Miracle-dz/UniFAR-FTUs huggingface dataset:
Circuit Partitioning for PCB Netlist Based on Net Attributes

As we all know, the difficulty of automatic placement and routing is proportional to the size of the circuit. Through Printed circuit board (PCB) netlist partition algorithms, PCB circuits can be divided into different sub-modules, and the problem scale can be effectively reduced in order to obtain the optimal automatic layout and routing. It is observed that when engineers design circuits, they usually mark important nets by annotation, called net attributes. This paper proposes a PCB netlist partition approach based on net attributes. Our partition approach takes the netlist as input, and module partition set as output. Firstly, the modules are pre-partitioned using net attributes. Further, the special patterns in circuits are discovered, and the scattered resistors, capacitors and other components caused by pre-partitioning according to net attributes would be allocated to initial modules by classifying and module matching rules. Our method is evaluated on 11 PCB netlists, and experimental results show that our proposed netlist partition approach outperforms the state of the arts, which can achieve 80%–96% partition accuracy.
The Multistep Process of Coating PCL Membranes with MXene Solution

The electrospinning technique is a method for producing a variety of polymeric nanofibrous mats. Poly($\varepsilon$-caprolactone) (PCL) is one of the most suitable biodegradable and biocompatible biopolymers for electrospinning. PCL is used as a polymeric matrix to create nanocomposites by integrating micro-and nanomaterials to improve their mechanical and biological properties for tissue engineering purposes. MXene is a two-dimensional (2D) material with potential applications in biomedicine due to its attractive physicochemical properties and good hydrophilicity for biocompatibility. Hydrophilic surface functionalization strategy with H2SO4 provides a layer-by-layer assembly of MXene on electrospun PCL nanofibrous membranes. The research aimed to create hydrophilic biocompatible porous nanofibrous PCL mats by loading with MXene and estimating their morphology and biological properties. The EDS analysis identified elements” content on the membrane surface depending on the number of procedure repetitions. Overall, results show that MXenes can be immobilized onto the PCL nanofibers, slightly modifying their structural properties. Despite the coatings number, the resazurin reduction assay revealed continuous growth of cells in the presence of the PCL membranes with immobilized MXenes. MXene flakes covering the electrospun PCL membrane are not cytotoxic, potentially contributing to constructing scaffolds for biomedical applications.
A penalty scheme and policy iteration for nonlocal HJB variational inequalities with monotone drivers

We propose a class of numerical schemes for nonlocal HJB variational inequalities (HJBVIs) with monotone drivers. The solution and free boundary of the HJBVI are constructed from a sequence of penalized equations, for which a continuous dependence result is derived and the penalization error is estimated. The penalized equation is then discretized by a class of semi-implicit monotone approximations. We present a novel analysis technique for the well-posedness of the discrete equation, and demonstrate the convergence of the scheme, which subsequently gives a constructive proof for the existence of a solution to the penalized equation and variational inequality. We further propose an efficient iterative algorithm with local superlinear convergence for solving the discrete equation. Numerical experiments are presented for an optimal investment problem under ambiguity and a recursive consumption-portfolio allocation problem.
Wave Cellular Automata for Computing Applications

There is a continuous urge for higher efficiency in conventional computing systems, driven by an ever-growing demand for these systems’ complexity to be able to match the one of convoluted and challenging problems. However, this type of problems has formulated the benchmarks for unconventional computing systems to validate their emerging applicability and prove their effectiveness. Towards this path, Cellular Automata (CAs) have been established as a promising mathematical tool for simulating physical processes and demonstrated a favourable methodology for effectively implementing computations in hardware by taking advantage of their inherent parallelism. Representing CAs with oscillating memristive networks could further enhance the performance of these systems, by incorporating the rich dynamics evident in memristors and their strong memory and computing features. In this work, a wave generator circuit has been designed with low-voltage fabricated CBRAM devices, that is able to act as a Wave Cellular Automaton (WCA). These wave generation units are located on a grid with adjusting multi-directional interconnections between neighbors. In addition to that, the ability to reconFigure the amount of such units that influence each other, facilitates the propagation of voltage signals through the grid following wave propagation features. An example of this computational domain is presented with the realization of complex logic gates on the grid of WCAs.
Sustainability of Machine Learning-based Android Malware Detection Using API calls and Permissions

As the Android platform and malicious apps continue to evolve, most existing Android malware detection techniques using machine learning are turning out to be unsustainable. In this paper, we propose machine learning-based Android malware detection techniques which uses both API calls and permissions as a feature set. These features are complementary and are often used to detect malicious apps. We first analyze whether a ‘yearly dataset-based trained classifier’ (YDataC) is sustainable or not. The ‘yearly dataset-based trained classifier’ refers to the classifier that learns from 80% of the dataset of a specific year from 2014 to 2021, and is tested with 20% of the datasets of every year between 2014 and 2021. Through experiments, we discovered that the classification rate has dropped significantly since 2019, and something big has changed. Therefore, the ‘yearly dataset-based trained classifier’ is judged to be unsustainable. Next, we present and evaluate two incremental learning methods for gradual training: an incrementally trained Random Forest (RF) and an incrementally trained Neural Network (NN). Evaluation results show that two incremental learning classifiers have better sustainability than the ‘yearly dataset-based trained classifier’. The incrementally trained RF has better sustainability than the incrementally trained NN in terms of given metrics such as $f_{1}\ score$ and AUT (Area under Time).
A Hybrid CMOS Photonic 25Gbps Microring Transmitter with a -0.5–1.2V Direct-Coupled Drive

Microring modulators integrated in silicon photonic technology platform have evolved to offer much higher energy-efficiency than the conventional Mach Zehnder modulators. This allows for lower drive voltages in the transmitter and compact layout footprint. However, the nonlinear response of the depletion-mode resonant device incurs unequal optical rise and fall times. In this work, we present a transmitter design for a microring based optical interconnects that provides a $1.7 V_{pp}$ swing while avoiding AC-coupling. The direct-coupled driver realized a universal NRZ transmitter with a small footprint. The transmitter is designed in a 65nm LP CMOS technology with 1.2V supply voltage and achieves 1.85 pJ/bit energy-efficiency at 25Gbps data rate, $\gt 8$ dB extinction ratio and 0.075mm2 area.
A Residual Network Model (ResNet152) for Bronchitis Detection using X-ray Images

Every year 5 Lakhs of people affects from bronchitis and it rate crores of rupees to the public treasury for the industry of health care. It also affects cancer causing agents like diseases which can also lead to death of a human being. Thus diagnosing at the early stage becomes successful detection, prevention and survival of that diseased patient. The work at present where doctors predict by observing the symptoms or observing the x rays of the patients then the doctor used to percept about the disease. The purpose of the model is to build a model used to classify whether the patient is suffering from bronchitis or normal lung by x-ray images. A pre-trained Residual network model (ResNet152) is used for predicting early bronchitis and classifying the images by labeling them.
Equilibrium of fluid membranes with tangent-plane order (TPO), elasticity of smectics with TPO, and dispiration asymmetry in smectics-C*

Fluid membranes endowed with tangent-plane order (TPO) such as tilt- and hexatic order afford unique soft matter systems for investigating the interplay between elasticity, shape, topology, and thermal fluctuations. Using the spin-connection formulation of membrane energy we obtain equa- tions of equilibrium together with free boundary conditions for ground states of such membranes. We extend the spin-connection formulation to smectic liquid crystals with TPO and show that for chiral smectics-C* this generalization leads to experimentally verifiable consequences for dispirations having topological indices (helicities) of the same magnitude but opposite signs.
From physical principles to relativistic classical Hamiltonian and Lagrangian particle mechanics

We show that classical particle mechanics (Hamiltonian and Lagrangian consistent with relativistic electromagnetism) can be derived from three fundamental assumptions: infinite reducibility, deterministic and reversible evolution, and kinematic equivalence. The core idea is that deterministic and reversible systems preserve the cardinality of a set of states, which puts considerable constraints on the equations of motion. This perspective links different concepts from different branches of math and physics (e.g. cardinality of a set, cotangent bundle for phase space, Hamiltonian flow, locally Minkowskian space-time manifold), providing new insights. The derivation strives to use definitions and mathematical concepts compatible with future extensions to field theories and quantum mechanics.
An 8T/Cell FeFET-Based Nonvolatile SRAM with Improved Density and Sub-fJ Backup and Restore Energy

In normally-off instant-on applications, power-gating of the embedded memory is an effective way for higher power efficiency by preventing long-standby-time leakage energy. Recent efforts of nonvolatile SRAM (nvSRAM) design with in-cell NVM element backup provide an efficient way for both normal-mode computing and off-mode backup and restore (B&R) operations. For these efforts, circuit innovations are required to achieve optimal balance between B&R energy and area overheads. In this paper, we report a novel 8T/cell FeFET-based nvSRAM design that outperforms prior FeFET-based designs with higher density, while still maintaining the advantage of only sub-fJ energy for each B&R operation, 363x lower than the existing RRAM-based nvSRAM design. Compared with prior FeFET-based designs, this design reduces the B&R transistor count per cell from 4 to only 2, which leads to a significant total area overhead reduction of 11%.
Cyclotron Resonance Energies and Orbital Elements of Accretion Pulsar 4U 0115+63 During the Giant Outburst in 2008

We present both timing and spectral analysis of the outburst of 4U 0115+63 in April -- May 2008 with INTEGRAL and RXTE observations. We have determined the spin period of the neutron star at $\sim 3.61430 \pm 0.00003$ s, and a spin up rate during the outburst of $\dot{P}=(-7.24 \pm 0.03)\times10^{-6} {\rm s d^{-1}}$, the angle of periapsis $\omega=48.67^\circ \pm 0.04^\circ$ in 2008 and its variation (apsidal motion) $\dot{\omega} = 0.048^\circ \pm 0.003^\circ {\rm yr}^{-1}$. We also confirm the relation of spin-up torque versus luminosity in this source during the giant outburst. The hard X-ray spectral properties of 4U 0115+63 during the outburst are studied with INTEGRAL and RXTE. Four cyclotron absorption lines are detected using the spectra from combined data of IBIS and JEM-X aboard INTEGRAL in the energy range of 3 -- 100 keV. The 5 -- 50 keV luminosities at an assumed distance of 7 kpc are determined to be in the range of $(1.5-12)\times 10^{37} {\rm ergs s^{-1}}$ during the outburst. The fundamental absorption line energy varies during the outburst: around 15 keV during the rising phase, and transiting to $\sim 10$ keV during the peak of the outburst, and further coming back to $\sim 15$ keV during the decreasing phase. The variations of photon index show the correlation with the fundamental line energy changes: the source becomes harder around the peak of the outburst and softer in both rising and decreasing phases. This correlation and transition processes during the outburst need further studies in both observations and theoretical work. The known relation of the fundamental line energy and X-ray luminosity is confirmed by our results, however, our discoveries suggest that some other factors besides luminosity play the important role in fundamental line energy variations and spectral transitions.
A Type-3 FMCW Radar Synthesizer with Wide Frequency Modulation Bandwidth

This work presents a 5-GHz frequency-modulated continuous wave signal generator (FMCW). The presented circuit aims to generate a wider frequency modulation bandwidth at low center frequency that grants significant advantage at saving power. Utilizing a fractional- N phase-locked loop (PLL)-based synthesizer with a 50-MHz reference frequency as the FMCW generator, the synthesizer modulates the carrier frequency across a range of 700 MHz. A Type-3 architecture with a switchable polarity is embedded to improve the linearity around the chirp turning-around points (TAPs) and switching-band points (SBPs). Switching Band Control (SBC) circuit and Multi-varactor LC Voltage Control Oscillator (MV-VCO) are proposed as an efficient method to increase the modulation range so that the FMCW signal generator can achieve 21.43-cm resolution in distance detecting. Fabricated in a TSMC 40-nm 1P10M CMOS technology, the proposed generator consumes 3.3 mW power and occupies 0.497mm2 die area. The measured root-mean-square (rms) frequency error of the generated triangle chirp over 1.15ms period is 625kHz.
A 0.73-to-1.71 V Capacitor-less Low-Noise Low-Dropout Regulator in 28-nm CMOS

This paper presents a low-noise low-dropout regulator (LNLDO) implemented in a 28 nm technology. The LNLDO features a two-stage architecture, using a low-pass filter with a very low cut-off frequency to separate the regulation loop and the noisy devices such as resistive divider and bandgap reference. Supplied by a 1.5 V-1.8 V input voltage, the LNLDO is able to provide a stable output voltage ranging from 0.73 V to 1.71 V, with a current supply ability from 0 to 35 mA. An on-chip capacitance enhancer is used to stabilize the regulation loop without an additional external capacitor. The RMS noise from 10 Hz to 100 kHz of the LNLDO is below 20μV.
Impacts of B-factory measurements on determination of fragmentation functions from electron-positron annihilation data

Fragmentation functions are determined for the pion and kaon by global analyses of charged-hadron production data in electron-positron annihilation. Accurate measurements were reported by the Belle and BaBar collaborations for the fragmentation functions at the center-of-mass energies of 10.52 GeV and 10.54 GeV, respectively, at the KEK and SLAC B factories, whereas other available $e^+e^-$ measurements were mostly done at higher energies, mainly at the $Z$ mass of 91.2 GeV. There is a possibility that gluon fragmentation functions, as well as quark fragmentation functions, are accurately determined by scaling violation. We report our global analysis of the fragmentation functions especially to show impacts of the B-factory measurements on the fragmentation function determination. Our results indicate that the fragmentation functions are determined more accurately not only by the scaling violation but also by high-statistical nature of the Belle and BaBar data. However, there are some tensions between the Belle and BaBar data in comparison with previous measurements. We also explain how the flavor dependence of quark fragmentation functions and the gluon function are separated by using measurements at different $Q^2$ values. In particular, the electric and weak changes are different depending on the quark type, so that a light-quark flavor separation also became possible in principle due to the precise data at both $\sqrt{s}\simeq 10.5$ GeV and 91.2 GeV.
Air Pollution Prediction Using Machine Learning Algorithms: A Literature Review

Air pollution continues to be a problem that affects us all worldwide, since it is estimated that 7 million people die each year due to repeated exposure to pollutants that cause chronic conditions such as severe respiratory diseases, cardiovascular problems and cancer. On the other hand, climatological effects lead to the deterioration of the planet’s ecosystems. Therefore, air quality monitoring systems are the main tools used by governments to control the emission of toxic gases into the atmosphere. This makes it possible to ensure the quality of life and the general well-being of the population, as well as to strengthen the agricultural and industrial sectors. The objective of this work is to present a review of the characteristics present in the mechanisms applied in the prediction of air pollution, with the main objective of synthesizing the knowledge found, identifying the models, approaches and variables that are most studied. The results show that the hybrid models based on CNN-LTSM are the most used. Other studies use GRU and ELM that have good results when making predictions. Suspended particles (PM10 and PM2.5) are the main object of study. Multivariate models are also more accurate and efficient when it comes to forecasting.
A Nanopower 95.6% Efficiency Voltage Regulator with Adaptive Supply-Switching for Energy Harvesting Applications

A nanopower highly efficient low-dropout (LDO) regulator for energy harvesting (EH) applications is presented in this paper. The LDO is fully autonomous with a bandgap reference (BGR) featuring a novel bandgap supply-switching (SS) topology, an over-voltage protection (OVP), a under-voltage lockout (UVLO) and control block to obtain stable output and robust cold-start. The system provides configurable voltage supply (1.1 $\sim$2V) for potential loads, while consuming as low as 66 nW power. The entire system achieves a peak power efficiency of 95.6% at Vout=2V and $I_{\iota_{oad}}$=100$\mu$A.
Adaptive Distributed Queuing Random Access Protocol for LoRa based IoT Networks

The Internet of Things is the idea of connecting any device to the Internet and to other connected devices. There are several wireless communication technologies in IoT depending upon the area in which they are employed. For device simplicity, Low Power Wide Area Network (LPWAN) tend to use a simple channel access control protocol such as Aloha, which impacts performance. While several LPWAN technologies are available in the field of IoT, focus on the Long Range (LoRa) network is still ongoing. Distributed Queuing LoRa (DQ-LoRa) algorithm has been used in the MAC layer of LoRa with fixed number of slots in its contention window for improving the performance of LoRaWAN. Depending upon the number of slots, either maximum throughput or minimum energy consumption could be achieved. The proposed work namely Adaptive DQ-LoRa (ADQ-LoRa) hovers over the dynamic slots mechanism for channel access control protocol in conjugation with the physical layer of LoRa. With every frame, the number of slots change depending upon the collisions and idle slots in the previous frame. With respect to DQ-LoRa protocol, which has the best performance, ADQ-LoRa gives 22% reduction in average delay and increases throughput by 12.5%. It has been shown to work better when nodes are more than 2000 in number, resulting in 4% decrease in energy consumption.
A 266-3750 MHz Wide-Range Adaptive Phase-Rotator-Based All Digital DLL for LPDDR5 Controllers

In this paper, an all-digital delay-locked loop (ADDLL) with an adaptive phase rotator (PR) is proposed to meet the wide operating range in the LPDDR5 controllers. The conventional lattice-delay-unit (LDU)-based delay line is replaced by the PR-based digitally controlled delay line (DCDL) to achieve a wide operating range with a low jitter accumulation and low power dissipation. For the proper operation of the PR-based DCDL with high linearity, the current strength of the PR should be set to the optimum value for each operating frequency. The proposed ADDLL adaptively controls the current strength of the PR by detecting the output swing for each frequency. The proposed ADDLL is fabricated in a 28-nm CMOS technology occupying an active area of 0.0043 mm2. It operates from 266 MHz to 3750 MHz for the LPDDR5 interfaces. The measured jitterrms is 2.06 ps, and the jitter$_{pk-pk}$ is 13.2 ps at 3750 MHz. The proposed ADDLL consumes 4.2 mW at 3750 MHz.
A Cross Regulation Reduced Multi-Output and Multi-VCR Piezoelectric Energy Harvesting System Using Shared Capacitors

This paper presents a triple-output piezoelectric energy harvesting (PEH) system with parallel synchronized switch harvesting on capacitors (P-SSHC) rectifier based on shared capacitors. By analyzing the principle of the P-SSHC rectifier, this paper supports the rationality of using shared capacitors to construct the multi-voltage conversion ratio (VCR) switch capacitor (SC) DC-DC converters. In addition, the adoption of a parallel structure in the multi-output SC DC-DC converters reduces the cross-regulation. Simulation results show that the proposed system not only has good input power adaptability (1/3X,1X,2X) but can also provide triple voltage (0.5V, 1V, 2V) with less cross-regulation. The maximum output power is 14. 4μW when peak-to-peak open circuit voltage is 1.7V.
High-Density Digital RRAM-based Memory with Bit-line Compute Capability

The AI revolution shows the ever increasing performance demands of AI applications like Deep Neural Networks DNNs which consist of tens of layers and do computations on tens of millions of data weights [1]. Conventional Von Neumann architectures are currently struggling to meet these emerging performance demands with deep memory hierarchies to bridge the processor-memory performance gap [2]. Emerging technologies (like RRAMs) have meanwhile shown a real promise to address the increasing challenges of the conventional computing technology. While the main direction of research is focussing on exploiting the analogue memory attributes of RRAMs specially for analogue computing crossbars [3], this paper focuses on a different perspective of building high-density and digital-friendly RRAM-based memory that is a good alternative to the SRAM-based Last-Level Caches LLCs. This digital RRAM-based memory with conventional 1T1R bit-cells is proposed to be an on-chip gigantic data reservoir, with much higher density than SRAMs, to bridge the memory gap. The paper also shows that the digital RRAM-based memory is capable of doing robust bit-line compute which opens the door for digital in-memory computing architectures that can mitigate the Von Neumann bottleneck while adopting RRAM’s high-density promise. Unlike analogue RRAM crossbars, RRAMs’ digital in-memory computing capability should inherit the large scalability and the fast time-to-market of the digital domain with less engineering effort for optimisation as there is no need any more to build DACs and ADCs.