Miracle-dz/UniFAR-FTUs huggingface dataset:
A 7.4μJ.ppm2 Resistance Sensor with ±120ppm (3σ) 1-Point-Trimmed Inaccuracy and <4ppm/°C Temperature Drift from -55°C to 125°C

Resistance sensors are widely used for acquiring environmental parameters, such as temperature [1], [2], chemical [3], and pressure [4]. To suit the needs of high-end Internet of Things (loT) applications, such sensors should be both energy-and cost-efficient but not at the expense of their accuracy and stability over PVT. Traditionally, such sensors are referenced to high-accuracy low-drift off-chip resistors [3, 5], which are bulky and expensive. For those referenced to onchip resistors [1, 6], their accuracy is limited by process variations, while their temperature stability is limited to tens of $\mathrm{ppm} /{}^{\circ} \mathrm{C}$. Alternatively, resistance can be converted to frequency via a capacitive reference [2]. However, [2] is designed for sensing on-chip thermistors, and its overall performance, especially area and inaccuracy, is still limited by the readout electronics.
Manufacturing Cost Saving Through Reduced Metrology Using Time Series Analysis

Inline metrology is critical in providing data used in advanced process control (APC) control loops. However, fabs benefit from running with as few metrology tools as possible while meeting quality and control requirements. This paper uses time series analysis to show the relationship between sampling rates and closed-loop variance of feedforward and feedback-controlled processes. The approach was used in GlobalFoundries’ Fab8, in Malta NY, to identify opportunities for reduced process sampling. A 7% reduction in metrology was achieved at a critical toolset, making it possible to avoid capital expenditure, improved overall cycle time, and reduced per wafer measurement cost.
Delay-aware and Resource-efficient VNF placement in 6G Non-Terrestrial Networks

Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.
A Study on Passive and Active Detection of Missiles

The Strategic missiles attempts to attack the enemy forces at a distance greater than ten thousand kilometers which are either propelled by the jet engines or the rockets. The detection and destruction of the missiles is a significant step for a nation to protect its land. Missile countermeasure methods are used to counter the missiles which includes active and passive methods. In Passive method the naturally emitted energy by the missile is detected whereas in the active detection method a radio signal is transmitted, and the backscattered signal is measured to find the range and the velocity of the target.
Design of Substrate Integrated Waveguide-based Metallic Strip Loaded Double Flared Horn Antenna

Analysis of the double-flared metallic strips loaded synthetic artificial waveguide-based electromagnetic horn antenna for X-band applications is discussed here. The complete design procedure and field analysis within the horn are also presented to better understand the proposed horn antenna. The analysis is also applicable for more number of flaring. The results in the form of reflection coefficients and radiation patterns in the operating band are presented to support the idea of a horn antenna using the SIW domain. The impedance bandwidth of 20.43 % (from 9.05-11.11 GHz) with respect to the centre frequency of 10.08 GHz is achieved. The gain performance of the antenna at all the frequencies is seen to be consistent (avg. 8.23 dB) with a highest value of 9 dB.
Performance of SWIPT enabled Full Duplex IoT Network with Hardware Impairments and Imperfect SIC

In this paper, we investigate the performance of a full-duplex (FD) Internet-of-Things (IoT) network with hardware impairments (HIs) over generalized α − μ fading channels, where IoT devices transfer their information to the access point via a simultaneous wireless information and power transfer (SWIPT) enabled relay node. The α − μ fading channel explores the non-linearity of the propagation medium and includes Nakagami-m, Rayleigh, exponential, one-sided Gaussian, and Weibull fading channels as special cases. For SWIPT enabling, we consider a power splitting (PS) receiver at the relay node. Further, we consider a practical scenario where the system suffers from residual self-interference (RSI) due to imperfect self-interference cancellation. We derive the closed-form expressions of outage probability (OP), asymptotic OP, system throughput, and energy efficiency over α − μ fading channels. The diversity order of the considered network at high SNR is obtained. An optimal value of the PS factor is obtained which depicts optimum outage performance. We highlight the impact of HIs, RSI, PS factor, and other system parameters on the performance of the considered network. Finally, Monte-Carlo simulations validate the derived closed-form expressions.
A Portable Base Station Assisted Localization with Grid Bias Elimination

Localization has always been one of the key issues for security applications. As security systems are turning more intelligent together with the development of smart information technologies, critical requirements for wireless target localization have challenged the traditional positioning techniques, including flexibility, portability, deployment cost, computational efficiency, and estimation accuracy, to name a few. Although the widely- accepted classical algorithm, MUltiple SIgnal Classification (MUSIC), has been proven to be an effective tool for the space-time estimation, it can hardly satisfy localization requirements under such security scenarios due to the high complexity and the bias error led by grid searching. Thus, in this paper, we propose a Joint Angle and Delay Estimation (JADE)-based localization algorithm using only one single portable base station, which eliminates the grid bias with low computational complexity. First, a MUSIC-based coarse JADE approach is proposed; then, a Taylor-series-based refinement method is introduced to eliminate the grid bias; and finally, the target mobile station is localized by the estimated time delay and angle information. The performance is evaluated by numerical simulations under various conditions, compared with five different existing algorithms. Our proposed MT-2D algorithm is proven to achieve a better estimation accuracy for the time delay, angle and position with a relatively low computational cost.
LSTM-based Path Selection for Successive Cancellation List Decoding for Short Polar Codes

Polar code is envisioned as a promising candidate for ultra-reliable low-latency communications (URLLC) in fifth-generation (5G) communication and beyond. To decode polar code, a successive cancellation list (SCL) decoder with a large list size can provide near maximum likelihood (ML) decoding performance. However, a large list size will lead to unacceptable spatial complexity, making it impractical. When the list size is small, although the complexity is low, its performance still needs to be improved. The main reason is that the sequence features implied in log-likelihood ratio (LLR) sequences are lost during calculating path metrics used for path selection. Because of the excellent sequence feature extraction ability of the long short-term memory (LSTM) network, we propose an LSTM-based path selection mechanism to replace the path metric-based path selection mechanism in SCL. In our proposed scheme, the LSTM network selects the surviving path according to the LLR sequences corresponding to the current paths. Simulation results show the effectiveness of the proposed LSTM-based path selection mechanism.
Weighted Coherent Detection of QCSP frames

A new efficient preamble-less short frame, called Quasi-Cyclic Short Packet (QCSP), has been recently proposed for the Internet of Things (IoT) deployment. It has demonstrated its good performances in the process of detection, synchronization, and error correction in an asynchronous Additive White Gaussian Noise (AWGN) channel, with residual frequency offset, and at a very low Signal-to-Noise Ratio (SNR). The detection process uses the normalized score of a non-coherent frame match filter to assess the presence of a frame or not. In this paper, we improve the detection performance by combining non-coherent detection with coherent detection. The proposed method decreases the probability of miss-detection by two decades, for a given probability of false alarm.
Forecasting growth and levels in loglinear unit root models

This paper considers unbiased prediction of growth and levels when data series are modelled as a random walk with drift and other exogenous factors after taking logs. We derive the unique unbiased predictors for growth and its variance. Derivation of level forecasts is more involved because the last observation enters the conditional expectation and is highly correlated with the parameter estimates, even asymptotically. This leads to conceptual questions regarding conditioning on endogenous variables and we prove that no conditionally unbiased forecast exists. We derive forecasts that are unconditionally unbiased and take into account estimation uncertainty, non-linearity of the transformations, and the correlation between the last observation and estimate which is quantitatively more important than estimation uncertainty and future disturbances together. The exact unbiased forecasts are shown to have lower MSFE than usual forecasts. We derive exact unbiased estimators of the MSFE and show that they can succesfully be used in the construction of forecast intervals. The results are applied to a disaggregated eight sector model of UK industrial production.
Do WiFi Probe-Requests Reveal Your Trajectory?

In this paper, we propose the first framework that introduces the concept of the user’s bounded trajectory. We propose to leverage the signal strength of users’ public WiFi probe requests collected from measurements of multiple deployed WiFi sniffers. First, we investigate and characterize errors in RSSI-based radial-distance (between the user and each sniffer) estimation. Then, we approximate such radial distances leverage and deduce bounds associated with a user’s position. Finally, we infer a user’s bounded trajectory using the spatiotemporal bounds of users’ locations over time. We guarantee the bounds to enclose a user in space and time, with 95% confidence and a 10% margin of error. Using real-world and large-scale synthetic datasets under heterogeneous contexts and wireless conditions, we infer trajectories with bounds’ width of less than 10m in 70% of cases with users’ inclusiveness close to 100%.
A Neural-Network-Based Controller for a Three- Phase Dual-Active-Bridge DC-DC Converter

Due to its many benefits, the Dual Active Bridge (DAB) DC-DC converter is always easy to find in micro grids applications, energy storage systems applications, vehicles to grid applications, and much more in the current energy architectures. Due to system changes and disturbances on both the input side and on the output side brought by the broad variety of applications, the DAB performs inadequately. The output voltage of the DAB is to be controlled and kept constant during system fluctuation with limited time response using a neural network based adaptive controller. The configuration of the proposed controller is identical to that of a PI controller. The study is done using MATLAB Simulink, where the system is tested under system variations. The proposed controller, a PI controller, and a combination of an AANN in parallel with a PI controller are all subjected to a performance test via time domain analysis. The results of the comparison between the three controllers favored the suggested controller.
Visual Path Odometry for Smart Autonomous E-Bikes

Recently, autonomous systems have been in rapid development. These systems are composed of various modules that enable automated navigation. The main generic modules consist of data acquisition, perceiving the environment, finding the optimal path and controlling the actuators. A core module of the automated vehicles is estimating where the vehicle is with respect to the path to be traversed, namely, vehicle localization. Localization has been applied through the past few years based on various sensors such as LiDARs, Cameras and GPS. Each sensor has its own strengths and weaknesses. For instance, Camera and LiDAR-Based traversed path estimation Odometry can function within GPS-denied environments. On the other hand, localization based on GPS can be used in environments where Cameras and LiDARs can fail to extract useful information, such as in a desert. For the case of automated vehicles, there is access to high power computers on board, which can help run complex and computationally expensive algorithms. However, for smaller platforms such as smart bikes, they are computationally limited, which can be a challenge especially for Visual Odometry Based algorithms. Given that there have been few researches exploring Monocular Visual Odometry (MVO) algorithms that are computationally efficient, this paper proposes the use of and enhancing a MVO module to estimate the path traversed by a smart bike platform in real-life. The obtained scores were compared to the output of the tested algorithm on a consumer grade PC to explore the trade-off between the gained speed up and reduced accuracy, the to validate the results.
PM-free electric motor powertrains in road and rail transport: an overview

The scarcity and the consequent price volatility of high-energy permanent magnets is becoming increasingly evident with the dramatic increase in electric and hybrid electric vehicle and rail powertrains and wind generators more extensive penetration. This overview paper summarizes a few key PM-free electric motor power trains R&D recent efforts concerning their design and control, with case-studies performance of practical interest for road and rail vehicle applications.
A 4.6K to 400K Functional PVT-Robust Ringamp-Based 250MS/s 12b Pipelined ADC with Pole-Aware Bias Calibration

Ring amplifiers (ringamps) have been shown to improve pipelined ADC performance by relaxing the traditional bottleneck imposed by power-hungry class-A residue amplifiers $[1,2]$. However, the operation of several high-performance ringamp structures is heavily dependent on device parameters and supply voltage, and thus, often sensitive to PVT variation. This creates challenges for applications in extreme environments, such as cryogenic quantum computing, space, and automotive. To overcome this issue, this work introduces a PVT-robust ringamp with pole-aware bias calibration and a biasenhancement technique. Furthermore, to deal with insufficient gain in advanced CMOS, we also propose a cascode Correlated Level Shifting (cascode-CLS) technique. These three techniques enable a prototype 12b 250MS/s pipelined ADC that achieves SNDR higher than $55 \mathrm{~dB}$ across $4.6 \mathrm{~K}$ to $400 \mathrm{~K}$ operating temperature range without requiring gain calibration. To the best of our knowledge, this work is the first pipelined ADC reported to operate at less than $40 \mathrm{~K}$.
Reinforcement Learning Aided Link Adaptation for Downlink NOMA Systems With Channel Imperfections

Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.
Alamouti Coded Asymmetrically Clipped Optical OFDM for Improved Performance of Multi-Mode Fiber

In this work, Alamouti coded asymmetrically clipped optical orthogonal frequency division multiplexing technique is investigated in a multimode fiber (MMF) transmission. Analysis is carried out on asymmetrically clipped O-OFDM and MMF models. The proposed technique finds significant improvement of bit error rate and received signal to noise ratio compared to conventional optical OFDM technique in MMF transmission.
Multi-AUV Task Scheduling for Target Hunting and Exploration: An AoI-Aware DMAPPO Approach

It is significant to design a task scheduling scheme for the multi-objective task of autonomous underwater vehicle (AUV) network for target hunting and environmental exploration. Due to the limited communication and detection conditions, it is difficult for each individual AUV in the network to accurately obtain all environmental information without a central control node. Therefore, most centralized scheduling schemes are infeasible to a fully distributed AUV network. To address the aforementioned issues, a distributed multi-agent proximal policy optimization (DMAPPO) scheme is proposed in this work, where AUVs are efficiently scheduled to achieve target hunting and environmental exploration. The distributed scheduling scheme is able to adjust the number of AUVs for each task according to practical requirement. In addition, we design an intra-network cooperative multi-AUV environmental exploration method by introducing the age of information (AoI). Simulation results validate that the proposed algorithm can achieve an effective task scheduling in the distributed AUV network.
Full-duplex Relay-assisted URLLC with Channel Estimation Error in a Factory Automation Scenario

This paper investigates the block error rate (BLER) performance and proposes an optimal power allocation scheme of a two-hop decode-and-forward full-duplex relaying system with imperfect channel estimation information in a factory automation scenario that requires ultra-reliable low-latency communication (URLLC). In the relay-assisted system, a central controller S sends signals to a remote device D with the aid of a robot R with channel estimation error (CEE). First, considering CEE at each receiver and the residual loop interference of the full-duplex relay, we derive an approximate closed-form expression of BLER under the Nakagami-m fading channel. Next, to minimize the overall BLER, we propose an optimal power allocation scheme between the controller and the robot with a total power constraint. Furthermore, we prove that BLERs of S-R and R-D links are strictly convex in terms of the transmit power of the controller. Simulation results illustrate the performance of the considered relay-assisted system and validate our theoretical analysis.
A geometry-based algorithm for TDoA-based localization in the presence of outliers

Localization of non-cooperative radio frequency transmitters in wireless networks can be performed by using Time Difference of Arrival (TDoA). However, TDoA measurements are often degraded by limited signal-to-noise ratio, multipath components, non-line-of-sight (NLOS) visibility, etc. This causes the TDoA measurements to have strong outliers, which cannot easily be modelled mathematically. This paper investigates algorithms to perform TDoA-based localization in the presence of outlier measurements. Our algorithm is based on a geometric interpretation, rather than a mathematical, noise model-based approach. We show that our algorithm outperforms existing algorithms when applied to real-world data in a vehicular scenario.
Design Procedure of a Misalignment Tolerant High-Efficiency Wireless Power Transfer System

Despite the potential advantages of Wireless Power Transfer Systems (WPT) compared to conventional conductive systems, complicated control structures and efficiency loss due to misalignment conditions become a major concern for adopting and wide-spread deployment of WPT systems. In the proposed work a simple design procedure is discussed based on the objectives of a simple control methodology and ensuring high-efficiency operation while Power regulation is guaranteed. The procedure is illustrated using analysis, an OCTAVE model, and a validated manufactured lab prototype.
Design Methodology of an Electrolytic Capacitorless LED Driver Using SEPIC PFC Converter

LED drivers are necessary for LEDs’ operation to control the LED’s current. This current control keeps the LED safe from the thermal runaway caused by the LED’s current increase above the maximum allowable value. The current control is also useful in controlling the output light. So, a reliable LED driver is highly required. The reliability of the LED drivers is strongly affected by the lifetime of the electrolytic capacitor which is small compared to the lifetime of the LED. As a result, in this paper, the electrolytic capacitor is replaced by a longlifetime film capacitor. This replacement is enabled using the SEPIC PFC converter operating in DCM III which enables controllability on the intermediate capacitor voltage. This controllability enables an optimized design for the intermediate capacitor to be a film capacitor and at the same time, the converter achieves an output light flicker within the IEEE Std 1789-2015 for flicker. The detailed design for the converter along with the simulation and experimental results is presented in this paper.