Two-stage Cascade Research Articles

Membrane Fractionation of Liquors from Lignin-First Biorefining.

For the utilization of each lignin fraction in the lignin liquors, the development of separation strategies to fractionate the lignin streams by molecular weight ranges constitutes a timely challenge to be tackled. Herein, membrane filtration was applied to the refining of lignin streams obtained from a lignin-first biorefining process based on H-transfer reactions catalyzed by Raney Ni, by using 2-PrOH as a part of the lignin extraction liquor and as an H-donor. A two-stage membrane cascade was considered to separate and concentrate the monophenol-rich fraction from the liquor. Building on the results, an economic evaluation of the potential of membrane filtration for the refining of lignin streams was undertaken. In this proof-of-concept report, a detailed analysis is presented of future developments in the performance required for the utilization of membrane filtration for lignin refining and, more aspiringly, solvent reclamation.

Thermodynamic performance comparison of a two-stage cryocooler and cascade cryocoolers with thermal anchoring for precooling purpose

This paper describes the prospective issue of actively incorporating a cryocooler for thermal anchoring purpose to block the heat ingress from room temperature and reduce the everlasting parasitic heat load. Two systems, one with a single two-stage cryocooler and the other with two independent cascade cryocoolers, are examined in this paper to elucidate the involved thermodynamic inefficiencies in terms of energy and entropy flows. The overall system efficiency is greatly affected by the percent Carnot efficiency of the actual cryocooler covering the large temperature span. The single two-stage cryocooler configuration is more efficient than the cascade cryocoolers in general because it can transfer the heat load from the primary cryogenic load temperature to room temperature directly by thermomechanical coupling. The cascade cryocoolers, however, have to handle the heat load at the intermediate temperature section when the lower temperature unit dumps heat to the cold end of the upper temperature stage, which often creates significant entropy burden to the upper one. On the other hand, the true cascade cryocoolers can also attain higher efficiency than the single two-stage cryocooler if the heat pumping process from the primary cryogenic load temperature to room temperature is too inefficient with excessively large temperature ratio. This efficiency reversal is possible by virtue of dividing the large temperature span into two small ones. In this case, the appropriate intermediate temperature must be selected and a highly effective external heat exchanger needs to be carefully incorporated in between. Parametric studies have been performed to discuss the impact of the intermediate temperature on thermal anchoring and different parasitic conduction or radiation heat leak.

A 0.65 V, linearized cascade UWB LNA by application of modified derivative superposition technique in 130 nm CMOS technology

In this paper, a low-voltage linearized ultra-wideband (UWB) low noise amplifier (LNA) is presented. The core of the proposed circuit is a two-stage cascade structure which is forward body biased to operate under a low supply voltage. In the proposed topology the low linearity drawback of the cascade structure is improved by exploiting the modified derivative superposition (MDS) technique at the second stage. The MDS technique consists of an auxiliary transistor biased in the moderate inversion region paralleled with the main transistor biased in the strong inversion region and two degeneration inductors connected at the source node of the main and auxiliary transistors which altogether aim to cancel out contributions of both the second and third-order non-linearity coefficients on the third-order input intercept point (IIP3). Applying MDS technique on cascade structure has the advantage to have both high linearity and low supply voltage features in an UWB LNA. Simulated at post-layout level in 130 nm RFCMOS TSMC technology, the structure features a 13.8 dBm IIP3, consuming only 11.2 mW from a low supply voltage of 0.65 V. The proposed LNA achieves a relatively flat gain of 12 ± 1 dB, noise figure of 1.8–4 dB, and return power loss less than − 10 dB.

Effect of rainbow trout production in a two-stage cascade system on water quality

Effect of rainbow trout production in a two-stage cascade system on water quality

Planar THz FELs Based on Intense Parallel Sheet Electron Beams and Intracavity Wave Scattering

A design for a planar FEL operating in the terahertz frequency range at the multimegawatt power level is being jointly developed at the ELMI accelerator by the Budker Institute of Nuclear Physics and the Institute of Applied Physics. The FEL oscillator is driven by parallel intense sheet electron beams of moderately relativistic energy, and transitions to the above frequency range via a two-stage cascade scheme. The first beam generates a powerful millimeter pump wave, which then travels through special waveguides to the parallel channel and is scattered by the second beam into a wave of terahertz radiation. Various possible arrangements of the FEL scheme are discussed, and results from their simulation are presented. The results from cold tests of the FEL’s electrodynamic system are reported.

A novel wheezing detection approach based on constrained non-negative matrix factorization

The early wheezing detection is still a challenging task in biomedical signal processing because the presence of wheeze sounds often indicate respiratory diseases from airway obstructions. Currently, most of the first clinical examinations to detect any airway obstructions are carried out using auscultation. However, a high percentage of diagnoses are misdiagnosed since they are highly dependent on the physician’s training in the wheezing detection, especially in noisy environments in which weak wheeze sounds can be masked by louder respiratory sounds. In this work, we propose a novel wheezing detection approach, based on Constrained Non-negative Matrix Factorization, that uses two-stage cascade: separation and detection. The novelty of the separation stage is to model wheeze and respiratory sounds as reliably as possible that they can be observed in the nature incorporating constraints (sparseness and smoothness) into the NMF factorization. Once the estimated wheezing and respiratory signal are obtained from the separation stage, the detection contribution is based on the use of the Kullback-Leibler divergence to discriminate between wheezing and respiratory areas. The experiments have been conducted using three different datasets composed of healthy or unhealthy patients. First, an optimization process is applied to obtain the optimal parameters of the separation stage. Finally, the performance of the wheezing detection of the proposed method is evaluated taking into account other state-of-the-art methods. Experimental results report that i) the proposed method outperforms recent state-of-the-art wheezing detection approaches showing a robust wheezing detection performance even evaluating noisy environments and ii) the ability of the proposal to reliably detect healthy patients.

A Novel Ship Target Detection Algorithm Based on Error Self-adjustment Extreme Learning Machine and Cascade Classifier

High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for ship target detection and tracking because of its wide visual field and large sea area coverage. However, most of the existing ship target detection methods of HFSWR face two main difficulties: (1) the received radar signals are strongly polluted by clutter and noises, and (2) it is difficult to detect ship targets in real-time due to high computational complexity. This paper presents a ship target detection algorithm to overcome the problems above by using a two-stage cascade classification structure. Firstly, to quickly obtain the target candidate regions, a simple gray-scale feature and a linear classifier were applied. Then, a new error self-adjustment extreme learning machine (ES-ELM) with Haar-like input features was adopted to further identify the target precisely in each candidate region. The proposed ES-ELM includes two parts: initialization part and updating part. In the former stage, the L1 regularizer process is adopted to find the sparse solution of output weights, to prune the useless neural nodes and to obtain the optimal number of hidden neurons. Also, to ensure an excellent generalization performance by the network, in the latter stage, the parameters of hidden layer are updated through several iterations using L2 regularizer process with pulled back error matrix. This process yields appropriate output weights and the appropriate hidden weights. Experimental results show that (1) compared with standard ELM, our proposed ES-ELM has higher classification accuracy and training efficiency, and the generalization performance is not sensitive to regularization parameter, (2) the proposed ship target detection algorithm based on ES-ELM outperforms most of the state-of-the-art methods for detection accuracy and computational efficiency.

A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network

Every day we experience unprecedented data growth from numerous sources, which contribute to big data in terms of volume, velocity, and variability. These datasets again impose great challenges to analytics framework and computational resources, making the overall analysis difficult for extracting meaningful information in a timely manner. Thus, to harness these kinds of challenges, developing an efficient big data analytics framework is an important research topic. Consequently, to address these challenges by exploiting non-linear relationships from very large and high-dimensional datasets, machine learning (ML) and deep learning (DL) algorithms are being used in analytics frameworks. Apache Spark has been in use as the fastest big data processing arsenal, which helps to solve iterative ML tasks, using distributed ML library called Spark MLlib. Considering real-world research problems, DL architectures such as Long Short-Term Memory (LSTM) is an effective approach to overcoming practical issues such as reduced accuracy, long-term sequence dependency, and vanishing and exploding gradient in conventional deep architectures. In this paper, we propose an efficient analytics framework, which is technically a progressive machine learning technique merged with Spark-based linear models, Multilayer Perceptron (MLP) and LSTM, using a two-stage cascade structure in order to enhance the predictive accuracy. Our proposed architecture enables us to organize big data analytics in a scalable and efficient way. To show the effectiveness of our framework, we applied the cascading structure to two different real-life datasets to solve a multiclass and a binary classification problem, respectively. Experimental results show that our analytical framework outperforms state-of-the-art approaches with a high-level of classification accuracy.

Dynamic hardware system for cascade SVM classification of melanoma

Melanoma is the most dangerous form of skin cancer, which is responsible for the majority of skin cancer-related deaths. Early diagnosis of melanoma can significantly reduce mortality rates and treatment costs. Therefore, skin cancer specialists are using image-based diagnostic tools for detecting melanoma earlier. We aim to develop a handheld device featured with low cost and high performance to enhance early detection of melanoma at the primary healthcare. But, developing this device is very challenging due to the complicated computations required by the embedded diagnosis system. Thus, we aim to exploit the recent hardware technology in reconfigurable computing to achieve a high-performance embedded system at low cost. Support vector machine (SVM) is a common classifier that shows high accuracy for classifying melanoma within the diagnosis system and is considered as the most compute-intensive task in the system. In this paper, we propose a dynamic hardware system for implementing a cascade SVM classifier on FPGA for early melanoma detection. A multi-core architecture is proposed to implement a two-stage cascade classifier using two classifiers with accuracies of 98% and 73%. The hardware implementation results were optimized by using the dynamic partial reconfiguration technology, where very low resource utilization of 1% slices and power consumption of 1.5 W were achieved. Consequently, the implemented dynamic hardware system meets vital embedded system constraints of high performance and low cost, resource utilization, and power consumption, while achieving efficient classification with high accuracy.

Optimization and thermodynamic analysis of a cascade PLNG (pressurized liquefied natural gas) process with CO2 cryogenic removal

Based on an increased liquefaction pressure, PLNG (Pressurized liquefied natural gas) technology has the advantages of energy-saving and space-saving. This paper proposes a novel cascade PLNG process with CO2 cryogenic removal, wherein a two-stage cascade refrigeration system is used to replace the conventional three-stage one and a CO2 cryogenic removal unit is integrated with the process to eliminate CO2 pre-treatment facilities. CO2 is removed to 0.5% level at a pressure of 1.5 MPa and temperature of 165.7 K. Three refrigerant options, namely CH4-C2H6, CH4-C2H4 and C2H4-C3H8, are selected for the novel process and three process configurations are designed based on the refrigerants. Taking the specific energy consumption as a target, the three designed processes are simulated and optimized. Results show that C2H4-C3H8 process has the lowest specific power consumption, the best matched composite curves and the smallest heat exchanger size. Under optimum condition, the specific power consumption of C2H4-C3H8 process is 0.2559 kWh/Nm3, 2.5% less than that of conventional CH4-C2H4-C3H8 process. In addition, thermodynamic analyses are conducted for the proposed processes. The COP and exergy efficiency of C2H4-C3H8 process are calculated to be 0.75 and 0.47, respectively, improving by 17% and 38% over that of conventional one.

Design and analysis of wideband low‐power LNA for improved RF performance with compact chip area

Here, a wideband low-noise amplifier (LNA) based on the two-stage cascade configuration is presented to improve the radiofrequency (RF) performance. With the common gate (CG) input stage, the proposed LNA provides wideband input matching, while the wideband gain response was achieved using the peaking inductors inserted at the drain terminals of each stage. With a standard CMOS process, the chip area of the proposed wideband LNA is only 0.116 mm2. However, it consumes a 5.4 mW power from a supply voltage of . From the post-layout simulation results, it achieves maximum power gain of 11.13 dB at 8.5 GHz, input return loss below −9.44 dB, reverse isolation less than −60 dB, and small group delay variation of ±97 ps across 8.5–20 GHz frequency range. Moreover, noise figure (NF) lies in the range of 2.19–3.23 dB, whereas the NF minimum () varies in the range of 1.55–2.91 dB for 8.5–20 GHz frequency range. Apart from this, the proposed LNA achieves an IIP3 of 0.96 dBm, when a two-tone test is performed with a frequency spacing of 50 MHz.

Iterative peptide synthesis in membrane cascades: Untangling operational decisions

Membrane enhanced peptide synthesis (MEPS) combines liquid-phase synthesis with membrane filtration, avoiding time-consuming separation steps such as precipitation and drying. Although performing MEPS in a multi-stage cascade is advantageous over a single-stage configuration in terms of overall yield, this is offset by the complex combination of operational variables such as the diavolume and recycle ratio in each diafiltration process. This research aims to tackle this problem using dynamic process simulation. The results suggest that the two-stage membrane cascade improves the overall yield of MEPS significantly from 72.2% to 95.3%, although more washing is required to remove impurities as the second-stage membrane retains impurities together with the anchored peptide. This clearly indicates a link between process configuration and operation. While the case study is based on the comparison of single-stage and two-stage MEPS, the results are transferable to other biopolymers such as oligonucleotides, and more complex system configurations (e.g. three-stage MEPS).

Design and Optimization of Pressurized Liquefaction Processes for Offshore Natural Gas Using Two-Stage Cascade Refrigeration Cycles

Because of increasing interests in offshore natural gas and due to limited deck space, simpler liquefied natural gas processes are necessary. The aim of this paper is to develop three pressurized liquefaction processes using two-stage cascade refrigeration cycles instead of the conventional three-stage refrigeration cycles. The proposed processes are CH4–C2H6, CH4–C2H4, and C2H4–C3H8 processes. Taking the specific energy consumption as the objective function, simulation based optimization is conducted for the three novel processes as well as two conventional cascade processes by a sequential search method. Optimization results show that the C2H4–C3H8 process is the most efficient one with a specific energy consumption of 0.2089 kWh/Nm3, 22% less than that of the conventional CH4–C2H4–C3H8 process. Moreover, a detailed thermodynamic analysis is carried out for the five processes. The thermodynamic analysis results confirm that the C2H4–C3H8 process presents the best composite curve match and the highest co...

Optimization of Two-Stage Cascade Refrigeration Systems Using Hfo Refrigerants in The High-Temperature Circuit and Hfc-134a in Low-Temperature Circuit

The eco-friendly refrigeration technologies which are receiving more and more attention in the days by day of for solving energy and environmental problems. In this paper, it is suggested to phase out presently most used refrigerant R-134a considering global warming and to use natural refrigerants such as ammonia, carbon dioxide and hydro carbons in two stage cascade vapour compression refrigeration system for sustainable environment. Thermal model was developed for single stage and also two stage cascade refrigeration systems using HFO-1234yf for low temperature applications in the range of 100°C to -51.5°C and HFO-1234ze for high temperature applications in the range of 100°C to -30.5°C. Numerical computation was carried out by using R1234ze in the temperature range of 60°Cto -30°Cin the high temperature circuit and R1234yf in the range of 20°Cto -50°Cfor low temperature range. The developed model can also predicts the thermal performances of using nine eco-friendly refrigerants in low temperature circuit and R1234ze n low temperature applications. The ecofriendly refrigerants HFO can replace R134a without minor modifications. However up to temperature -50°Cin the low temperature evaporator circuit with temperature overlapping (Approach=10oC) and condenser temperature of 55°Cwith cascade evaporator temperature of 0°C , the combination of R1234ze in high temperature circuit and R1234yf in low temperature circuit gives best results for replacing R134a and for -100°Cof low temperature evaporator circuit and 55°Cof high temperature condenser and -30°C cascade evaporator with temperature overlapping (Approach=10oC) the combination of R1234ze – and R245fa gives better thermodynamic performances as compared to R-1234yf and R245fa. The optimum values for system-6 (using R1234ze in high temperature circuit and HFC-134a in low temperature circuit , the optimum second law efficiency (exergetic efficiency) obtained at low temperature evaporator is at -78°Cto -80°Crespectively and no optimum was found using HFO-1234yf in high temperature circuit and HFC-134a in low temperature circuit.

Tube-based MPC with Nonlinear Control for Load Transportation using a UAV

Abstract This paper presents a two-stage cascade control framework to solve hierarchically the trajectory tracking problem of a Tilt-rotor Unmanned Aerial Vehicle (UAV) carrying a suspended load. Initially, a nonlinear dynamic model is presented, which is after decoupled into two subsystems. The outer control system is designed by means of a robust tube-based Model Predictive Control (MPC) strategy, which is used to control the UAV’s planar motion and stabilize the suspended load. For the inner control system, the input-output feedback linearization (IOFL) technique combined with the dynamic extension approach and a discrete mixed H2/H∞ controller is considered to control the UAV’s altitude and attitude. Simulations results are carried out to corroborate the proposed control strategy.

Optimum Performance Analysis of a Hybrid Cascade Refrigeration System Using Alternative Refrigerants

This research article deals with a Hybrid cascade refrigeration system (HCRS) comprising of vapor absorption refrigeration system (VARS) at the top or high temperature stage and vapor compression refrigeration system (VCRS) at the bottom or low temperature stage in a two-stage cascade refrigeration system using a mathematical model for energy and exergy analysis. The theoretical analysis is carried out taking H2O-LiBr solution in VARS and R134a, R32, R1234yf in VCRS as refrigerants. The three refrigerants (R134a, R32 and R1234yf) in this study are selected because these refrigerants have been recommended as future alternative refrigerants due to their zero ODP and low GWP properties. It has been found that the optimum generator temperature of HCRS is same for all the three eco-friendly refrigerants at which maximum coefficient of performance (COP) and maximum exergetic efficiency occurs. Further it is observed that the performance of the system considered in the study is better with R134a in comparison to R32 and R1234yf as refrigerants in the VCRS and R32 exhibits better result when compared with R1234yf.

Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.

Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different life stages of bladder cells by using two cascade Convolutional Neural Networks (CNNs). Initially, a hybrid object proposal algorithm (called EdgeSelective) by combining EdgeBoxes and Selective Search is proposed to generate candidate object proposals instead of a single Selective Search method in Region-CNN (R-CNN), and it can exploit the advantages of different mechanisms for generating proposals so that each cell in the image can be fully contained by at least one proposed region during the detection process. Then, the obtained cells from the previous step are used to train and extract features by employing CNNs for the purpose of cell life stage recognition. Finally, a series of comparison experiments are implemented. The results show that the proposed method can obtain better performance than traditional methods either in the stage of cell detection or cell life stage recognition, and it encourages and suggests the application in the development of new anticancer drug and cytopathology analysis of cancer patients in the near future.

Aza Diels-Alder Reactions of Nitriles, N,N-Dimethylhydrazones, and Oximino Ethers. Application in Formal [2 + 2 + 2] Cycloadditions for the Synthesis of Pyridines.

Metal-free, formal [2 + 2 + 2] cycloaddition strategies for the synthesis of polycyclic pyridine derivatives are described. The overall transformation proceeds via a two-stage pericyclic cascade mechanism. In the first step, an intramolecular propargylic ene reaction generates a vinylallene that is necessarily locked in the s-cis conformation. This vinylallene exhibits exceptional reactivity as a Diels-Alder reaction partner and engages in [4 + 2] cycloadditions with normally unreactive azadienophiles including unactivated cyano groups and heterosubstituted imine derivatives such as dimethylhydrazones and oximino ethers. Few examples of oximino ether Diels-Alder reactions have been reported previously, and normal electron-demand [4 + 2] cycloadditions of unactivated dialkylhydrazones are unprecedented. Overall, this metal-free formal [2 + 2 + 2] cycloaddition provides access to polycyclic pyridine derivatives and complements transition-metal-catalyzed [2 + 2 + 2] strategies.

Multi-Rate Acquisition for Dead Time Reduction in Magnetic Resonance Receivers: Application to Imaging With Zero Echo Time.

For magnetic resonance imaging of tissues with very short transverse relaxation times, radio-frequency excitation must be immediately followed by data acquisition with fast spatial encoding. In zero-echo-time (ZTE) imaging, excitation is performed while the readout gradient is already on, causing data loss due to an initial dead time. One major dead time contribution is the settling time of the filters involved in signal down-conversion. In this paper, a multi-rate acquisition scheme is proposed to minimize dead time due to filtering. Short filters and high output bandwidth are used initially to minimize settling time. With increasing time since the signal onset, longer filters with better frequency selectivity enable stronger signal decimation. In this way, significant dead time reduction is accomplished at only a slight increase in the overall amount of output data. Multi-rate acquisition was implemented with a two-stage filter cascade in a digital receiver based on a field-programmable gate array. In ZTE imaging in a phantom and in vivo, dead time reduction by multi-rate acquisition is shown to improve image quality and expand the feasible bandwidth while increasing the amount of data collected by only a few percent.

On-Chip Two-Tone Synthesizer Based on a Mixing-FIR Architecture

A low-distortion current-steering two-tone sinusoidal signal synthesizer based on a mixing-finite impulse response (FIR) architecture is proposed. The proposed robust synthesizer adopts only digital blocks. It implements a two-stage cascade FIR harmonic cancellation technique that generates a single tone quasi-sinusoidal waveform and suppress the odd-order harmonics up to the 21st order. Differential-mode circuitry and a 50% duty cycle clock are also utilized to cancel the even-order harmonics. The single-tone signal is further up-converted to the desired local oscillator (LO) frequency band, thereby producing the desired two-tone sinusoidal signals. The proposed synthesizer contains a current mirror array implementing the FIR tap coefficients. Accuracy is enhanced using dynamic element matching. The other building blocks consist of a 24-phase clock generator, a current combiner, and a passive mixer with bootstrapped MOS switches. This two-tone synthesizer can be used to conduct a linearity built-in self-test. It is fabricated in 130-nm standard CMOS technology, occupying a 0.056-mm2 silicon area. Measurement shows better than −68 dBc third-order intermodulation (IM3) below 480-MHz LO frequency without calibration. For the LO frequency <76.8 MHz and the two-tone difference <2 MHz, an IM3 better than −75 dBc can be achieved. The imbalance between the two-tone amplitudes is measured <0.1 dB across the whole frequency range from dc to 1 GHz.