Small Input Research Articles

A 250 MS/s 65.8-dB SNDR SAR ADC With Cross-Phase Common-Mode Feedback and Reconfigurable Inverter-Based Preamplifier in 28-nm CMOS

This brief presents a 250 MS/s 12b successive-approximation-register (SAR) analog-to-digital converter (ADC) in 28 nm CMOS. The inverter-based preamplifier is introduced to suppress comparator noise. To improve the ADC robustness and speed, first, a cross-phase common mode feedback which explores the common mode voltage during the sampling phase, is proposed to address the PVT and input common mode voltage sensitivity of the inverter-based preamplifier. Second, a reconfigurable method is proposed to eliminate the long recovery time of the preamplifier at large input signals, as well as reduce the kickback noise at small input signals. Third, a PMOS pair with deliberate timing is introduced to accelerate the comparator regeneration without deteriorating its noise. The ADC employs 360 fF/side sampling capacitance and takes 2 ns conversion time to resolve 15 raw bits. Fabricated in 28 nm CMOS, the prototype ADC achieves 65.8 dB SNDR and 80.9 dB SFDR for a near-Nyquist input, occupies an active area of 0.01 mm2 and consumes a power of 4.7 mW, resulting in a Walden FOMW of 11.8 fJ/conv-step and a Schreier <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {FOM}}_{{\boldsymbol{S}}}$ </tex-math></inline-formula> of 170.0 dB, respectively. Last but not least, the ADC attains a rail-to-rail input CM range.

(Integral)ISS of Switched and Time-Varying Impulsive Systems Based on Global State Weak Linearization

It is shown that impulsive systems of nonlinear, time-varying, and/or switched form that allow a stable global state weak linearization are jointly input-to-state stable (ISS) under small inputs and integral ISS. The system is said to allow a global state weak linearization if its flow and jump equations can be written as a (time-varying, switched) linear part plus a (nonlinear) perturbation satisfying a bound of affine form on the state. This bound reduces to a linear form under zero input but does not force the system to be linear under zero input. The given results generalize and extend previously existing ones in many directions: 1) no (dwell-time or other) constraints are placed on the impulse time sequence; 2) the system needs not be linear under zero input; 3) existence of a (common) Lyapunov function is not required; and 4) the perturbation bound needs not be linear on the input.

Optimal Dynamic and Steady-State Performance of PV-Interfaced Converters Using Adaptive Observers

The voltage regulation of a grid-tied PV system is constantly perturbed for the maximum power point tracking (MPPT), the transients of which determine the dynamic MPPT efficiency. Meanwhile, the PV-link voltage ripple coupled from the DC to single-phase AC power conversion adversely affects the static MPPT efficiency. Thus, optimal dynamic and steady-state performance is necessary to maximize the solar energy harvest. This brief proposes a novel adaptive mechanism preserving the advantages of two observers: the classical extended state observer and reduced-order generalized proportional integral observer. Simulation and experimental results prove the enhanced performance regarding the overshoot-free fast transient response and steady state against disturbances, even with small input capacitance. Meanwhile, the observer switching happens seamlessly through the proposed mechanism, which is confirmed analytically and experimentally.

A 16-Bit 120 MS/s Pipelined ADC Using a Multi-Level Dither Technique

In wireless applications, such as radars, tens of MHz signals need to be quantized using an analog-to-digital converter (ADC) with a large dynamic range. The detected signal amplitude can be random, with a small or large amplitude. In addition, the dynamic performance is degraded by capacitor mismatches. A 16-bit 120 MS/s pipelined ADC implemented in a 180 nm complementary metal–oxide–semiconductor (CMOS) process is presented in this work. We propose a multi-level dither technique that can significantly enhance the ADC linearity. The injected dither also helps improve the linearity when the ADC handles an input signal with a small amplitude. Traditional dither injection leads to an increase in the amplifier output swing. A counteracting dither injection scheme, both in sub-flash ADC and the multiplying digital-to-analog converter (MDAC), is proposed to remedy this issue. Moreover, capacitor mismatches in the first three pipeline stages are calibrated in a foreground way. The inter-stage residue gain accuracy is guaranteed by a gain-boosting amplifier. To demonstrate the effectiveness of the dither scheme, we obtained the dynamic performance of the ADC with a small input signal (−12 dBFS). The proposed calibration and dither injection technique improved the spurious-free dynamic range (SFDR) from 77 dBc to 85 dBc with −12 dBFS input. With −1 dBFS input, the SFDR remained at over 85 dBc, reaching up to the Nyquist input frequency. Therefore, the dither scheme enhances the dynamic performance when the ADC handles a signal with small amplitude.

A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces

Visual complexity is an important factor affecting the efficiency and functionality of user interfaces. Its impact on the user's impression and the usability is significant, especially for mobile applications with constraints such as layout size, on screen keys and small input fields. Conventional approaches for visual complexity evaluation of user interfaces are either based on user evaluations with surveys or based on pre-specified formal metrics or on heuristics. Alternatively, in this study, we have explored the effectiveness of deep learning models for visual complexity evaluation, specifically, of mobile user interfaces. We have experimented with five state of the art pre-trained deep learning models known to be effective for computer vision tasks, namely, VGG16, DenseNet121, MobileNetv2, GoogleNet and ResNet152 were trained with 3635 different mobile user interface images as login, menu, search and settings. Furthermore, in order to validate the effectiveness of this approach, a new validation dataset and survey application was developed and an evaluation study was conducted with 98 participants where 7309 comparison result were obtained from the study. It was found that the agreement rate between the results of deep learning models and the user evaluations was up to 78% and 74% on the average. The high to moderate agreement rate between the results of deep learning models and the user evaluations reveals that this approach can be useful for designers in visual complexity evaluation of mobile user interfaces.

The Nodewalk assay to quantitate chromatin fiber interactomes in very small cell populations.

The chromosome conformation capture method and its derivatives, such as circularized chromosome conformation capture, carbon copy chromosome conformation capture, high-throughput chromosome conformation capture and capture high-throughput chromosome conformation capture, have pioneered our understanding of the principles of chromosome folding in the nucleus. These technical advances, however, cannot precisely quantitate interaction frequency in very small input samples. Here we describe a protocol for the Nodewalk assay, which is based on converting chromosome conformation capture DNA samples to RNA and subsequently to cDNA using strategically placed primers. This pipeline enables the quantitative analyses of chromatin fiber interactions without compromising its sensitivity down to <300 cells, making it suitable for MiSeq analyses of higher-order chromatin structures in biopsies, circulating tumor cells and transitional cell states, for example. Importantly, the quality of the Nodewalk sample can be assessed before sequencing to avoid unnecessary costs. Moreover, it enables analyses from hundreds of different restriction enzyme fragment viewpoints within the same initial small input sample to uncover complex, genome-wide networks. Following optimization of the different steps, the entire protocol can be completed within 2 weeks.

An interleaved non‐isolated high gain soft switching DC–DC converter with small input current ripple

AbstractThis article suggests a non‐isolated high step‐up interleaved DC–DC topology for renewable energy applications. Two three‐winding coupled‐inductors along with two voltage multiplier cells are used to enhance the voltage gain significantly. One interesting aspect of this new topology is that high duty cycles are not required to achieve high voltage gains; this is done by adjusting the turn ratio of the coupled inductors. Moreover, the presented interleaved converter enjoys not only having less current stress due to the interleaving structure, but also showing 16% increase in voltage gain compared to the best similar boost topologies. Additional elements are designed for the purpose of soft switching and gain increase, which have little effect on the cost, weight and size of the proposed converter. Interestingly, the voltage stresses both on the switching devices and passive elements are much less than the output voltage. Furthermore, the coupled inductor's leakage inductance energy is recovered by employing a passive clamp circuit to minimize stresses across the semiconductors; therefore, efficiency is improved. The suggested converter is analyzed in the steady state, and a 400 W experimental setup was implemented; the outcomes verify the practicality of the proposed boost converter in accordance with the analysis.

Efficient Prediction of Fuel Cell Performance Using Global Modeling Method

A global modeling method is developed to describe the relationship between multi-type parameters and fuel cell performance, which significantly contributes to the efficient performance prediction of fuel cell systems. The multi-type parameters include operating parameters, geometric parameters of the graphite end plates, and the membrane electrolyte assembly physical parameters. An adaptive sampling method integrated with the Kriging method is newly developed for global modeling. Experiments are designed and implemented for model construction and evaluation. The results show the local development and global development in the whole design space can be balanced during the adaptive sampling process. Meanwhile, the prediction capability of accuracy and sensitivity for the global model is reliable in the whole design space. The prediction accuracy is improved by nearly 26% compared to the fuel cell model built for optimization with the same sample size. The prediction sensitivity also proved that the global model could follow the experimental variations under small input deviations.

Solar charging of a Zn-air battery

Solar-charging of Zn-air batteries has been studied by employing a photoelectrocatalytic or a photovoltaic system. Discharging of a Zn-air battery corresponds to oxidation of Zn and formation of ZnO. Charging is the reverse action, i.e., the reduction of ZnO and deposition of metallic Zn on the Zn electrode. In a typical Zn-air battery, charging also corresponds to water oxidation on the counter electrode and oxygen evolution. Because of the high overpotential for the oxygen evolution reaction, charging of the battery necessitates very high voltages, much higher than the voltage produced by the battery. This problem can be overcome by substituting oxygen evolution reaction by photocatalytic oxidation of an organic substance, which may be a biomass waste. This photoelectrocatalytic charging was presently realized with 100% Faradaic efficiency. More than 2 V of power gain was achieved through photoelectrocatalytic charging, i.e., a small input voltage was then necessary in order to charge the battery. Battery charging was also possible by using a photovoltaic cell as long as the cell provides enough open circuit voltage to overcome battery potential, overpotentials and losses. In the present case charging was possible with a commercial photovoltaic cell generating 2.25 V at an incident light intensity of 100 mW cm−2.

RANCANG BANGUN PERANGKAP HAMA SERANGGA MENGGUNAKAN SENSOR PASSIVE INFRARED RECEIVER DAN SENSOR ULTRASONIK BERBASIS MIKROKONTROLER ESP32

Designing automatic midges for rice plants, in addition to trapping and repelling insect pests, can alsokill pests that always interfere with the growth of rice plants automatically based on microcontrollers. In thisdesign, PIR, ultrasonic sensors, and ESP32 are used as control systems. Ultrasonic sensors are used to emitultrasonic waves as a pest disruptor that wants to get close to rice plants. The PIR sensor itself is set to detectpests approaching plants and then react to the ultrasonic sensor to emit waves to disturb pests that perch on riceplants. The PIR sensor will work by capturing the thermal energy possessed by insect pests in front of the sensor. This is where the Arduino plays a role as a controller, which will carry out the execution. The use of this electric racket module is to give a sting to pests if the pests caught directly perch on the wire surrounding the lamp, where the wire has been supplied with a high electrical voltage even with a small electrical voltage input. In addition, the use of the module here is useful for killing pests that perch on traps if pests are not disturbed by waves emitted by ultrasonic sensors. The test results show that the performance of the designed system can function properly. The designed system can detect objects in the form of insect pests and can accurately display data to the 16x2 LCD.

Mixture 2D Convolutions for 3D Medical Image Segmentation.

Three-dimensional (3D) medical image segmentation plays a crucial role in medical care applications. Although various two-dimensional (2D) and 3D neural network models have been applied to 3D medical image segmentation and achieved impressive results, a trade-off remains between efficiency and accuracy. To address this issue, a novel mixture convolutional network (MixConvNet) is proposed, in which traditional 2D/3D convolutional blocks are replaced with novel MixConv blocks. In the MixConv block, 3D convolution is decomposed into a mixture of 2D convolutions from different views. Therefore, the MixConv block fully utilizes the advantages of 2D convolution and maintains the learning ability of 3D convolution. It acts as 3D convolutions and thus can process volumetric input directly and learn intra-slice features, which are absent in the traditional 2D convolutional block. By contrast, the proposed MixConv block only contains 2D convolutions; hence, it has significantly fewer trainable parameters and less computation budget than a block containing 3D convolutions. Furthermore, the proposed MixConvNet is pre-trained with small input patches and fine-tuned with large input patches to improve segmentation performance further. In experiments on the Decathlon Heart dataset and Sliver07 dataset, the proposed MixConvNet outperformed the state-of-the-art methods such as UNet3D, VNet, and nnUnet.

Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles.

This paper presents the development and evaluation of neural network models using a small input-output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water.

Defect-rich covalently-crosslinked UiO-66(Zr)-NH2/PVC adsorption ultrafiltration membrane for effective phosphate ions removal from water

BackgroundPhosphorus in wastewater is a major environmental concern. Very small phosphorus inputs in the environment can cause algal blooms that can deplete the dissolved oxygen and decrease the water quality, leading to eutrophication. Furthermore, some algal blooms produce toxins and bacterial growth that can cause human illness through exposure to contaminated water. Therefore, It is necessary to remove phosphorus before it is discharged to the environment. Methodsadsorbent enhanced adsorptive ultrafiltration (UF) has recently emerged as an advanced ion recovery technology with membrane filtration function and adsorption function, and is very effective in removing cationic bisphosphate and anionic phosphate. Significant findingsHerein, a novel defect-rich and covalently-crosslinked octanoic acid (OA)-UiO-66(Zr)-NH2/PVC adsorption ultrafiltration membrane are designed for removal of phosphate ions in water. The OA-UiO-66-NH2/PVC-33 membrane achieved the adsorption capacity (∼30 mgg−1) and higher removal efficiency (∼90%). The OA-UiO-66(Zr)-NH2/PVC ultrafiltration membrane exhibited a higher water flux of 266.9 L/(m2·h·bar) compared to those of pristine PVC and UiO-66-NH2/PVC. Filtration and cyclic stability experiments reveal that OA-UiO-66(Zr)-NH2/PVC membrane can maintain a high retention capacity and exhibit excellent regeneration performance after 5 cycles.

Compact antenna integrated with a Greinacher voltage multiplier for ambient energy harvesting applications

Radio frequency (RF) energy harvesting from freely available electromagnetic fields is a promising alternative for powering smart electronic devices. This article presents a compact and efficient rectenna for RF energy harvesting at an Industrial, Scientific, and Medical (ISM) band of 2.4 GHz. A compact microstrip antenna with opened-end slots and a full ground plane is designed. Moreover, a Greinacher voltage doubler topology-based rectifier was designed to sense small input power levels. The antenna and rectifier optimization were performed through the simulation software. The optimized prototypes were fabricated and measured individually and as well as an integrated system. Measured results were in good correlation with the simulations. A peak measured efficiency of 75% with a constant output voltage of 2.9 V was achieved for the proposed rectifier at a small input power of 10 dBm. Furthermore, for demonstration of the RF conversion efficiency and output voltage, the antenna was integrated with the rectifier of load resistance, which was later replaced by LED for WPT demonstration purposes. The demonstration verified that the proposed rectenna with achieved efficiency and sufficient output voltage is a promising candidate for powering smart electronic devices.

Travelling salesman problem modelling by mixed integer linear programming of python (MIP)

A famous travelling salesman problem, appearing simple to state but complex to solve, has been widely investigated and various algorithms have been proposed. In this article, mixed integer linear programming of python (MIP) is used to model this problem with varying input data. The result shows that with small input data the modelling code of MIP executing quickly and converging to optimal value, while large scale input data require plenty of computation time; thereby algorithm improvement as well as parallel implementation are suggested.

Air–Sea Interactions on Titan: Effect of Radiative Transfer on the Lake Evaporation and Atmospheric Circulation

Titan’s northern high latitudes host many large hydrocarbon lakes. Like water lakes on Earth, Titan’s lakes are constantly subject to evaporation. This process strongly affects the atmospheric methane abundance, the atmospheric temperature, the lake mixed layer temperature, and the local wind circulation. In this work we use a 2D atmospheric mesoscale model coupled to a slab lake model to investigate the effect of solar and infrared radiation on the exchange of energy and methane between Titan’s lakes and atmosphere. The magnitude of solar radiation reaching the surface of Titan through its thick atmosphere is only a few watts per square meter. However, we find that this small energy input is important and is comparable in absolute magnitude to the latent and sensible heat fluxes, as suggested in a study by Rafkin & Soto (2020). The implementation of a gray radiative scheme in the model confirms the importance of radiation when studying lakes at the surface of Titan. Solar and infrared radiation change the energy balance of the system leading to an enhancement of the methane evaporation rate, an increase of the equilibrium lake temperature almost completely determined by its environment (humidity, insolation, and background wind), and a strengthening of the local sea breeze, which undergoes diurnal variations. The sea breeze efficiently transports methane vapor horizontally, from the lake to the land, and vertically due to rising motion along the sea breeze front and due to radiation-induced turbulence over the land.

A 6-Bit 20 GS/s Time-Interleaved Two-Step Flash ADC in 40 nm CMOS

A 6-bit 20 GS/s 16-channel time-interleaved (TI) analog-to-digital converter (ADC) using a two-step flash ADC with a sample-and-hold (S/H) sharing technique and a gain-boosted voltage-to-time converter (VTC) is presented for high-speed wireline communication systems. By sharing one S/H between coarse and fine stages in the two-step flash ADC, the input bandwidth as well as area and power efficiency can be improved without a gain error between coarse and fine ADCs. Thanks to an eight-time interpolation using the gain-boosted VTC, the fine ADC has a small gate capacitance without a speed penalty, even in a small input voltage range. A prototype ADC implemented in a 40 nm CMOS process occupies a 0.1 mm2 active area. The measured differential non-linearity (DNL) and integral non-linearity (INL) after offset and gain calibrations were 0.45 and 0.39 least significant bit (LSB), respectively. With a 9.042 GHz input, the measured signal-to-noise and distortion ratio (SNDR) and the spurious-free dynamic range (SFDR) were 30.12 and 40.23 dB, respectively. The small input capacitance of the sub-ADC enables a power-efficient track-and-hold amplifier (THA), resulting in a power consumption of 56.2 mW under a supply voltage of 0.9 V. The prototype ADC achieves a figure of merit (FoM) of 107.4 fJ/conversion-step at 20 GS/s.

Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs

Data-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of sample data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an example. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.

A sorting algorithm based on ordered block insertions

Among the comparison-based algorithms, insertionsort is recognized as one of the fastest methods to sort relatively small data sets, or when the elements are relatively ordered. However, due to not offering good asymptotic complexity in its runtime, it performs very poorly both in the worst case and in the average case for most large data collections. In this article we offer a new sorting algorithm based on orderer block insertions with worst case optimal time. At the cost of an additional memory space of nk words, for any constant k, our algorithm is able to easily transform it into an in-place algorithm with a time of o(n2) in the worst case. Empirically, our method outperforms insertionsort in all the different cases tested, even for small input collections. Furthermore, our experiments show that, for small or large datasets from either of the two main probability distributions—Uniform and Normal; our algorithms also outperform any traditional method like quicksort, mergesort or heapsort, and even better than the efficient hybrid algorithm introsort —std::sort() method provided by the GNU C++ Standard Library.

Abstract B029: Flip-flopping of fusion-positive rhabdomyosarcoma regulating intratumoral heterogeneity for metastasis

Abstract Of rhabdomyosarcoma (RMS), the most common pediatric soft tissue sarcoma, higher metastatic propensity of fusion-positive rhabdomyosarcoma (FPRMS), a subtype with PAX3-FOXO1 fusion gene, than fusion-negative subtype (FNRMS) suggest that the fusion gene may stimulate metastasis. Recent single-cell level research revealed that FPRMS has heterogeneity in the expression levels of the fusion gene, i.e., PAX3-FOXO1 fusion protein (P3F-FP) and corresponding phenotypes related to metastasis, e.g., higher cell motility of cells with low P3F-FP (FPLow). However, it remains unclear how the fusion gene and its heterogeneous expression regulate metastasis. Interestingly, FPRMS shows collective invasion, distinct from individual invasion of FNRMS. The collective invasion is prevalently observed in invasive tumors and has higher metastatic potentials than individual invasion. Despite its substantial commonality with well-characterized individual invasion, its features stemming from multi-cellular organization cause distinct features, potentially related to its higher metastatic potentials. Specifically, heterogeneity of a collective cell group, i.e., the leader/follower coordination, regulates collective invasion. The collective cell mass consists of “leaders,” trailblazing invasion paths, and “followers”, spreading through the paths despite their less invasiveness. At the initiation step of metastasis, the mass may enrich the subpopulation of leaders, driving collective invasion. However, it may revert the enrichment at the colonization step, suppressing migration and promoting colonization driven by followers at the secondary tumor site. This well-tuned process upon each metastasis step suggests the significance of dynamic switching between leaders/followers, regulating collective invasion and, in turn, metastasis. Here, we suggest bistability, a system with two stable states, represents the leader/follower coordination in FPRMS. The bistable system exhibits switch-like transitions between the states. i.e., “flip-flopping.” The flip-flopping enables even a small input to drive an “on/off” transition towards leaders or the other, implying the dynamic switching corresponding to each metastasis step. We discovered two distinct subpopulations with leader-like fast migrating FPLow/follower-like slow FPHigh cells, representing bistability in FPRMS. Specifically, the marginal region of FPRMS cell mass where leaders mainly reside showed higher subpopulation of fast FPLow cells. The bistability requires double-positive/negative feedback in the signaling, regulating flip-flopping. We found that YAP signaling may establish double-negative feedback with P3F-FP, modulating the flip-flopping. Thus, we suggest that the fusion gene functions as a toggle switch of bistability, and its "flip-flopping" modulation of expression by YAP signaling may designate cells as leaders or followers, regulating collective invasion and, thus, metastasis. This project will shed light on the metastasis of FPRMS, potentially leading us to suppress its metastasis. Citation Format: JinSeok Park, Anthonios Chronopolous. Flip-flopping of fusion-positive rhabdomyosarcoma regulating intratumoral heterogeneity for metastasis [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B029.