Miracle-dz/UniFAR-FTUs huggingface dataset:
A Time-Domain Temperature Sensor using Nwell Diodes on 820µm^2 in 7nm FinFET

We present a novel concept for thermal sensing, which provides a duty-cycle modulated output signal for high resolution measurement. The circuit is self-contained and features distinct simplicity, containing only a few switches, capacitors and an auto-zero comparator. The active Nwell-Substrate diode is utilized as a robust sensing device in advanced FinFET nodes. Two sampling capacitors operate as a charge-pump and discharge periodically across the diode. The sampled voltages represent PTAT and CTAT signals, which are balanced by means of adjusting the diode current through pulse timings. This control pulses are generated in a feedback loop by the comparator and a counter, while the temperature is extracted from their duty-cycle. Our prototype in 7nm FinFET consumes only 820µm2 active area, due to low matching requirements. From simulated data, the sensor operates down to Vdd = 0.90V and achieves an untrimmed accuracy of ±2.8°C from -10°C to 120°C. It dissipates ~11µW including an internal clock generator, with a conversion time between 2. 5µs and 600µs.
A Model-Based Testing Approach for Cockpit Display Systems of Avionics

Avionics are highly critical systems that require extensive testing governed by international safety standards. Cockpit Display Systems (CDS) are an essential component of modern aircraft cockpits and display information from the user application (UA) using various widgets. A significant step in the testing of avionics is to evaluate whether these CDS are displaying the correct information. A common industrial practice is to manually test the information on these CDS by taking the aircraft into different scenarios during the simulation. Such testing is required very frequently and at various changes in the avionics. Given the large number of scenarios to test, manual testing of such behavior is a laborious activity. In this paper, we propose a model-based strategy for automated testing of the information displayed on CDS. Our testing approach focuses on evaluating that the information from the user applications is being displayed correctly on the CDS. For this purpose, we develop a profile for capturing the details of different widgets of the display screens using models. The profile is based on the ARINC 661 standard for Cockpit Display Systems. The expected behavior of the CDS visible on the screens of the aircraft is captured using constraints written in Object Constraint Language. We apply our approach on an industrial case study of a Primary Flight Display (PFD) developed for an aircraft. Our results showed that the proposed approach is able to automatically identify faults in the simulation of PFD. Based on the results, it is concluded that the proposed approach is useful in finding display faults on avionics CDS.
Low-Rank Approximation via the Generalized Reweighted Iterative Nuclear Norm

low-rank approximation problem has recently attracted wide concern due to its excellent performance in realworld applications such as image restoration, traffic monitoring, and face recognition. Compared with the classic nuclear norm, the Schatten-p norm is stated to be a closer approximation to restrain the singular values for practical applications in the real world. However, Schatten-p norm minimization is a challenging non-convex, non-smooth, and non-Lipschitz problem. In this paper, inspired by the reweighted $\ell_{1}$ norm for compressive sensing, the generalized iterative reweighted nuclear norm (GIRNN) algorithm is proposed to approximate Schatten-p norm minimization. By involving the proposed algorithms, the problem becomes more tractable and the closed solutions are derived from the iteratively reweighted subproblems. Numerical experiments for the practical matrix completion (MC) problem and robust principal component analysis (RPCA) problem are illustrated to validate the superior performance of both algorithms over some common state-of-the-art methods.
A 0.98-nW/kHz 33-kHz Fully Integrated Subthreshold-Region Operation RC Oscillator With Forward-Body-Biasing

The lower bound on the power expended by an RC relaxation oscillator is decided by the RC network. This can be minimized by reducing the oscillation swing and increasing R. In the former technique, tighter comparator constraints limit power benefits while the latter technique increases resistor thermal noise bounding long-term jitter. To this end, this letter presents a fully integrated RC oscillator with core voltage aggressively scaled to subthreshold levels. A self-clocked switched-capacitor network is used to minimize voltage drop-out power loss. Full forward-body-biasing technique helps reduce device on-resistance. Additionally, temperature coefficient compensation for time constant is accomplished by poly resistors and a VTH-tracking reference scheme which avoids the use of diffusion resistors. This design is silicon-proven on 65-nm CMOS (0.0356-mm2 area). The implementation has a 33-kHz clock with 32.2 nW at 1.2 V. Line sensitivity is within +0.7/−0.6% per volt across 16 samples for 1 to 1.5 V. Temperature sensitivity was measured to be 56 ppm/°C from 0 °C to 85 °C and measured Allan deviation <100 ppm for averaging interval of τ = 400 s and <40 ppm for τ = 3000 s.
A Computationally Efficient Solution Algorithm for Least Absolute Value State Estimation Problem

The most common methods used to solve the state estimation problem are the Weighted Least Squares (WLS) and the Least Absolute Value (LAV) algorithms. WLS is computationally efficient but it is not robust to outliers. On the other hand, LAV is robust to outliers as long as they are not present in leverage points but it is computationally demanding. This paper presents a new LAV-based algorithm that is fast and robust. The LAV problem is formulated as an unconstrained non-linear optimization problem that can be solved using gradient-based approaches. The motivation is to combine the desirable bad data rejection properties of LAV with the computational efficiency of WLS. The proposed algorithm is compared with the traditional WLS and LAV algorithms in terms of computational time and robustness using test cases of various sizes, from 30 to over 13000 buses. The proposed LAV algorithm is shown to be faster than the traditional LAV while possessing the same bad data handling properties.
Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Magnetic resonance (MR) images have distinctive advantages in radiation treatment (RT) planning due to their superior, anatomic and functional information compared with computed tomography (CT). For the RT dose calculation, MR images cannot be directly used because of the lack of electron density information. To address this issue, we propose to generate pseudo-CT (pCT) in terms of multiple matching Dixon MR images to support MR-only RT, particularly in the challenging body section of the abdomen. To this end, we design the dedicated multichannel residual conditional generative adversarial network (MCRCGAN). The significance of our efforts is three-fold: 1) The MCRCGAN organically incorporates multiple theories and techniques, such as multichannel residual network (ResNet) and conditional generative adversarial network (cGAN), which facilitate its more authentic pCT generation than many existing methods. 2) The usage of residual modules effectively deepens the network without performance degradation, and the multichannel ResNet helps to simultaneously capture the substance of images, as extensively as possible, which is implicitly contained in the multiple different MR images of the same subject. 3) Due to the designed dedicated network structure, the MCRCGAN is capable of generating satisfactory pCTs under the condition of limited training data as well as prompt prediction response. Experimental studies on ten patients’ paired MR-CT images demonstrate the effectiveness of our proposed MCRCGAN model on both the pCT generation quality and the performance stability.
Robust Self-Localization of Microphone Arrays Using a Minimum Number of Acoustic Sources

Multi-microphone signal processing is becoming increasingly popular in applications such as distant speech recognition or communication in adverse environments. To deploy source localization or signal enhancement algorithms like beamforming the locations of the microphones must be known. One well-studied approach to retrieve the relative positions of the microphones is based on time-difference-of-arrival (TDoA) measurements. However, current approaches are restricted to scenarios with a large number of sources or specific coherence assumptions. In this paper a non-iterative approach based on orthogonal geometric projection (OGP), which is able to perform a blind self-localization of the array in 2D with only two sources at arbitrary positions, is presented and extended to estimate a 3D array shape with only three sources. Furthermore, an efficient method for outlier correction in the pairwise distance (PD) estimates is proposed, that significantly reduces the position error.
Visualizing and Quantifying Thoraco-Abdominal Asynchrony in Children From Motion Point Clouds: A Pilot Study

In a situation of respiratory failure (RF), patients show signs of increased work of breathing leading to the involvement of accessory respiratory muscles and a desynchronization between rib cage and abdomen known as thoraco-abdominal asynchrony (TAA). Proper assessment of these signs requires sufficiently skilled and trained medical staff. However, human assessment is subjective and is practically impossible to audit. A new non-contact method is proposed for TAA visualization and quantification, in children with RF. The surface variations are analyzed by calculating the 3-dimensional motion of the thorax and abdomen regions during the breathing process. A high-fidelity mannequin was used to simulate thoraco-abdominal asynchrony. The proposed system uses depth information recorded by an RGB-D (Red Green Blue-Depth) camera. Furthermore, surface displacement was calculated in four simulated modes from the normal to the severe TAA mode. Respiratory rates were also calculated based on the analysis of the surface movements. The proposed method was compared to a highly precise laser-ranging system with 1mm resolution. The resulting root mean square deviation (RMSD) showed an error of 1.78 ml in normal mode, 2.83 mm in mild mode, 2.23 mm in severe mode and 2.34 mm in irregular mode. The results showed a high correlation between the two methods in estimating the retraction distance and respiratory rate ( $\rho >0.985$ ).
Mono-static Automotive Joint Radar-Communications System

The paper investigates the applicability of a conventional communications signal for sensing and detection in vehicular scenario. This Joint Radar-Communications (JRC) system caters to an automotive setting characterized by a multi-target environment including clutter. In this contribution, an automotive radar employing phase modulated communication signal is considered wherein the detection of other vehicles is enabled in presence of clutter. Particularly, it is shown when the clutter is dominant in the environment, bit error rate (BER) of communications will suffer. Further, the receiver processing includes demodulation of the communication symbols at communicating vehicle in addition to the extraction of the radar parameters − range and Doppler shifts of the targets − at the JRC-equipped vehicle. The study investigates the impact of various parameters in determining the performance of the JRC set-up.
Solution of the Capacitated Vehicle Routing Problem using Variable Neighborhood Search with Threshold

The capacitated vehicle routing problem (CVRP) arises in daily logistics problem. Here a set of customers are served by a number of similar vehicles originated from a depot. The aim is to find the shortest routes to serve the customers. An adaptation of the variable neighborhood search (VNS) is used to solve the CVRP. In the algorithm, a threshold is applied in the shaking procedure. A number of neighborhood and local searches are used in the searching process. In the local search, the first improvement search strategy is used. Five instances from literature are used to test the proposed algorithm. The solutions produced are good, as the average deviation obtained by the proposed algorithm is just 0.04% less than the average deviation of the recently published CVRP paper.
Web Metrics for the Next Generation Performance Enhancing Proxies

The fast growth of Internet traffic, the growing importance of cellular accesses and the escalating competition between content providers and network operators result in a growing interest in improving network performance and user experience. In terms of network transport, different solutions ranging from tuning TCP to installing middleboxes are applied. It turns out, however, that the practical results sometimes are disappointing and we believe that poor testing is one of the reasons for this. Indeed, many cases in the literature limit testing to the simple and rare use case of a single file download, while common and complex use cases like web browsing often are ignored or modelled only by considering smaller files. To facilitate better testing, we present a broader view which includes domain counts, flow counts and different forms of concurrency to better characterise the complexity around web pages, and from which the potential for different optimisation techniques can be estimated. We also derive numerical values of these metrics for a small set of popular web pages and study similarities and differences between pages with the same kind of content (newspapers, e-commerce and video) and between pages designed for the same platform (computer and smartphone).
Open Innovation from Chesbrough to now: where do we stand today?: An exploratory review of Open Innovation Model

Open Innovation (OI) has become one of the most popular concepts in Industry and Academia. Over the past decade, an outstanding body of knowledge (BOK) has been accumulated about OI and nowadays, it is one of the most reviewed topic in the field of “Innovation Management”. The concept has swiftly progressed from a restricted circle of high-tech practitioners, to a widely discussed and implemented concept in different industries. The community of researchers have made it an established research field. The primary goal of this article is to explore the concept recent developments in light of new research findings and industry best practices. Thus, we start by analyzing the model of OI, commonly contrasted to “Closed Model”, then we attempt to categorize OI and to analyze strengths and limitations of each class type. Lastly, we address the crucial issue of OI processes, we attempt to assess recent models, to discuss implementation issues and to select best practices. This paper aims to build on past and present studies to provide future directions, also, limitations are presented to point out issues that remain insufficiently documented and require further academic researches. The main theories and authors (practitioners and academics) who deal with OI have been highlighted.
Towards efficient production of digital climatic maps for the Campania Region

gPRISM is an efficient software for the production of digital climatic maps from point measurements according to a well-known climatic regression model called PRISM. To overcome performance and scalability issues, gPRISM exploits the GPU computing power with a highly parallel implementation, favoured by the nature of the PRISM algorithm. This paper discusses the application of the current version of gPRISM to the Campania Region. The presented evaluation shows that gPRISM can be applied to larger areas and to finer time scales, confirming the validity of the GPU-based approach.
Energy Separation Algorithm Based Spectrum Estimation for Very Short Duration of Speech

In this paper, we propose a novel method of estimation of short-time spectrum for analysis of speech signals in the closed phase regions of glottal activity. This method uses Teager Energy Operator (TEO) and a related Energy Separation Algorithm (ESA) iteratively, along with the design of digital resonator to estimate formants from a very short duration of the speech. The spectrum of cascade of these four resonators is referred to as our proposed ESA spectrum of speech. The novelty of the proposed approach lies in using very short duration of analysis speech frame that is synchronized with glottal closure instant (i.e., about 1-2 ms) to estimate the proposed spectrum in order to ensure that the vocal tract system characteristics do not change much within this interval and to alleviate erroneous estimation of formants due to nonlinear interaction of excitation source with the vocal tract system. To demonstrate the effectiveness of proposed algorithm for formant estimation on speech data, we have used 1.5 ms speech signal corresponding to closed phase glottal cycles derived from a male speaker of CMU-ARCTIC database.
Optimal Placement and Settings of FACTS Devices for Reactive Power Compensation Using a Firefly Algorithm

In this paper, the firefly algorithm with a modified parameterless penalty function is used to determine the optimal settings of Flexible AC Transmission System (FACTS) devices including the Thyristor controlled phase shifting transformer TCPST, Thyristor controlled series capacitor (TCSC) and Static VAR Compensator (SVC). Use of FACTS in a power system reduces the active power losses and improves the voltage profile. The proposed algorithm solves an optimal power flow (OPF) problem for the system without FACTS devices as an initial step to determine the optimal settings for the generators' voltages and active powers and to calculate the initial active power losses. The optimal locations for FACTS devices are heuristically chosen by ranking the lines and the buses of the power system according to the reduction in the power losses. Finally, the optimal settings of the FACTS devices are selected to minimize the active power losses and to enhance the voltage profile. The proposed algorithm is tested on the IEEE 30-bus and the IEEE 57-bus power systems and gives high quality solutions in a relatively small computational time.
Inter-System Interference Reduction in RadCom Systems - Filter Bank Multicarrier Radar

In this paper, we evaluate the application of Filter Bank Multicarrier (FBMC) for radar operations to mitigate inter-system interference in combined radar/communications (RadCom) systems, one limiting factor of the radar’s performance. This analysis is verified with simulations and real measurements at 24 GHz for a scenario with one user and another with multiple users. To the authors’ knowledge, this paper presents, for the first time, the validation of QAM-FBMC radar with real measurements. The results show that FBMC is less affected by inter-system interference than OFDM.
Effect of Sink Node Position on Performance in Direct Diffusion Based Sensor Networks

Wireless sensor networks are networks that multiple sensor nodes form among themselves without the need for any communication infrastructure. It is thought that wireless sensor networks will be used in many fields, especially in the defense industry in the future. The processor, ram and battery life of the sensor nodes is very limited. The sensor nodes become inoperable when the batteries run out. Therefore, there are many studies in the literature aimed at minimizing energy consumption. The life of the sensor nodes is directly proportional to the batteries. The sensor nodes transmit the data they collect from the medium to the central unit called sink by means of the wireless network they establish among themselves. There are many methods in the literature that allow the nodes forming wireless sensor networks to communicate with the sink node. Direct diffusion method is the basis of these methods. In this article, the effect of the number and position of the sink node on the total performance of the network is investigated in direct diffusion-based sensor networks. The sensor network environment is divided into three categories: discrete, normal and dense. Experimental studies were performed for each category. NS2 was used as the simulation medium. Obtained results; energy consumption, packet delivery rate, end-to-end delay and network routing load criteria.
Significance of Pitch-Based Spectral Normalization for Children's Speech Recognition

It is well known from the literature that due to several acoustic mismatches, the recognition performances of children's speech using adult-trained-acoustic models get deteriorated. The differences in pitch and speaking rate are the two major factors that cause the acoustic mismatch between two groups of speakers. This work proposes to incorporate pitch information into an automatic speech recognition (ASR) system by exploiting the correlation between pitch and formants. By using the pitch-based spectrum normalization module in the front-end feature extraction process, the performance of mismatch ASR system is improved for children of different age groups. Further, fuzzy-based time scale modification is applied to study the effect of speaking-rate normalization on the proposed feature. The proposed feature results in relative improvement of <inline-formula><tex-math notation="LaTeX">$\text{30}{\%}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{33}{\%}$</tex-math></inline-formula> on DLSTM-based ASR system over the MFCC baseline without and with speaking-rate normalization, respectively.
From Speech to Facial Activity: Towards Cross-modal Sequence-to-Sequence Attention Networks

Multimodal data sources offer the possibility to capture and model interactions between modalities, leading to an improved understanding of underlying relationships. In this regard, the work presented in this paper explores the relationship between facial muscle movements and speech signals. Specifically, we explore the efficacy of different sequence-to-sequence neural network architectures for the task of predicting Facial Action Coding System Action Units (AUs) from one of two acoustic feature representations extracted from speech signals, namely the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPs) or the Interspeech Computational Paralinguistics Challenge features set (ComParE). Furthermore, these architectures were enhanced by two different attention mechanisms (intra- and inter-attention) and various state-of-the-art network settings to improve prediction performance. Results indicate that a sequence-to-sequence model with inter-attention can achieve on average an Unweighted Average Recall (UAR) of 65.9 % for AU onset, 67.8 % for AU apex (both eGeMAPs), 79.7 % for AU offset and 65.3 % for AU occurrence (both ComParE) detection over all AUs.
LTE Ranging Measurement Using Uplink Opportunistic Signals and the SAGE algorithm

With the increase of services that need accurate location of the user, new techniques that cooperate with the Global Navigation Satellite System (GNSS) are necessary. GNSS suffers poor performance in indoor and dense urban environments, due to high signal attenuation and severe multipath propagation. The current release of the 3rd Generation Partnership Project(3GPP) LTE specification, supports the Uplink Time Difference Of Arrival (UTDOA) localization technique, which uses as reference the Sounding Reference Signal (SRS). Local Measurement Units (LMUs) devices use knowledge of the SRS to perform time difference measures. This paper studies the possibility of performing radio localization using a new UTDOA technique that exploits the uplink Demodulation Reference Signal (DM-RS) in 4G Long Term Evolution (LTE) cellular networks. We point out the advantages of our proposal and evaluate its feasibility by measuring the distance between two antennas using real DM-RS signals generated by an LTE module.