Miracle-dz/UniFAR-FTUs huggingface dataset:
A Versatile Multichannel Instrument for Measurement of Ratiometric Fluorescence Intensity and Phosphorescence Lifetime

Optical biosensing is being actively investigated for minimally-invasive monitoring of key biomarkers both <inline-formula> <tex-math notation="LaTeX">$in~ vitro$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$in~ vivo$ </tex-math></inline-formula>. However, typical benchtop instruments are not portable and are not well suited to high-throughput, real-time analysis. This paper presents a versatile multichannel instrument for measurement of emission intensity and lifetime values arising from luminescent biosensor materials. A detailed design description of the opto-electronic hardware as well as the control software is provided, elaborating a flexible, user-configurable system that may be customized or duplicated for a wide range of applications. This article presents experimental measurements that prove the <inline-formula> <tex-math notation="LaTeX">$in~ vitro$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$in ~vivo$ </tex-math></inline-formula> functionality of the system. Such tools may be adopted for many research and development purposes, including evaluation of new biosensor materials, and may also serve as prototypes for future miniaturized handheld or wearable devices.
A 1024-Channel Simultaneous Recording Neural SoC with Stimulation and Real-Time Spike Detection

A fully implantable brain-machine interface (BMI) targeting clinical applications has stringent size and power requirements. In this paper we present a 5×4mm2 neural system-on-chip (SoC) capable of recording and stimulating from 1024 implanted electrodes via a serial digital link. The design has on-chip configurable spike detection that can reduce off-chip bandwidth by 1250×. With fully integrated power management circuitry with power-on-reset and brown-out detection, our design consumes 24.7mW total power consumption, making it the lowest-power, highest-density AC-coupled neural SoC reported for recording both local field potential (LFP) and action potential (AP) with a 5Hz-10kHz bandwidth.
A compact H-band Power Amplifier with High Output Power

We report a compact H-band power amplifier with high output power in 250nm InP HBT technology. Stacking and parallel power combining together provide the desired output power. Common-base stages with base capacitive degeneration act as stacked power cells. Four power cells are combined by a compact low-loss 4:1 transmission line network. At 270GHz, the four-stage amplifier has 16.8dBm saturated output power with 4% power-added efficiency (PAE). Over 266-285GHz, the amplifier's saturated output power is 14-16.7dBm with an associated 2.2-4%PAE. The 3-dB small-signal bandwidth extends from 233GHz to 281GHz with a peak gain of 20.5dB at 264GHz. The amplifier has a compact area of $\mathrm{1.08} \text{mm}\times \mathrm{0.77}\text{mm}$ and $P_{\text{sat}}$ /mm2 of 57.6mW/mm2. To the authors' knowledge, these results demonstrate a record output power and $P_{\text{sat}}/\text{mm}^{2}$ for H-band amplifiers working around 270GHz.
Improved Thermoelectric Power Factor of InGaZnO/SiO₂ Thin Film Transistor via Gate-Tunable Energy Filtering

Progress on improving the thermoelectric power factor <italic>(PF)</italic> of InGaZnO thin films by various surface treatment techniques has been seriously hindered by the innate coupled but opposing relationship between its electrical conductivity (<inline-formula> <tex-math notation="LaTeX">$\sigma _{{\mathrm {e}}}$ </tex-math></inline-formula>) and Seebeck coefficient <italic>(S)</italic>. Therefore, proposing unique solutions to decouple these properties is crucial in advancing its thermoelectric performance. This study demonstrates that the <inline-formula> <tex-math notation="LaTeX">${S} - \sigma _{e}$ </tex-math></inline-formula> coupled relationship can be suppressed by integrating InGaZnO in an InGaZnO/SiO<sub>2</sub> thin film transistor, leading to a significantly enhanced <italic>PF</italic> compared to pristine InGaZnO thin films. An exponential increase in <inline-formula> <tex-math notation="LaTeX">$\sigma _{e}$ </tex-math></inline-formula> was observed without the typical severe decline in <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>. An extremely narrow charge accumulation layer near the InGaZnO/SiO<sub>2</sub> interface could have formed under a positive applied gate voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{G}$ </tex-math></inline-formula>), causing a dramatic reduction of the thermoelectrically active region to an almost two-dimensional surface. By utilizing the Kamiya-Nomura percolation modeling, it was observed that energy filtering occurs at the InGaZnO/SiO<sub>2</sub> interface. This likely causes a change in the shape of the potential barriers, which leads to the suppression of the <inline-formula> <tex-math notation="LaTeX">${S} - \sigma _{e}$ </tex-math></inline-formula> coupling relationship.
Performance Measurement of Federated Learning on Imbalanced Data

AI often suffers from getting imbalanced data distribution as unequal samples in classes which will increase the bias of machine learning algorithms. This research aimed to study effects of skew data distribution towards development of data rebalancing on Federated learning (FL) in the future. This research sets left skewed distribution, right skewed distribution and symmetric distribution on Modified National Institute of Standards and Technology database (MNIST) to operate on Convolutional neural network (CNN) in FL mechanism. Then, FL’s performance for working on these imbalanced distributions was tested. Results showed that in overview, the symmetric, left skewed, and right skewed distribution were not different in accuracy but theses imbalanced distributions were different in accuracy from the balanced distribution which has equal samples in all classes at significant level of.05. Standard deviation (SD) of data distribution was directly correlated with FL’s accuracy in high level.
Compact Modeling of Nonlinear Contact Effects in Short-Channel Coplanar and Staggered Organic Thin-Film Transistors

We present analytical physics-based compact models for the Schottky barriers at the interfaces between the organic semiconductor and the source and drain contacts in organic thin-film transistors (TFTs) fabricated in the coplanar and the staggered device architecture, and we illustrate the effect of these Schottky barriers on the current–voltage characteristics of the TFTs. The model for the source barrier explicitly considers the field-dependent barrier lowering due to image charges. Potential solutions have been derived by applying the Schwarz–Christoffel transformation, leading to expressions for the electric field at the source barrier and for the contact resistance at the source contact. With regard to the drain barrier, a generic compact-modeling scheme based on the current–voltage characteristics of a barrier-less TFT is introduced that can be applied to any compact dc model. Finally, both models are incorporated into an existing charge-based compact dc model and verified against the results of measurements performed on coplanar and staggered organic TFTs with channel lengths ranging from 0.5 to $10.5~\mu \text{m}$ .
One stage gain boosted power driver at 184 GHz in 28 nm FD-SOI CMOS

In order to improve amplifiers' power gain for a close to $f_{max}$ operation, a methodology to size the embedding of any active two port is described and then applied to the design of a single-stage amplifier as a proof of concept. The 184 GHz measured 28 nm FD-SOI CMOS amplifier presents a power gain of 7.6 dB, a bandwidth of 20 GHz, a $P_{sat}$ of −3.7 dBm and a peak PAE of 4.2% for a power consumption of 5.1 mW.
A generalized algorithm for continuous-time distributed optimization

This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.
Dynamic Fall Recovery Motion Generation on Biped Robot With Shell Protector

Fall recovery after falling over is a critical feature for biped robots that work in the real world. We propose a method by considering robot joint angle trajectory as a multivariable function of time and orientation of robot root links. Compared with the conventional method, our method required less time and human effort, as the joint trajectory was autonomously optimized via evolutionary search in a dynamic simulator. In addition, the gap between simulation and real-world experiments could be narrowed by adding push disturbances when performing evolutionary search in the dynamic simulator. Using the proposed method, we successfully realized the fall-recovery feature on life-sized biped robot that was equipped with shell protector CHIDORI-F as depicted in Fig. 1. Combining the aforementioned fall-recovery feature and previously developed fall-tolerant robot hardware, fall detector and fall-damage-reduction motion, we realized a biped robot platform that could tolerate falling, recover from all the fallen states, and continue working for considerable time without a tether.
A Romanian Solution for Real-time Monitoring of Overhead Transmission Lines

Based on the need to make more intensive use of transmission system infrastructure, transmission system operators (TSOs) have begun to investigate what are the most appropriate measures to achieve this goal. For TSO, the construction of new transmission lines is not a simple procedure, mainly due to strict environmental regulations. It is becoming increasingly clear that replacing, building and equipping with the necessary new lines will become one of the important problems difficult to achieve, which can jeopardize the achievement of the EU’s 20-20-20 objectives. Therefore, a new approach has become mandatory in order to increase the efficiency of existing electricity networks in a safe way and to deliver the required energy in a timely and economical manner.In Romania, the technical conditions for the on-line OHL monitoring systems are mentioned in the internal technical norm “Technical specification for complex system for online monitoring of overhead power lines of CNTEE Transelectrica SA”. The monitoring system is appropriate to be used for 110 kV, 220 kV and 400 kV overhead lines.The paper presents the OHLM system, a Romanian innovative system for online monitoring of the operational parameters of the OHL’s (based mainly on real-time measurements, such as: current through the line, conductor temperature, conductor inclination angle/sag, mechanical tension of the line, conductor oscillations, etc.), that includes also intelligent electronic devices for acquisition / processing / storage and remote data transmission (to the power system operation dispatcher / the power system diagnosis and condition monitoring center / the data management center, etc.).
ToP: Time-dependent Zone-enhanced Points-of-interest Embedding-based Explainable Recommender system

Points-of-interest (POIs) recommendation plays a vital role by introducing unexplored POIs to consumers and has drawn extensive attention from both academia and industry. Existing POI recommender systems usually learn latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatiotemporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers complicated check-in actions. In this paper, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe POIs dynamic function. Besides, check-in actions to a POI is also affected by the zone it belongs to. In other words, the zone’s embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance the accuracy of POI recommendations. Along this line, we propose a Time-dependent Zone-enhanced POI embedding model (ToP), a recommender system that integrates knowledge graph and topic model to introduce the spatiotemporal effects into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in different time to capture its dynamic functions. Jointly combining these vectors with zones representations, ToP enhances the spatiotemporal interpretability of POI recommendations. With this hybrid architecture, some existing POI recommender systems can be treated as special cases of ToP. Extensive experiments on real-world Changchun city datasets demonstrate that ToP not only achieves state-of-the-art performance in terms of common metrics, but also provides more insights for consumers POI check-in actions.
Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people’s well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
Designing a Marker Set for Vertical Tangible User Interfaces

Tangible User Interfaces (TUI)s extend the domain of reality-based human-computer interaction by providing users the ability to manipulate digital data using physical objects which embody representational significance. Whilst various advancements have been registered over the past years through the development and availability of TUI toolkits, these have mostly converged towards the deployment of tabletop TUI architectures. In this context, markers used in current toolkits can only be placed underneath the tangible objects to provide recognition. Albeit being effective in various literature studies, the limitations and challenges of deploying tabletop architectures have significantly hindered the proliferation of TUI technology due to the limited audience reach such systems can provide. Furthermore, available marker sets restrict the placement and use of tangible objects since if placed on top of the tangible object, the marker will interfere with the shape and texture of the object limiting the effect the TUI has on the end-user. To this end, this paper proposes the design and development of an innovative tangible marker set specifically designed towards the development of vertical TUIs. The proposed marker set design was optimized through a genetic algorithms to ensure robustness in scale invariance, the capability of being successfully detected with distances of up to 3.5 meters and a true occlusion resistance of up to 25%, where the marker is recognized and not tracked. Open-source versions of the marker set are provided through research license on www.geoffslab. com/tangiboard_marker_set.
Using an Android Robot to Improve Social Connectedness by Sharing Recent Experiences of Group Members in Human-Robot Conversations

Social connectedness is vital for developing group cohesion and strengthening belongingness. However, with the accelerating pace of modern life, people have fewer opportunities to participate in group-building activities. Furthermore, owing to the teleworking and quarantine requirements necessitated by the Covid-19 pandemic, the social connectedness of group members may become weak. To address this issue, in this study, we used an android robot to conduct daily conversations, and as an intermediary to increase intra-group connectedness. Specifically, we constructed an android robot system for collecting and sharing recent member-related experiences. The system has a chatbot function based on BERT and a memory function with a neural-network-based dialog action analysis model. We conducted a 3-day human-robot conversation experiment to verify the effectiveness of the proposed system. The results of a questionnaire-based evaluation and empirical analysis demonstrate that the proposed system can increase the familiarity and closeness of group members. This suggests that the proposed method is useful for enhancing social connectedness. Moreover, it can improve the closeness of the user-robot relation, as well as the performance of robots in conducting conversations with people.
High-performance Four-channel Analog Optical Transceiver for 5G ARoF-based Mobile Fronthaul

A high-performance 4-channel DML-based analog optical transceiver is designed, and novel impedance-matching using 40-ohm TFR nested in AlN-carrier is proposed. The EVM of 4×600Mbit/s LTE-64QAM-OFDM signal with carrier-frequency from 2-12GHz over 15.5km SSMF is <3.2%.
Decentralized Localization in Homogeneous Swarms Considering Real-World Non-Idealities

This letter presents a decentralized algorithm that allows a swarm of identically programmed agents to cooperatively estimate their global poses using local range and bearing measurements. The design of our algorithm explicitly considers the phase asynchrony of each agent's local clock, moreover, the execution of our algorithm does not require each agent to actively keep the same neighbors over time. A theoretical analysis about the effect of each agent's sensing noise and communication loss is given, in addition, we validate the presented algorithm via experiments running on a swarm of up to 256 simulated robots and a swarm of 100 physical robots. The results from the experiments show that the presented algorithm allows each agent to estimate its global pose quickly and reliably. Video of 100 robots executing the presented algorithm as well as supplementary material can be found in [1] and [2].
Generalized Coherent Point Drift With Multi-Variate Gaussian Distribution and Watson Distribution

This letter introduces a novel rigid point set registration (PSR) approach that accurately aligns the pre-operative space and the intra-operative space together in the scenario of computer-assisted orthopedic surgery (CAOS). Motivated by considering anisotropic positional localization noise and utilizing undirected normal vectors in the point sets (PSs), the multi-variate Gaussian distribution and the Watson distribution are utilized to model positional and normal vectors’ error distributions respectively. In the proposed approach, with the above probability distributions, the PSR problem is then formulated as a maximum likelihood estimation (MLE) problem and solved under the expectation maximization (EM) framework. Our contributions are three folds. First, the rigid registration problem of aligning generalized points with undirected normal vectors is formally formulated in a probabilistic manner. Second, the MLE problem is solved under the EM framework. Third, the gradients of associated objective functions with respect to desired parameters are computed and provided. Experimental results on both the human pelvis and femur models demonstrate the potential clinical values and that the proposed approach owns significantly improved performances compared with existing methods.
OTA-free 1-1 MASH ADC using Fully Passive Noise Shaping SAR & VCO ADC

We present an OTA-free 1-1 MASH ADC utilizing a fully passive noise shaping (FPNS) SAR as first-stage and open-loop VCO ADC as second stage. The key contribution of this work is to address the challenge of driving large sampling capacitor for high resolution NS-SAR. The proposed architecture reduces resolution of SAR stage and leverages residue attenuation due to passive charge sharing in the FPNS SAR to linearize the VCO. Combining an FPNS SAR with a VCO ADC shapes in-band thermal noise of VCO and SAR comparator at ADC output. Additionally, we demonstrate a computationally inexpensive foreground inter-stage gain calibration algorithm for the proposed ADC architecture. The prototype ADC consumes 0.16mW while achieving an SNDR/DR of 71.5/75.8dB over a 1.1MHz bandwidth and walden FoM of 23.3fJ/step which is the lowest in 65nm technology.
A 140 GHz T/R Front-End Module in 22 nm FD-SOI CMOS

This paper presents a D-band front-end module (FEM) with an integrated transmit/receive (T/R) switch in 22-nm fully-depleted silicon on insulator (FD-SOI) CMOS technology for beyond-5G communication. The asymmetric T/R switch topology with intrinsic ESD protection leverages the Tx- and Rx-mode RF performance. Both the PA and LNA have a differential topology with transformer-based matching networks to eliminate unwanted effects from common-mode parasitics, especially at the antenna port. The adopted stacked-FET PA achieves a high output power while consuming much less area than a PA based on passive power combining. At 140 GHz, the Tx achieves a power gain $G_{p}$ of 33.6 / 35.7 dB, a saturated output power Psat of 12.5 / 14.7 dBm, a peak power-added efficiency PAE of 10.8 / 11.3%, and output 1-dB compression point (OP1dB) of 9.4 / 11.2 dBm with nominal/boosted supplies. At 140 GHz, the Rx achieves a 20-dB $G_{p}$, a −24-dBm input 1-dB compression point (IP1dB), and a 9.2-dB noise figure (NF) with only 20-mW power consumption from a 0.8-V supply. This compact FEM has a PA / LNA core area of 0.024 / 0.032 mm2, respectively.
Physical Modeling of Charge Trapping in 4H-SiC DMOSFET Technologies

Silicon carbide (SiC) MOSFETs still exhibit higher drifts of the threshold voltage than comparable silicon devices due to charge trapping, especially regarding small time scales. Understanding this behavior and the consequences in application relevant conditions is therefore of high research interest. Since charge trapping at different defects close to the SiC/ SiO2 interface and in the bulk oxide is strong bias and temperature-dependent, the phenomenon is referred to as bias temperature instability (BTI). It has been shown that drifts caused by BTI vary both in transient shape and magnitude for commercially available devices. These differences arise from defect densities and properties in the respective technologies. A physical model together with defect parameters that explain the charge transfer reactions at the defects is essential to understand all peculiarities of the transient degradation. In this work, we use a novel method to semiautomatically extract defect parameters within a two-state nonradiative multiphonon model framework. Our work reveals properties of defects responsible for shifts of the threshold voltage for both short-term ac and long-term dc stress conditions which are accurately reproduced for three different DMOS technologies. Our calibrated simulation framework is further used to extrapolate device degradation at operation relevant ac bias conditions to typical device lifetimes.