Miracle-dz/UniFAR-FTUs huggingface dataset:
A 103 dBΩ 977 MHz Transimpedance Amplifier for 149 MHz Capacitive MEMS Disk Resonator 1

This paper presents a 103-dBΩ 977-MHz transimpedance amplifier (TIA) for driving VHF-band radial contour-mode MEMS disk resonators. Although the simulation results are given, it is the first time to drive 149 MHz resonators with motional resistance of up to 20 kΩ by using an on-chip circuit. The proposed TIA mainly consists of a regulated-cascode (RGC) amplifier, an inverter-based Cherry-Hooper amplifier, and two buffers. The highlight of the TIA is the resistance-capacitance network, which provides a stable input bias for the Cherry-Hooper amplifier and introduces a leading phase to compensate for the excessive lagging phase of the TIA at 149 MHz. Implemented in the 0.18 μm CMOS process, the TIA occupies a silicon area of 90 μm*40 μm. The simulated gain of the TIA is 103 dBΩ, the -3 dB bandwidth is 977 MHz, and the input-referred current noise is 19.4 pA/√Hz. The MEMS oscillator sets up within 160 μs with an amplitude of 1.37 Vp-p. The simulated phase noise is -114 dBc/Hz at 10 kHz offset and -118 dBc/Hz at 100 kHz offset. The power consumption without buffers is 1.1 mW under supplies of 1.8 V and 1.6 V.
Numerical Simulation of Influence of the Thermal and Mechanical Fluctuations in the Coupling Elements of Microresonators

Ultra-stable lasers of high spectral purity are the technological basis for optical atomic clocks, quantum measurements, stable microwave signal sources and high-resolution optical spectroscopy. For these purposes, lasers with a linewidth of the order of hertz are used. Such lasers are usually stabilized with Fabry-Perot resonators made of ultra-low expansion materials, which are very fragile and bulky. Therefore, there is a clear demand for reliable miniature lasers with narrow linewidths. Optical resonators with a "whispering gallery" mode (WGM) make it possible to create narrow-band microlasers on their basis using self-injection locking method. But they have not yet reached the stability determined by their fundamental noise. Here we calculate the noise characteristics of lasers self-injection locked to WGM microresonators and estimate the linewidth limitations due to thermorefractive, thermoelastic, photoelastic and Brownian noise. We also calculated the total noise in an integrated waveguide and obtained an estimate of the linewidth of 0.5 kHz at an averaging time of 1 s.
Building structural design generation and optimisation including spatial modification

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Scalar Potassium Magnetometer Based On Amplitude Modulated Nonlinear Magneto-Optical Rotation

In this work, a scalar potassium magnetometer based on amplitude modulated nonlinear magneto-optical rotation (AM NMOR) is experimentally built and reaches noise level of $30{\text{fT}}/\sqrt {{\text{Hz}}} $ under a low-noise bias magnetic field. Our potassium magnetometer could be applied to a large earth’s magnetic field, where the performance of cesium magnetometer is limited by nonlinear Zeeman effect (NLZ). A group of totally separated magnetic-resonance lines are got and each line has a FWHW (full width half maximum) of ~10 Hz , which is 10-times narrower than cesium’s magnetic-resonance linewidth in the same case. The noise-level of $130{\text{fT}}/\sqrt {{\text{Hz}}} $ is achieved under bias magnetic field of 100000 nT, which is mainly due to ambient magnetic-field noise.
Joint channel estimation and multi-user detection for multipath fading channels in DS-CDMA systems

The technique of joint blind channel estimation and multiple access interference (MAI) suppression for an asynchronous code-division multiple-access (CDMA) system is investigated in this research. To identify and track dispersive time-varying fading channels and to avoid the phase ambiguity that come with the second-order statistic approaches, a sliding-window scheme using the expectation maximization (EM) algorithm is proposed. The complexity of joint channel equalization and symbol detection for all users increases exponentially with system loading and the channel memory. The situation is exacerbated if strong inter-symbol interference (ISI) exists. To reduce the complexity and the number of samples required for channel estimation, a blind multiuser detector is developed. Together with multi-stage interference cancellation using soft outputs provided by this detector, our algorithm can track fading channels with no phase ambiguity even when channel gains attenuate close to zero.
Microalgae as potential bioindicators for heavy metal pollution

Heavy metals (HMs) are considered persistent pollutants due to their low degradation rate. They can be easily accumulated along the food chain and reach the higher trophic levels, posing a threat for both the environment and human health. Microalgae, being at the base of food webs in marine and fresh waters, are one of the first groups to be affected by HM pollution and can be considered potential HM indicators in aquatic environments. In this frame we propose QPM (quantitative phase microscopy) optical techniques as an innovative approach for rapid detection of HMs stress on microalgae. Furthermore, since some HMs are considered essential elements for cell growth and metabolism, microalgae have designed strategies for their uptake and have been thus considered attractive candidates for bioremediation purposes. In the present work, a strain of Skeletonema pseudocostatum isolated from the mouth of the Sarno river (Gulf of Naples) was exposed to several concentrations of copper to test its tolerance to the HM in 72 hours small-scale experiments. A lethal (25 μM) and a sub-lethal (10 μM) copper concentration were identified and used for 72 hours scale-up experiments. The rate of copper adsorption/compartmentalization was evaluated by ICP-MS analyses of microalgal pellets at the end of each experiment. S. pseudocostatum showed tolerance to copper up to a concentration of 10 μM, with a rate of removal of 14% of copper administered in solution in larger scale experiments. These results seem promising considering future possible applications of this species for HM removal. In addition, preliminary QPM investigations of copper effects on cellular and subcellular morphology seem to suggest that these techniques could aid in elucidating S. pseudocostatum potential as a sensor of HM environmental pollution.
Digital column readout architecture for the ATLAS pixel 0.25 /spl mu/m front end IC

A fast, low noise, limited power, radiation-hard front-end chip was developed for reading out the ATLAS pixel silicon detector. As in the past prototypes, every chip is used to digitize and read out charge and time information from hits on each one of its 2880 inputs. The basic column readout architecture idea was adopted and modified in order to allow a safe transition to quarter micron technology and overcome any possible situation where single point defects and charge deposited by heavy ions in the silicon could have lead to unpredicted forbidden states. Moreover, the smaller feature sizes and the higher speed of these processes allowed us to enhance the design of the basic blocks and adopt a fail safe, design for testability approach.
Aperture Excitation of Electrically Large, Lossy Cavities

We present a theory based on power balance for aperture excitation of electrically large, lossy cavities. The theory yields expressions for shielding effectiveness, cavity Q, and cavity time constant. In shielding effectiveness calculations, the incident field can be either a single plane wave or a uniformly random field to model reverberation chamber or random field illumination. The Q theory includes wall loss, absorption by lossy objects within the cavity, aperture leakage, and power received by antennas within the cavity. Extensive measurements of shielding effectiveness, cavity Q, and cavity time constant were made on a rectangular cavity, and good agreement with theory was obtained for frequencies from 1 to 18 GHz. >
DDE process: A requirements engineering approach for machine learning in automated driving

Machine learning (ML) is key to achieve complex automation like in self-driving cars: implementation of implicit requirements and faster time-to-market are just two promises. Despite technological advances, research questions remain open about improving the level of trust and quality (quality in terms of ISO 25010) that can be placed on such ML-based systems. Their quality depends on the quality of the data used for training and appropriate verification and validation. This data quality - and with it the confidence in ML - relies on a systematic and structured process incorporating hierarchical requirements engineering for the quality and composition of data sets.This paper presents the data-driven engineering process (DDE process) as a new systematic and structured approach for leveraging future application of ML in industry. The DDE process includes hierarchical requirements engineering to link the operational design domain with the requirements and semi-automated generation of data sets. We describe the DDE process as a Vmodel that is fully integrated with other engineering processes. It represents a consistent approach that harmonizes development abstraction levels and DDE for ML as a third technology next to hardware and software (section III). Furthermore, the DDE process allows process automation leading to automated data set compilation. Applicability of the DDE process is shown by an application example using a convolutional neural network for traffic light detection (section IV). A summary and next steps are concluding the paper (section V).
Performance evaluation of a 5G device in a non public network

5G technology is one of the most prominent wireless communication technologies to cope with industrial applications. However, it is still necessary to investigate the temporal behavior in the transmission of messages from the application point of view. This work presents test cases that evaluate response time and update time of a 5G system composed by a 5G device and a 5G non-public network.
An Efficient Transfer Learning-based Model for Classification of Brain Tumor

Brain tumor is ranked 12th deadliest cancerous tumor among children and adults. An early detection and identification of its type can help radiologists and medical practitioners in defining an effective treatment. Therefore, this study aims to devise an efficient but accurate human braintumor classification tool that exploits magnetic resonance imaging (MRI) to segregate glioma, meningioma, pituitary, and no tumor. For this, the study exploits a dataset of 7023 MR images and performed various image pro-processing steps to get brain image by removing unwanted noisy areas and margins. Further, it incorporates data augmentation techniques to enhance limited dataset and avoid overfitting issue. Finally, a modified pre-trained deep learning-based ResNet152V2 model is trained. Two separate experiments are conducted by training proposed model with and without augmented data. It is observed that the proposed network trained on augmented data significantly outperformed the network trained on original data by successfully distinguishing four clinical states of brain tumor with an overall accuracy of 98.9%.
Deep U-Net Network for identifying Covid 19 infection Using X Ray Images

The Novel coronavirus (COVID-19) detection is a challenging task for physicians and clinical people. Due the higher rate of affected people, the clinical tests take too much of time to provide the test results of the patients. The alternate screening is the X-ray and CT images to detect the infection in respiratory system. The classification is complex for visual examination in the X-ray, CT images due to COVID, Pneumonia and other respiratory problems. In this paper, a novel Unet is proposed for segmentation and classification of COVID with X-ray images to other diseased images. In this analysis 2000 COVID X-ray images, 2000 bacteria and 2000 normal images are used. Overall, 6000 test images are considered to train the network for classification. The proposed method provides the accuracy of 98.49 %.
Ultrasound Phased Array Radar Design for Ranging and Direction of Arrival Estimation

The design of an air-coupled ultrasound phased array radar is presented. The target application is for the estimations of object distance and direction in short range. The main technical contributions of this design include 1) mitigating the estimation alias problem introduced by a under-sampling phased array front end, and 2) developing a low computing complexity DoA estimation scheme and implemented in an FPGA device. The ultrasound transducer array front end is built from discrete air-coupled components. A skewed two-row array configuration consisting of one transmitter and 8 receivers in total is introduced to tackle the under-sampling problem due to excessive transducer component size. The FoV (field of view) alias free estimation is extended to ±50 degrees. The proposed system adopts a pulse modulation scheme and performs ranging based on time-of-flight. A low complexity orthogonal matching pursuit (OMP) scheme is employed for DoA estimation. The maximum detection distance is 2 meters with a 3-cm resolution. In addition, a maximum of 4 objects of the same distance can be distinguished with an angular resolution of 5 degrees and a 78.1% accuracy rate. The hardwired design of the proposed algorithm is also developed and implemented in an FPGA device. It consists mainly of a 32-point FFT computing kernel, a QR decomposition module, a matching pursuit module. The design operates at 48MHz and support a new estimation every 14.5ms.
Changing The Paradigm In Common Mode Transient Immunity (CMTI) Testing Of Dual Channel Galvanically Isolated Gate Drivers

This paper presents a method to measure the CMTI performance of dual channel galvanically isolated gate drivers using transformers and filtering capacitors on the outputs. As a result, typical application CMTI conditions are replicated. The working principle was successfully verified using a 5 V and 50% duty cycle PWM pulse with 10 ns rise and fall times. Three commercially available dual channel galvanically isolated gate drivers were used to evaluate the proposed setup in comparison with the state-of-the-art approach. CMTI slopes of ±50 kV/μs, ±95 kV/μs, and ±125 kV/μs, together with a common mode pulse of ±1.5 kV were used in the assessment. The state-of-the-art method showed several CMTI failures, whereas the proposed setup passed all conditions.
Performance Study of Large Scale Network Slice Deployment in a 5G Core Testbed

Network slicing in 5G forms a pivotal enabler to satisfy unprecedented requirements in terms of data rate, latency, and capacity in the form of heterogeneous network services namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building end to end logical network. In this paper, we study the performance of various network slice deployment modes in a Network Function Virtualization (NFV) based 5G Core (5GC) for High Availability (HA) and Reliable functioning keeping in mind Capital Expenditure (CAPEX) and Operating Expenditure (OPEX). Our experimental results on the in-house 3GPP compliant testbed prototype of 5GC show that the completely shared slice mode saves the resources and its cost (about 66.6% compared to the isolated mode). However, it risks HA and reliability of slice service by consuming additional time (about 40 seconds) during activation, for serving a maximum of 90K subscribers. Whereas, the completely isolated mode caters to reliable slice service using dedicated resources but, with higher CAPEX and OPEX to Telecommunications Service Provider (TSP)s. A reasonable middle-ground partially shared mode of slice functioning balances both reliability and cost concerns. We believe that this work will guide TSPs in building autonomous, smart management, and orchestration of network slice deployments.
Advanced Materials: Challenges and Opportunities

SUMMARY Our ability to solve the engineering challenges oftoday and tomorrow is still being driven by cost andavailability. Materials and processing methods thatyield equivalent or improved performance at lower cost,such as the SCRIMP ® , rapid prototyping, and othertechnologies referred to in this article, are being active-ly pursued. Additional streamlining and optimization ofthe design process will be needed to further reduce thecost of implementing new materials, to have confi-dence in their ability to perform as intended, and totake full advantage of their properties. Our understand-ing of how materials interact, how they can be reliablyjoined or integrated, which ones to select or avoid forspecific applications or environments, and even how tochange or tailor their properties is expanding daily.The challenges, as well as the opportunities, willbe in all areas of materials development: synthesis,fabrication, integration, characterization, and inspec-tion. Sensor development will continue to requirenew, targeted materials. Fabrication methods will bedriven to less costly and faster processes. Next-gener-ation (“smart”) materials will need to continue tofunction over time when joined or even intimatelyintegrated with other components. New and especiallyexisting materials will need to be characterized inmore depth and with greater accuracy so that theirperformance can be pushed to the limits more reliably.Finally, designed and integrated materials will mostlikely present more difficulties in verifying their integ-rity and reliability by conventional means than everbefore. TSD will continue to support APL’s missionareas by maintaining and expanding its capabilities tokeep pace with advances in materials and associatedtechnologies.
A Miniaturized Frequency Selective Surface Based on Square Loop Aperture Element

We propose a miniaturized band-pass frequency selective surface (FSS) with periodic unit cell structure. The proposed FSS is realized by symmetrically bending the edges of the square loop aperture element, by which our proposed FSS increases the resonant length, and, hence, reduces its size. In this FSS, each unit cell has a dimension of 0. 0538λ × 0. 0538λ, where λ represents the wavelength of the corresponding resonant frequency. Both the theoretical analysis and simulation results demonstrate that our proposed FSS, having high polarization stability and angle stability, can achieve smaller size in comparison with the previously proposed structures.
Hubbing in U.S. Air Transportation System: Economic Approach

The dynamics of airline hubbing in the postderegulation U.S. air transportation system are examined. It is asserted that hubbing is not the result of arbitrary decisions by airlines but of economic forces that allow hubbed traffic to be understood and possibly predicted. On the basis of three primary hubbing criteria, an economic model of hub choice is developed. In the model, hubbing is assumed to be (a) intricately tied to the national air network, (b) grounded in the airlines' economic cost structure, and (c) the result of a balance of supply and demand forces. The route choice model is estimated for domestic U.S. hub markets. The model results support the first two of the three hubbing criteria and produce relative valuations of in-flight time, airport capacity delays, and schedule delays for connecting flights. These results are valuable in understanding the interplay of forces that lead to hub development and decline. In addition, the model structure is promising as a planning tool to investigate the redistribution of traffic in the U.S. air transportation network under various development scenarios and policy actions.
Driver-in-the-Loop for computer-vision based ADAS testing

Driver safety is a main concern nowadays. Hence, a real need for systems like Advanced Driver Assistance Systems (ADAS) has arisen. With this need to such systems, many challenges come up. These challenges are including the time consumption of testing of these systems as It needs to be tested in different weather conditions. Besides, the difficulty of covering all the possible scenarios while testing these systems and the carbon dioxide emission from cars in the process of testing the system. Hence, the purpose of the proposed paper is to implement a simulation-based driver in the loop simulation-based system for indoor testing of computer vision based ADAS. A simplified tricycle kinematics model is implemented for this purpose by using Matlab/Simulink. A vehicle virtual environment is implemented in unreal engine environment by MATLAB platform. The interaction of the driver with the virtual car in this virtual environment is made by a simplified hardware for the car. A feedback is taken from the driver through sensors connected to an Arduino microcontroller. Arduino microcontroller interpret the driver feedbacks and feed it as an inputs to the vehicle model to move the car in the virtual environment. The proposed system is tested for computer vision lane detection, departure, and change algorithms. These algorithms relay mainly on Hough transform. The proposed system is tested in real time and the results obtained is very similar to the results obtained from testing a computer vision based ADAS on a real street.
A holistic method for reliability performance assessment and critical components detection in complex networks

Many infrastructures are now considered to be critical for both the economic development and general functioning of modern societies. Thus, understanding their performance is important as a basis to develop intelligent and cost-effective ways to protect these networks. In this article, a critical infrastructure is modeled as a complex network for which a new metric is defined to understand its reliability. This metric called reliability Π describes the average reliability between every pair of nodes in a complex network. As such, it is related to the two-terminal reliability concept in the traditional network context. Furthermore, in an effort to identify the most critical components that affect reliability Π, a multi-objective optimization problem, known as the critical component detection problem, is introduced. The solution to this problem provides two important insights about the behavior of a complex network: (i) an approximation to the set of optimal solutions that identifies the most critical components; and (ii) a quantitative assessment of how these failures affect the complete complex network.
Review of the regulations for the use of stainless steels for orthopedic implants in Argentina

Motivated by the relatively high rate of failure of orthopedic implants in Argentina, the authors review the current normative regulating the use of stainless steels in the fabrication of these metallic parts in the country, and compare it with the regulations currently in use in other countries. The analysis shows that several standards in effect in the country do not comply with broadly recognized international standards. This situation is aggravated by a recent revision of the normative that failed to improve the quality standards to reach levels similar to those applied in developed countries or even in MERCOSUR associates. The national organization in charge of implant certification in Argentina, complying with the law, accepts the applicability of IRAM standards to certify stainless steels implants. In the opinion of the authors, the current practice used to certify implants does not guarantee the structural stability and biocompatibility of the devices, increasing the risk of failure in service, and escalating the cost of the public health care system.