Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

A Novel Computerized Cognitive Test for the Detection of Mild Cognitive Impairment and Its Association with Neurodegeneration in Alzheimer’s Disease Prone Brain Regions

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.

Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

A more accurate determination for the Probability of Failure on Demand(PFD)of the Safety Instrumented System(SIS)contributes to more SIS realiability,thereby ensuring more safety and lower cost.IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas.However,these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources,which,including high redundant systems architectures,cannot be assessed,have perfect proof test assumption,and are neglegted in partial stroke testing(PST)of impact on the system PFD.On the other hand,determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time.This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem.A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor(DC)and common cause failures(CCF).In order to simulate the proof test effectiveness,the Proof Test Coverage(PTC)factor has been incorporated into the formula.Additionally,the system PFD value has been improved by incorporating PST for the final control element into the formula.The new developed formula is modelled using the Genetic Algorithm(GA)artificial technique.The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables.The proposed model has been applicated on SIS design for crude oil test separator using MATLAB.The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality.Furthermore,the cost and associated implementation testing activities are reduced.

Adaptive Backoff Algorithm for IEEE 802.11 MAC Protocol

A Mobile Ad Hoc Network (MANET) is a collection of mobile nodes that can communicate directly over wireless media, without the need for a preconfigured infrastructure. Several approaches have been suggested to improve Quality of Service (QoS) in IEEE 802.11-based MANETs through modifying some of the IEEE 802.11 Medium Access Control (MAC) algorithms, such as the backoff algorithm that is used to control the packets collision aftermath. In this work, an adaptive IEEE 802.11 backoff algorithm to improve QoS is de-veloped and tested in simulations as well as in testbed implementation. While the Binary Exponential Backoff (BEB) algorithm deployed by IEEE 802.11 reacts based on individual packet transmit trials, the new algo-rithm takes the history of successive packet transmit trials into account to provide a better QoS performance. The new algorithm has been tested against the legacy IEEE 802.11 through simulations using QualNet and a Linux-based testbed comprising a number of stations. The performed tests have shown significant im-provements in performance, with up to 33.51% improvement in delay and 7.36% improvement in packet delivery fraction compared to the original IEEE 802.11.

Numerical Analysis of the Behavior of A New Aeronautical Alloy (Ti555-03) Under the Effect of A High-Speed Water Jet

In this paper, we present a numerical simulation of a water jet impacting a new aeronautical material Ti555-03 plate.The Computational Fluid Dynamics(CFD) behavior of the jet is investigated using a FV(Finite Volume) method.The Fluid–Structure Interaction(FSI) is studied using a coupled SPH(Smoothed Particle Hydrodynamics)-FE(Finite Element) method. The jets hit the metal sheet with an initial velocity 500 m/s. Two configurations which differ from each other by the position(angle of inclination) of the plate relatively to the axis of revolution of the jet inlet are investigated in this study. The objective of this study is to predict the impact of the fluid produced at high pressure and high speed especially at the first moment of impact. Numerical simulations are carried out under ABAQUS. We have shown in this study that the inclination of the titanium alloy plate by 45° stimulates the phenomenon of recirculation of water. This affects the velocity profile, turbulence and boundary layers in the impact zone. The stagnation zone and the phenomenon of water recirculation are strongly influenced by the slope of the plate which gives a pressure gradient and displacement very important between the two configurations. Fluctuations of physical variables(displacement and pressure) prove the need for a noise and vibratory study. These predictions will subsequently be used for the modeling of the problem of an orthogonal cut in a high-speed machining process assisted by high-pressure water jet.

A FIXED POINT APPROACH TO THE STABILITY OF A GENERAL MIXED ADDITIVE-CUBIC EQUATION ON BANACH MODULES

Using a fixed-point method, we establish the generalized Hyers-Ulam stability of a general mixed additive-cubic equation： f（kx ＋ y） ＋ f（kx - y） = kf（x ＋ y） ＋ kf（x - y） ＋ 2f（kx） - 2kf（x） in Banach modules over a unital Banach algebra.

Improving Smart Home Security via MQTT: Maximizing Data Privacy and Device Authentication Using Elliptic Curve Cryptography

The rapid adoption of Internet of Things(IoT)technologies has introduced significant security challenges across the physical,network,and application layers,particularly with the widespread use of the Message Queue Telemetry Transport(MQTT)protocol,which,while efficient in bandwidth consumption,lacks inherent security features,making it vulnerable to various cyber threats.This research addresses these challenges by presenting a secure,lightweight communication proxy that enhances the scalability and security of MQTT-based Internet of Things(IoT)networks.The proposed solution builds upon the Dang-Scheme,a mutual authentication protocol designed explicitly for resource-constrained environments and enhances it using Elliptic Curve Cryptography(ECC).This integration significantly improves device authentication,data confidentiality,and energy efficiency,achieving an 87.68%increase in data confidentiality and up to 77.04%energy savings during publish/subscribe communications in smart homes.The Middleware Broker System dynamically manages transaction keys and session IDs,offering robust defences against common cyber threats like impersonation and brute-force attacks.Penetration testing with tools such as Hydra and Nmap further validated the system’s security,demonstrating its potential to significantly improve the security and efficiency of IoT networks while underscoring the need for ongoing research to combat emerging threats.

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

True Random Bit Generator Using ZCDPLL Based on TMS320C6416

A True Random Binary Generator (TRBG) based on a zero crossing digital phase-locked loop (ZCDPLL) is proposed. In order to face the challenges of using the proposed TRBG in cryptography, the proposed TRBG is subjected to the AIS 31 test suite. The ZCDPLL operate as chaotic generator for certain loop filter gains and this has been used to generate TRBs. The generated binary sequences have a good autocorrelation and cross-correlation properties as seen from the simulation results. A prototype of TRBG using ZCDPLL has been developed through Texas Instruments TMS320C6416 DSP development kit. The proposed TRBG successfully passed the AIS 31 test suit.

Adding Pseudo-Random Test Sequence Generator in the Test Simulator for DFT Approach

This paper presents modified version of a realistic test tool suitable to Design For Testability （DFT） and Built-ln Self Test （BIST） environments. A comprehensive tool is developed in the form of a test simulator. The simulator is capable of providing a required goal of test for the Circuit Under Test （CUT）. The simulator uses the approach of fault diagnostics with fault grading procedures to provide the optimum tests. The current version of the simulator embeds features of exhaustive and pseudo-random test generation schemes along with the search solutions of cost effective test goals. The simulator provides facilities of realizing all possible pseudo-random sequence generators with all possible combinations of seeds. The tool is developed on a common Personal Computer （PC） platform and hence no special software is required. Thereby, it is a low cost tool hence economical. The tool is very much suitable for determining realistic test sequences for a targeted goal of testing for any CUT. The developed tool incorporates flexible Graphical User Interface （GUI） procedures and can be operated without any special programming skill. The tool is debugged and tested with the results of many bench mark circuits. Further, this developed tool can be utilized for educational purposes for many courses such as fault-tolerant computing, fault diagnosis, digital electronics, and safe-reliable-testable digital logic designs.

Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

Correlation of subway turnstile entries and COVID-19 incidence and deaths in New York City

In this paper,we show a strong correlation between turnstile entries data of the New York City(NYC)subway provided by NYC Metropolitan Transport Authority and COVID-19 deaths and cases reported by the NYC Department of Health from March to May 2020.This correlation is obtained through linear regression and confirmed by the prediction of the number of deaths by a Long Short-Term Memory neural network.The correlation is more significant after considering incubation and symptomatic phases of this disease as experienced by people who died from it.We extend the analysis to each individual NYC borough.We also estimate the dates when the number of COVID-19 deaths and cases would approach zero by using the Auto-Regressive Integrated Moving Average model on the reported deaths and cases.We also backward forecast the dates when the first cases and deaths might have occurred.

Observer-based Adaptive Fuzzy Control for a Class of Nonlinear Time-delay Systems

An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

Serum microRNA-221 as a biomarker for diabetic retinopathy in patients associated with type 2 diabetes

AIM: To investigate the candidate microRNA(miRNA), miR-221 as a novel biomarker for diabetic retinopathy(DR) in patients associated with type 2 diabetes(T2D).METHODS: The subjects involved were divided into four groups: healthy control(HC), no diabetic retinopathy(NDR), non-proliferative diabetic retinopathy(NPDR) and proliferative diabetic retinopathy(PDR) group. Serum miR-221 was validated by real-time quantitative reversetranscription polymerase chain reaction(qRT-PCR). Also, serum angiotensin II(Ang II) and vascular endothelial growth factor(VEGF) were examined by enzyme-linked immunosorbent assay. In addition, receiver operating characteristic(ROC) curve was performed to explore the diagnostic accuracy of miR-221, Ang Ⅱ and VEGF for DR in patients with T2D. Spearman’s rank correlation coefficient was executed to estimate the correlations of serum miR-221 with metabolic parameters and serum markers in patients with T2D.RESULTS: Primarily, serum miR-221, Ang Ⅱ and VEGF were increased significantly in T2D patients compared to HC participant respectively, and progressive up-regulated in NDR, NPDR and PDR groups(P<0.001). Additionally, miR-221 in serum was remarkably positively correlatedwith metabolic parameters such as glycated hemoglobin(r=0.310, P=0.002) and homeostasis model assessment for insulin resistance(r=0.413, P<0.001), as well as serum markers for instance Ang Ⅱ(r=0.667, P<0.001) and VEGF(r=0.499, P<0.001). Furthermore, serum miR-221(AUC, 0.894; 95%CI, 0.833-0.955; P<0.001), Ang Ⅱ(AUC, 0.888; 95%CI, 0.828-0.949; P<0.001) and VEGF(AUC, 0.785; 95%CI, 0.695-0.875; P<0.001) had evidently diagnostic efficiency in DR, and miR-221 is the most effective among them.CONCLUSION: Serum miR-221 as a potential biomarker could be related to not only occurrence but also progression for DR in patients with T2D. However, a prospective clinical trial is warranted.

Generalized Hyers-Ulam Stability of a General Mixed Additive-cubic Functional Equation in Quasi-Banach Spaces

In this paper, we establish a general solution and the generalized Hyers-Ulam-Rassias stability of the following general mixed additive-cubic functional equation

Social Energy:Mining Energy From the Society

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social systems.From this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions.The recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and management.In this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy concept.In the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this work.The addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine discomfort.This novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on it.Additionally,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted trees.The proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in Simulink.The results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing works.The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

Power Domain Multiplexing Waveform for 5G Wireless Networks

Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented challenges.NOMA employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency misalignments.Unfortunately,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless networks.Therefore,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high PAPR.Additionally,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)gain.In the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned resources.The simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional framework.Hence,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC waveform.The key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting side.Thus,a huge count of mobile devices could be supported under specific restrictions.DHTNOMA-UFMC can be regarded as the most effective applications for 5G Mobile andWireless Networks.However,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.

MEMS-actuated terahertz metamaterials driven by phase-transition materials

The non-ionizing and penetrative characteristics of terahertz(THz)radiation have recently led to its adoption across a variety of applications.To effectively utilize THz radiation,modulators with precise control are imperative.While most recent THz modulators manipulate the amplitude,frequency,or phase of incident THz radiation,considerably less progress has been made toward THz polarization modulation.Conventional methods for polarization control suffer from high driving voltages,restricted modulation depth,and narrow band capabilities,which hinder device performance and broader applications.Consequently,an ideal THz modulator that offers high modulation depth along with ease of processing and operation is required.In this paper,we propose and realize a THz metamaterial comprised of microelectromechanical systems(MEMS)actuated by the phase-transition material vanadium dioxide(VO_(2)).Simulation and experimental results of the three-dimensional metamaterials show that by leveraging the unique phase-transition attributes of VO_(2),our THz polarization modulator offers notable advancements over existing designs,including broad operation spectrum,high modulation depth,ease of fabrication,ease of operation condition,and continuous modulation capabilities.These enhanced features make the system a viable candidate for a range of THz applications,including telecommunications,imaging,and radar systems.

Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter

This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy mea- surements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter （UKF）. Two data fusion architectures： measurement fusion （MF） and state vector fusion （SVF） are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.