Feasibility Study of Community Earthquake Warning System Proposed for Mbeya City and Surrounding Regions

The Mbeya city and its surrounding regions (Rukwa, Katavi and Njombe) in south western Tanzania, fall among the fast growing economical regions in Tanzania. It is also unfortunately under potential for seismic risk due to its proximity to the western and eastern junction of the East African Rift (EAR) Valley. Construction of Community Earthquake Early Warning (CEEW) system model based on community owned MEMS accelerometer sensors in being proposed for the region. To optimize the warning time function and as the process of planning and designing the community hosted seismic network, the paper presents the simulation of warning times that can be realized in this region based on the distribution of sensor stations in relation to the historical strong earthquakes and target sites. The distribution of sensor stations determines the detection and reporting time of the event, while location of earthquakes and position of target site determine the available warning time for the target to be protected. Testing the various sensor station configura- tions (regional, On-site and Hybrid) models of CEEWS by simulation of scenario earthquakes, the hybrid configuration that distributes sensor stations closer to the source and on target sites, was able to provide at least 5 seconds of warning times to various targets. This time has been demon- strated to be enough for shutting down hazardous industrial processes and for people to take cover at safer locations to reduce injuries.

Real Time Monitoring of Extreme Rainfall Events with Simple X-Band Mini Weather Radar

Real time rainfall events monitoring is very important for a large number of reasons: Civil Protection, hydrogeological risk management, hydroelectric power purposes, road and traffic regulation, and tourism. Efficient monitoring operations need continuous, high-resolution and large-coverage data. To monitor and observe extreme rainfall events, often much localized over small basins of interest, and that could frequently causing flash floods, an unrealistic extremely dense rain gauge network should be needed. On the other hand, common large C-band or S-band long range radars do not provide the necessary spatial and temporal resolution. Simple short-range X-band mini weather radar can be a valid compromise solution. The present work shows how a single polarization, non-Doppler and non-coherent, simple and low cost X-band radar allowed monitoring three very intense rainfall events occurred near Turin during July 2014. The events, which caused damages and floods, are detected and monitored in real time with a sample rate of 1 minute and a radial spatial resolution of 60 m, thus allowing to describe the intensity of the precipitation on each small portion of territory. This information could be very useful if used by authorities in charge of Civil Protection in order to avoid inconvenience to people and to monitor dangerous situations.

Ultrasonic focusing and scanning with multiple waves

This paper presents a new focusing and scanning method which focuses multiple waves on a target. The key of the method is to control excitation pulses for each element of the transducer array. The excitation pulse on each array element is obtained by time reversing the signal received by the same element, which is generated by an imaginary source at the target. The excitation pulses from all array elements are transmitted and arrive at the target simultaneously, and focusing is achieved. The performance of the two methods is compared in numerical examples, and it is demonstrated that the proposed method achieves a satisfactory focusing and a good signal-to-noise ratio no matter where the target location is.

Visualized SERS Imaging of Single Molecule by Ag/ Black Phosphorus Nanosheets

Single-molecule detection and imaging are of great value in chemical analysis,biomarker identification and other trace detection fields.However,the localization and visualization of single molecule are still quite a challenge.Here,we report a special-engineered nanostructure of Ag nanoparticles embedded in multi-layer black phosphorus nanosheets(Ag/BP-NS)synthesized by a unique photoreduction method as a surfaceenhanced Raman scattering(SERS)sensor.Such a SERS substrate features the lowest detection limit of 10^(–20) mol L^(−1) for R6G,which is due to the three synergistic resonance enhancement of molecular resonance,photoinduced charge transfer resonance and electromagnetic resonance.We propose a polarization-mapping strategy to realize the detection and visualization of single molecule.In addition,combined with machine learning,Ag/BP-NS substrates are capable of recognition of different tumor exosomes,which is meaningful for monitoring and early warning of the cancer.This work provides a reliable strategy for the detection of single molecule and a potential candidate for the practical bio-application of SERS technology.

Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular networks.This scenario affects the quality of service(QoS)of vehicle and non-vehicle users.Nowadays,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly accessible.Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks.However,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task.This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs.Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision.However,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available.The present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions.The proposed mechanism is decentralized,where each vehicle makes the prediction by itself.This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios.The MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading ratio(i.e.,above 90%)than the existing mechanisms for several tested scenarios.

3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

Increasing Distance Increasing Bits Substitution (IDIBS) Algorithm for Implementation of VTVB Steganography

Cryptography and steganography are two important and related fields of information security.But,steganography is slightly different in the sense that it hides the existence of secret information from unauthorized users.It is one of the most appealing research domains,have applications like copyright protection,data integrity protection and manipulation detection.Several steganography techniques have been proposed in literature.But,in this work a new information hiding algorithm is presented.The proposed technique de-correlates frequency components of cover image using discrete cosine transform and uses the least significant bits of frequency components for hiding secret information.The tech-nique hides variable number of bits of secret message in different frequency components.Therefore,it hides different amount of secret information in different regions of cover im-age and results in enhancement of security.The algorithm has the flexibility to change the hiding capacity and quality of final stego image.It has been observed from experimental results that a hiding a capacity from 3%to 43%can be achieved with significantly good quality of 41 dB to 37 dB in term of peak signal to noise ratio.The successful recovery of the hidden information need the pattern,called stego key,in which is used in hiding process.The algorithm provides twofold security;hiding keeps the existence of hidden information secret and the large key size makes the retrieval of hidden information difficult for intruders.

Sensing, Signal Processing, and Communication for WBANs

A wireless body area network （WBAN） enables real-time monitoring of physiological signals and helps with the early detection of life-threatening diseases. WBAN nodes can be located on, inside, or in close proximity to the body in order to detect vital signals. Measurements from sensors are processed and transmitted over wireless channels. Issues in sensing, signal processing, and com-munication have to be addressed before WBAN can be implemented. In this paper, we survey recent advances in research on sig-nal processing for the sensor measurements, and we describe aspects of communication based on IEEE 802.15.6. We also discuss state-of-the-art WBAN channel modeling in all the frequencies specified by IEEE 802.15.6 as well as the need for new channel models for new different frequencies.

Resource requirements of an edge-based digital twin service:an experimental study

Digital Twin(DT)is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution(Industry 4.0).DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots,thus allowing their remote control.Relying on the latest advances in Information and Communication Technologies(ICT),namely Network Function Virtualization(NFV)and Edge-computing,DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies.However,with the purpose of achieving the well-known scalability,flexibility,availability and performance guarantees benefits foreseen by the latest ICT,it is steadily required to experimentally profile and assess DT as a Service(DTaaS)solutions.Moreover,the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated.In this work,an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where,in compliance with the NFV paradigm,the service has been segmented in virtual network functions.Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements,and the number of robots consuming the service varies.Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations.Moreover,the same analysis has been carried out as a function of the industrial processes,namely based on the commands imposed on the robots,and particularly of their ion-level,resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees.The derived results provide crucial insights for the design of network service scaling and resource orchestration fra-meworks dealing with DTaaS applications.Finally,we empirically prove LTE shortage to accommodate the minimum DT latency requirements.

Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes

Energy management in smart homes is one of the most critical problems for the Quality of Life(QoL)and preserving energy resources.One of the relevant issues in this subject is environmental contamination,which threatens the world's future.Green computing-enabled Artificial Intelligence(Al)algorithms can provide impactful solutions to this topic.This research proposes using one of the Recurrent Neural Network(RNN)algorithms known as Long Short-Term Memory(LSTM)to comprehend how it is feasible to perform the cloud/fog/edge-enabled prediction of the building's energy.Four parameters of power electricity,power heating,power cooling,and total power in an office/home in cold-climate cities are considered as our features in the study.Based on the collected data,we evaluate the LSTM approach for forecasting parameters for the next year to predict energy consumption and online monitoring of the model's performance under various conditions.Towards implementing the Al predictive algorithm,several existing tools are studied.The results have been generated through simulations,and we find them promisingforfutureapplications.

Period–Luminosity Relationship forδScuti Stars Revisited

The Gaia DR3 parallax approach was used to estimate the absolute parameters of 2375δScuti stars from the ASAS catalog.The selected stars have a variety of observational characteristics,with a higher than 80%probability of beingδScuti stars.We have displayed all the stars in the Hertzsprung-Russell diagram along with theδScuti instability strip,the Zero Age Main Sequence and the Terminal Age Main Sequence.Then,we determined which fundamental and overtone modes each star belongs to using pulsation constant(Q)calculations.In addition,we evaluated the parameters in the Q calculation equation using three machine learning methods,which showed that surface gravity and temperature have the greatest effect on its calculation.The Period-Luminosity(P-L)relationship of theδScuti stars was also revisited.Eventually,using least squares linear regression,we made four linear fits for fundamental and overtone modes and updated their relationships.

Race Classification Using Deep Learning

Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.

PERFORMANCE ANALYSIS OF OPTIMAL CHANNEL ESTIMATION AND MULTIUSER DETECTION IN A RANDOMLY-SPREAD CDMA CHANNEL

This paper analyzes performance of optimal channel estimation and multiuser detection(MUD) in a block-fading code-division multiple-access (CDMA) channel on the assumptions of randomspreading and large-system limit,by using the replica method developed in statistical mechanics.The authors find that the asymptotic spectral efficiency of the linear minimum mean-squared error(LMMSE) MUD which was proposed and analyzed by Evans and Tse in 2000 is indistinguishable fromthat of the optimal MUD for small system loads.Our results imply that performance of MUD scarcelyimproves even if one spends more computational cost than that of the LMMSE MUD,i.e.,at most thecube of the number of users,on the above-described conditions.

On Hiding Secret Information in Medium Frequency DCT Components Using Least Significant Bits Steganography

This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques.

Design and analysis of a proportional-integral controller based on a Smith predictor for TCP/AQM network systems

Active queue management(AQM)is essential to prevent the degradation of quality of service in TCP/AQM systems with round-trip time(RTT)delay.RTT delays are primarily caused by packet-propagation delays,but they can also be caused by the processing time of queuing operations and dynamically changing network situations.This study focuses on the design and analysis of an AQM digital controller under time-delay uncertainty.The controller is based on the Smith predictor algorithm and is called the SMITHPI controller.This study also demonstrates the stability of the controller and its robustness against network parameter variations such as the number of TCP connections,time delays,and user datagram protocol flows.The performance,robustness,and effectiveness of the proposed SMITHPI controller are evaluated using the NS-2 simulator.Finally,the performance of the SMITHPI controller is compared with that of a well-known queue-based AQM,called the proportional-integral controller.

Effect of size and position of gold nanocrystals embedded in gate oxide of SiO_(2)/Si MOS structures

The influence of single and double layered gold(Au)nanocrystals(NC),embedded in SiO_(2) matrix,on the electrical characteristics of metal–oxide–semiconductor(MOS)structures is reported in this communication.The size and position of the NCs are varied and study is made using Sentaurus TCAD simulation tools.In a single NC-layered MOS structure,the role of NCs is more prominent when they are placed closer to SiO_(2)/Si-substrate interface than to SiO_(2)/Al–gate interface.In MOS structures with larger NC dots and double layered NCs,the charge storage capacity is increased due to charging of the dielectric in the presence of NCs.Higher breakdown voltage and smaller leakage current are also obtained in the case of dual NC-layered MOS device.A new phenomenon of smearing out of the capacitance–voltage curve is observed in the presence of dual NC layer indicating generation of interface traps.An internal electric field developed between these two charged NC layers is expected to generate such interface traps at the SiO_(2)/Si interface.

The relation between lesions and localization of sources of slow biphasic complexes in encephalitis

Slow biphasic complexes(SBC)were found in the electroencephalogram(EEG)of patients with inflammations of the brain.We have developed an automated method to identify them and proved that they represent a sensitive marker of the severity of encephalitis.Here we focus on another property of SBCs,i.e.,the localization of their sources.We present two encephalitic patients,showing lesions in the magnetic resonance images,which are either spread in the brain or focused on the left hemisphere,respectively.Applying a source localization algorithm to the identified SBCs,we found either a diffused or a left-focused distribution,respectively.This result further suggests a relation between neuroinflammation and appearance of SBCs,indicating that their distribution reflects in part the localization of brain lesions.This promising result extends the information that can be extracted from EEG,promoting the reduction of expensive or invasive measurements in encephalitic patients.

A 37 GHz Millimeter-Wave Antenna Array for 5G Communication Terminals

This work presents,design and specific absorption rate(SAR)analysis of a 37GHz antenna,for 5th Generation(5G)applications.The proposed antenna comprises of 4-elements of rectangular patch and an even distribution.The radiating element is composed of copper material supported by Rogers RT5880 substrate of thickness,0.254 mm,dielectric constant(εr),2.2,and loss tangent,0.0009.The 4-elements array antenna is compact in size with a dimension of 8mm×20mm in length and width.The radiating patch is excited with a 50 ohms connector i.e.,K-type.The antenna resonates in the frequency band of 37 GHz,that covers the 5G applications.The antenna behavior is studied both in free space and in the proximity of the human body.Three models of the human body,i.e.,belly,hand,and head(contain skin,fat,muscles,and bone)are considered for on-body simulations.At resonant frequency,the antenna gives a boresight gain of 11.6 dB.The antenna radiates efficiently with a radiated efficiency of more than 90%.Also,it is observed that the antenna detunes to the lowest in the proximity of the human body,but still a good impedance matching is achieved considering the−10 dB criteria.Moreover,SAR is also being presented.The safe limit of 2 W/kg for any 10 g of biological tissue,specified by the European International Electro Technical Commission(IEC)has been considered.The calculated values of SAR for human body models,i.e.,belly,hand and head are 1.82,1.81 and 1.09 W/kg,respectively.The SAR values are less than the international recommendations for the three models.Furthermore,the simulated and measured results of the antenna are in close agreement,which makes it,a potential candidate for the fifth-generation smart phones and other handheld devices.

Science Missions Using CubeSats

As the role of missions and experiments carried out in outer space becomes more and more essential in our understanding of many earthly problems,such as resource management,environmental problems,and disaster management,as well as space science questions,thanks to their lower cost and faster development process CubeSats can benefit humanity and therefore,young scientists and engineers have been motivated to research and develop new CubeSat missions.Not very long after their inception,CubeSats have evolved to become accepted platforms for scientific and commercial applications.The last couple of years showed that they are a feasible tool for conducting scientific experiments,not only in the Earth orbit but also in the interplanetary space.For many countries,a CubeSat mission could prompt the community and young teams around the world to build the national capacity to launch and operate national space missions.This paper presents an overview of the key scientific and engineering gateways opened up to the younger scientific community by the advent and adaptation of new technology into CubeSat missions.The role of cooperation and the opportunities for capacity-building and education are also explored.Thus,the present article also aims to provide useful recommendations to scientists,early-career researchers,engineers,students,and anyone who intends to explore the potential and opportunities offered by CubeSats and CubeSats-based missions.

Hidden Dimensions Hypotheses

Galileo introduced the scientific method, based on observation of natural phenomena, their interpretation and validation on the base of verified results. Albert Einstein broke the pattern of Galilean physics by postulating the invariance of the light speed and arguing that the laws of physics are valid independently of the inertial reference system used, introducing the concept of relativity and proposing a new physics based on four dimensions instead of the three previously known, with the addition of the variable time as the fourth dimension. In this article, we wonder if it is possible to find a methodology, necessarily not conventional, allowing to recognize additional new dimensions. The analysis of the quantization of nature has led us to hypothesize the use of irrational numbers in a ratio as a detector for any new dimension. A series of conjectures can bring us to imagine a scenario following the Big Bang with the creation of orthogonal matter, allowing us to find a seemingly plausible explanation to some unanswered questions of the contemporaneous physics. Under these hypotheses, it is possible to give two not conventional interpretations about gravity and light. The article prefers the logic explanation of the phenomena to their quantification through mathematics, reserving this last one only where it is necessary to clear the concept.