Miracle-dz/UniFAR-FTUs huggingface dataset:
A Metamodel Residual-based Stopping Criterion for Adaptive Verification of Integrated Circuits

Adaptive verification is gaining increasing use as solution to overcome the coverage problem of complex integrated circuits' verification. As its name suggests, it places more points in regions of interest by learning the information from previous data, offering great potential to the engineer to understand the behavior of the system under study and to identify interesting regions in the design space. Since it is an iterative task, it should be stopped at the point that is optimum or near-optimum. This paper proposes a stopping criterion for planning adaptive experiments (simulations/measurements) for the identification of extreme regions (minimum/maximum) of the response. It is based on the concept of metamodeling and helps to efficiently characterize the targeted extreme region of complex integrated circuits with the advantage of low experimental effort. The approach was evaluated on synthetic test functions and an analog integrated circuit. The stopping criterion shows substantial improvements in the number of simulations/measurements while maintaining high resolution in the region of interest.
Analysis of information perception and processing during long-term and intense cognitive load using combined EEG and NIRS

This work is devoted to the analysis of information perception and processing during long-term and intense cognitive load using combined EEG + NIRS. We consider changes in the reaction time during long-term and intense cognitive load and a comparison of brain activity with behavioral characteristics. We found the establishment of a characteristic mode in the brain corresponding to a routine cognitive task.
Characterization of Reverberation Chamber - A Comprehensive Review

This study presents a comprehensive review of the characterization techniques of a reverberation chamber (RC). After describing the limitations of the theoretical models for the determination of the lowest usable frequency (LUF) of a chamber, the normative procedure of IEC 61000-4-21 standard is summarized for the estimation of LUF of an RC. Then a brief overview of the recent approaches for the full-wave EM modeling and characterization of EM fields in an RC using statistical tests is presented. The study also discusses the limitations of conventional field uniformity based RC calibration procedure with a review of the recently proposed fast indirect validation procedure of RC using <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-parameters measurements. Lastly, this work reviews the estimation of the RC quality factor (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factor) using analytical, time-and frequency-domain techniques and their limitations.
Deep Reinforcement Learning for Caching Placement and Content Delivery in UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) cellular network is investigated in this article, where the massive access capability is enhanced by applying non-orthogonal multiple access (NOMA). More particularly, a mobile UAV base station, which caches the popular contents to release the pressure on wireless backhaul links, is deployed to assist the delivery of large volume multimedia contents for ground users. The dynamic UAV cellular network with the dynamic UAV locations and content requests in practical scenario is considered in this paper. A long-term caching placement and content delivery joint optimization problem for content delivery delay minimization is formulated as a Markov decision process (MDP) to cope with the dynamic environment. A deep reinforcement learning (DRL) based caching placement and content delivery algorithm is proposed to tackle the MDP with large action space. Finally, it is demonstrated by the numerical results that: 1) a low content delivery delay is achieved by the studied cache-enabling UAV NOMA networks; 2) a good performance is provided by the proposed algorithm.
High Gain Modular Resonant DC/DC Converter for HVDC-Flexible System in Offshore Wind Farm

The flexible HVDC power transmission system (HVDC-Flexible) used in offshore wind farms is an effective solution to eliminate the bulky power frequency transformers in traditional HVAC grid. The DC converter is one of the key equipment of the HVDC-Flexible system. This paper presents a modular resonant DC/DC converter which achieves high voltage gain with resonant circuits and voltage doubling rectifier circuits. Its output voltage is regulated by variable frequency control, and the resonant currents in the resonant circuit module are balanced via adjusting the duties of switches. Finally, a 1150V/10kV, 500kW converter module is simulated in MATLAB/Simulink. The simulation results validate the theoretical analysis of the proposed resonant DC/DC converter.
Smart Lock Security System Based on Artificial Internet of Things

Artificial intelligence and Internet of Things (AIoT) technology were applied to develop an integrated system for remote space authentification and power management. The system is applied to various commercial uses such as the control of mobile or cloud authorization in buildings and the management of shared offices or hospitals. To develop the proposed system, AIoT smart lock with low power consumption was designed to control the access and manage the power consumption of other IoT devices effectively in the venue. AIoT communication firmware and devices were developed to enable smart control and management. Finally, an energy management system helps enterprises monitor the power consumption of devices in real-time and grant authorization for device monitoring and management. The proposed system sends monitoring data to a cloud server and enables the managing of commercial activities and unmanned venues. Through hardware and software integration, this study created the AIoT smart lock, promoted the use of IoT devices in relevant industries, and enhanced the competitiveness of product research and development.
An Approach for Preventing and Detecting Attacks in the Cloud

Preventing and detecting attacks in the cloud are difficult tasks involving technical, financial, and legal challenges. Baseline security solutions from cloud providers are often inadequate to secure cloud instances properly. In addition, entry-level cloud instances offer few resources, as little as 512MB of RAM, and particular actions are either costly or limited by cloud providers, hindering the operation of commercial security solutions, such as antivirus software, and intrusion detection and prevention (IDP) systems. State-of-the-art research IDP systems have made great progress using machine and deep learning but they encounter certain limitations when operating in the cloud. We introduce Xshield, a lightweight IDP framework designed for the cloud, that consists of a limited number of Producers constantly gathering malicious information, analyzing it through one or more arbitrary intrusion detection and/or prevention strategies and passing the processed information along to Consumers, an IDP agent on cloud customers’ instances. We implement and evaluate a Producer prototype by deploying 138 Producers on a cloud provider across 15 regions for seven days and use the collected information to demonstrate how a limited number but strategically placed Producers are capable of protecting cloud customers’ instances as well as present insights on attacker behavior in the cloud. We then discuss, based on attacker behavior insights, what kind of existing IDP strategies can be adapted to operate on Producers.
Ch’i: Scaling Microkernel Capabilities in Cache-Incoherent Systems

Hardware cache coherence limits the scalability of shared-memory multicore and multi-processors. Recently, there has been an increasing shift towards cache-incoherent architectures in many computing environments. Supercomputers can benefit from shared memory, yet must avoid the price of coherency across the system. To support co-locating multiple applications on cores, processors, and the overall system, an Operating System must manage the distributed memory resources. In this context, incoherence poses a significant challenge for OS that must manage memory access permissions across the system without compromising the performance of software.In this paper, we introduce the Ch’i microkernel that leverages incoherent shared memory using quiescence-based techniques to bound the extent of non-coherency. Ch’i maintains type and context consistency of its core data structures: capabilities. This enables a uniform, capability-based security model to manage system-wide memory. Using this approach, we demonstrate data-structure consistency in software in support of high-throughput, low-latency applications.
Microswitch in 0.13 μm CMOS Process for IoT Device Transceivers

The rapid proliferation of the network of internet of things (IoT) have led to a significant demand for low-cost stand-alone communication devices which can currently be realised with CMOS RF integrated circuits. Therefore, it has become essential to design a compact microswitch to share the antenna at its transceiver frontend. This research proposes a compact microswitch design utilising a 0.13 μm CMOS process. The switch uses transistor width optimisation, gate bias resistance minimisation, resistive body floating technique and DC biasing at the input as well as the output nodes. The proposed switch achieved 0.465 dB insertion loss, 39.02 dB isolation and 18.11 dBm power handling capacity at an operating frequency of 2.4 GHz. Moreover, the core of the microswitch occupies only 0.0005 mm2 of silicon area without passive elements. Therefore, it is evident from our research that the microswitch is well suited for low power IoT transceiver applications operated at the widely used ISM (industrial, scientific and medical) 2.4 GHz band.
High PSR output-capacitor-less LDO with double buffers technique

An output-capacitorless LDO regulator with double buffers technique has been proposed and simulated in a commercial 0. 18µm CMOS process in this paper. To get over the tradeoff between PSR against other important design parameters such as loop stability and load capability, the proposed LDO replicates a supply ripple to the gate of pass transistor using a PMOS buffer with diode connected transistor and extends the load range by an NMOS buffer. Simulation results verify that PSR is improved effectively in the whole unity gain frequency (UGF) and the improvement is up to 40dB. In addition, the quiescent current is 33µA, providing a maximum load current of 50 mA. Besides, excellent load regulation of 2µ V/mA and line regulation of 0.06mV/V are obtained with supply from 1.2V to 2V.
Control of a centrifugal pump as a turbine in an energy recovery system

In this paper, the control of an energy recovery system is analyzed. The energy recovery system is built with a pump working as a turbine (PAT), an asynchronous generator, and a four-quadrants converter that connects the system to the electrical network. This system regulates the water pressure at the output of the PAT and, because of the drop in pressure, it injects the recovered energy in the electrical grid. Furthermore, a controller is designed for the system shaping the dynamic response of the system using a Bessel polynomial and practical considerations. The design of the controller is validated through simulations.
Auxiliary Pulse-Extender and Current-Attenuator Circuits for Flexible Interaction with Memristive Crossbars in SNNs

This paper presents a pulse-extender, a delay-element, and a current-attenuator as auxiliary circuits that make it possible to have flexible interaction with memristor crossbars in spiking neural networks. In the presynaptic part, the pulse-extender makes the inputs compatible with the pulsed-characterization of memristors. In the post-synaptic part, the current attenuator relaxes the system in terms of requiring low-offset amplifiers and also makes it possible to design neurons with small membrane capacitors. The circuits are fabricated in a CMOS 180nm technology. The measurements verify that these blocks play an important role in reaching an SNN with real-time performance.
Underdetermined Blind Source Separation in Reverberant Environment

This work studies a blind source separation method for convolutive mixtures in an underdetermined case. Due to strong reverberation, the mixing model still presents convolution property even in the time-frequency (TF) domain. Based on the convolutive model in TF domain, a blind source separation is developed, and the resulting optimization problem is solved via an alternating framework where the alternating direction method of multipliers (ADMM) is developed. Experimental results show that the proposed method outperforms other methods in strong reverberant environment.
Heuristic Approach for Identification of Random TSV Defects in 3D IC During Pre-bond Testing

The possibility of 3D integrated circuit (3D IC) has been considered as a choice to overcome the difficulties faced by two-dimensional integrated circuits (2D IC). Several technologies exist to connect the layers in 3D IC. Among these technologies through-silicon vias (TSVs) is the promising one since it helps to reduce interconnect length, delays and power consumption. In spite of the advantages it introduces different types of defects which ultimately make an entire IC faulty. Thus, testing of TSVs is an important necessity. Depending on the timing, the testing may be of two types -pre-bond and post-bond. In pre-bond TSV testing TSVs are tested in sessions. In this paper our objective is to reduce the pre-bond TSV test sessions as much as possible, so that overall testing time decreases. To reduce test sessions we need to reduce the individual TSV testing and increase group wise TSV testing.
Sensor Fusion Module Using IMU and GPS Sensors For Autonomous Car

Autonomous vehicle employ multiple sensors and algorithms to analyze data streams from the sensors to accurately interpret the surroundings. The aim of the research presented in this paper is to design a sensor fusion algorithm that predicts the next state of the position and orientation of Autonomous vehicle based on data fusion of IMU and GPS. This is essential to achieve the highest safety and security standards through enhanced performance that enable consistency, accuracy and reliability. Traditionally, for vehicle navigation, GPS standalone solutions have been utilized which lack to scan the surrounding environment. Therefore, the data from an Inertial Measurement unit (IMU) is fused and correlated with GPS measurements to overcome the limitations of GPS based navigation system.
Symbiotic Communication: Concurrent Transmission for Multi-Users Based on Backscatter Communication

As backscattering technology has become more widely used in the Internet of Things, how to correctly recover multiple reflected signals has become a hot research in the field of reverse communication. In the background of backscatter communication (BackCom), this paper proposes a symbiotic communication system for multi-user random access. By using a simple coding algorithm that does not require strict synchronization, we propose a method to avoid interference between multiple reflected signals. Passive users use mutually orthogonal chips to encode, so that the interference between the reflected signals is the orthogonal interference, which achieving the parallel transmission for multiple users. Meanwhile, this study designs the coding and decoding algorithms to achieve two orthogonal chips, which are used with different chip lengths and different chip transmission rate. We compares with the parallel transmission scheme based on code division multiple access (CDMA). After the verification of orthogonal interference among multiple reflected signals, we make the simulation analysis of the random access multi-user symbiotic system. The simulation results verify the feasibility and effects of the proposed algorithm.
Wireless Technology for Autonomous Albanian Farming and Crop Monitoring

The adoption of Internet of Things (IoT) has made possible remote networking of a diverse and broad spectrum of applications from the microscopic to the industrial scale. Agricultural science is rapidly evolving along with the need to apply more cost-effective techniques and equipment that would make this science more productive and efficient. The deployment of wireless technologies would be an essential factor that will facilitate small farm holders to reach these goals in the farming ecosystem, such as arable land usage, asset tracking and management, natural environment and crop monitoring. This paper surveys the technology and recommends the selected products with their full costing to implement a small-scale wireless monitored autonomous farming system in Albania.
Performance Trade-offs in GPU Communication: A Study of Host and Device-initiated Approaches

Network communication on GPU-based systems is a significant roadblock for many applications with small but frequent messaging requirements. One common question for application developers is, "How can they reduce the overheads and achieve the best communication performance on GPUs? " This work examines device initiated versus host initiated inter-node GPU communication using NVSHMEM. We derive basic communication model parameters for single message and batched communication before validating our model against distributed GEMM benchmarks. We use our model to estimate performance benefits for applications transitioning from CPUs to GPUS for fixed-size and scaled workloads and provide general guidelines for reducing communication overheads. Our findings show that the host-initiated approach generally outperforms the device-initiated approach for the system evaluated.
Characterization of Single Event Transient Effects in Standard Delay Cells

Standard delay cells (SDCs) are commonly used for timing synchronization in digital designs. Due to skewed transistor sizing, the SETs induced in SDCs may be stretched during propagation through the cell, thus increasing the soft error rate. In this work, the simulation analysis of Single Event Transient (SET) effects in 130 nm SDCs is presented. The results have shown that the SETs induced in SDCs may be up to 1.46 ns wide, which is more than twice the width of SETs in other standard logic cells, for the same simulation settings. As the conventional gate-level SET mitigation techniques are not effective against long SETs, we have investigated two hardening solutions for SDCs: (i) complete duplication with a guard gate, and (ii) partial duplication with a guard gate applied only to the most sensitive inverters in the SDC. Both solutions can reduce significantly the generated SET pulse width, with the delay overhead of less than 250 ps. The estimated area overhead per cell is around 115 % for complete duplication and 80 % for partial duplication.
A Unique Design of Hybrid Full Adder for the Application of Low Power VLSI Circuits

In this research work, a unique Hybrid Full Adder (HFA) is developed by deploying Pass Transistor Logic (PTL), CMOS logic and transmission gate (TG) logic on the Cadence Virtuoso platform in 90-nm technology. Various modules, namely the XOR module, the carry generator module, sum generator module are implemented for realizing 1-bit HFA. An inverter logic is employed next to the XOR logic to obtain the logic of XNOR which is required for designing the proposed HFA. The propagation delay (tpd) and average power of the circuit are turned out to be 20 ns and ~6.889 μW respectively, at 1V supply voltage. So, the power delay product (PDP) is remarkably low with the value 137.78 fJ of the proposed HFA. The area is also satisfyingly less because the proposed design used only 13 transistors. Hence, the proposed design gives a remarkable improvement in terms of PDP which may be applicable for basic building blocks of VLSI circuits.
Analysis of Variations in AMSR2 Cold Brightness Temperature over Five Years

The AMSR2 cold brightness temperatures were systematically assessed and analyzed over the past five years. The theoretical minimal $\mathrm{T}_{B}\mathrm{s}$ were simulated by the radiative transfer model for AMSR2 channels. The monthly histograms were composed of the cold measurement occurrences that $\mathrm{T}_{B}\mathrm{s}$ are falling within ±10K of the modeled minimums. The monthly statistical minimums $\mathrm{T}_{Bmin}$ were extrapolated by the histograms, which were individually fitted with third-order polynomials. The trend of extrapolated $\mathrm{T}_{Bmin}$ in accordance with variation of AMSR2 cold measurements, presented weak periodicity due to cycled variations of SST and WV at all AMSR2 channel except 23.8V and 36.5H channels. The lowest $\mathrm{T}_{B}$ in July 2015 was significantly lower than that in other months at the 36.5H channel.
Concerto: Leveraging Ensembles for Timely, Accurate Model Training Over Voluminous Datasets

As data volumes increase, there is a pressing need to make sense of the data in a timely fashion. Voluminous datasets are often multidimensional with individual data points representing a vector of features. Data scientists fit models to the data — using all features or a subset thereof — and then use these models to inform their understanding of phenomena or make predictions. The performance of these analytical models is assessed based on their accuracy and ability to generalize on unseen data. Several frameworks exist for drawing insights from voluminous datasets, but have limited scalability (which leads to prolonged training times), poor resource utilization, narrow applicability across problem domains, and insufficient support for combining diverse model fitting algorithms.In this study, we describe our methodology for scalable supervised learning over voluminous datasets. The methodology explores the effect of controlled partitioning of the feature space, as well as how analytical models can be combined to preserve accuracy. Rather than build a single, all-encompassing model, we enable practitioners to construct an ensemble of models that are trained independently in parallel over different portions of the data space. This can provide faster training times and increased prediction accuracy overall; our empirical benchmarks demonstrate the suitability of our approach using real-world data.
Using Artificial Intelligence to Predict Fall-risk During Adaptive Locomotion in Humans

Falls are the third leading cause of unintentional injuries for ages 18–35 years according to the CDC; although there are many studies for falls in the elderly population, the causes and circumstances of falls for younger adults are understudied. The primary objective of this research is to develop a predictive fall indicator using machine learning for this poorly studied population of fallers that are of high risk of injury or death. This work is anchored by a novel locomotion dataset due to the population studied, young adults. This dataset is composed of 88 participants tracked over 150 runs. In this work, Gradient Boosting algorithm yielded the best prediction rates.