical Vomiting and Severe Hyponatremia in a Young Female

Introduction. Recurrent vomiting is a commonly overlooked debilitating symptom which causes significant impact on the quality of life. There are several causes for vomiting, ranging from commonly known causes to rare causes. Nonfunctioning pituitary macroadenomas generally present with visual disturbances, headache, and symptoms due to anterior pituitary hormone deficiencies. This case report is about an atypical presentation of a nonfunctioning pituitary macroadenoma in which the patient presented with cyclical vomiting with severe hyponatremia. Case Report. A 23-year-old girl presented with four to five episodes of vomiting per day for two days duration. She had a history of similar episodes of vomiting since 2016, with each episode generally lasting for 4-5 days and occurring in every four to six months. All episodes exhibited similar symptomatology and she was free of symptoms in-between. Generalized body weakness, postural dizziness, reduced appetite, and secondary amenorrhea were other symptoms she has had since 2016. Examination findings showed a low body mass index (BMI) (16 kg/m2) with normal system examination. Investigations showed severe hyponatremia (110 mmol/L) with hypokalemia (3.2 mmol/L) and hypochloremia (74 mmol/L). Her urinary excretion of potassium, sodium, and serum osmolality was low. Urine osmolality was mildly elevated compared to serum osmolality. Blood urea was normal. Severe hyponatremia with minimal hyponatremic symptoms was suggestive of chronic hyponatremia, which was accentuated by ongoing vomiting and possible reduced intake of salt. Further investigations showed evidence of secondary hypoadrenalism, central hypothyroidism, hypogonadotropic hypogonadism, and mild hyperprolactinemia. Magnetic resonance imaging (MRI) revealed a pituitary macroadenoma with mass effect on the optic chiasma. Hydrocortisone and levothyroxine were started, and she underwent transsphenoidal resection of the pituitary tumor. She recovered from cyclical vomiting. Conclusion. There can be multiple overlapping aetiologies for every observed symptom, sign, and abnormal investigation finding. Therefore, aetiological diagnosis is challenging, especially in the presence of an atypical clinical presentation. Cyclical vomiting and severe hyponatremia are atypical presentations of nonfunctioning pituitary macroadenomas.