Open-Source FPGA Coprocessor for the Doppler Emulation of Moving Fluids

Embedded systems are nowadays employed in a wide range of application, and their capability to implement calculation-intensive algorithms is growing quickly and constantly. This result is obtained by the exploitation of powerful embedded processors that are often connected to coprocessors optimized for a particular application. This work presents an open-source coprocessor dedicated to the real-time generation of a synthetic signal that mimics the echoes produced by a moving fluid when investigated by ultrasounds. The coprocessor is implemented in a Field Programmable Gate Array (FPGA) device and integrated in an embedded system. The system can replace the complex and inaccurate flow-rigs employed in laboratorial tests of Doppler ultrasound systems and methods. This paper details the coprocessor and its standard interfaces, and shows how it can be integrated in the wider architecture of an embedded system. Experiments showed its capability to emulate a fluid flowing in a pipe when investigated by an echographic Doppler system.

A Method for Computationally Constructing Eukaryotic Synthetic Signal Peptide Sequences

We developed a computational method for constructing synthetic signal peptides from a base set of signal peptides (SPs) and non-SP sequences. A large number of structured "building blocks", represented as m-step ordered pairs of amino acids, are extracted from the base. Using a straightforward procedure, the building blocks enable the construction of a diverse set of synthetic SPs that could be utilized for industrial and therapeutic purposes. We have validated the proposed methodology using existing sequence prediction platforms such as Signal-BLAST and MULocDeep. In one experiment, 9,555 protein sequences were generated from a large randomly selected set of "building blocks". Signal-BLAST identified 8,444 (88%) of the sequences as signal peptides. In addition, the Signal-BLAST tool predicted that the generated synthetic sequences belonged to 854 distinct eukaryotic organisms. Here, we provide detailed descriptions and results from various experiments illustrating the potential usefulness of the methodology in generating signal peptide protein sequences.

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

In marine seismic acquisitions, signal interference remains a major menace. In this paper, a denoising approach based on the Variational Mode Decomposition (VMD) combined with the Hausdorff distance (HD) and Wavelet transform (WT) is proposed. There has been substantial research in this field over the years. However, traditional denoising methods fall short of achieving satisfactory results in an extremely low signal to noise ratio (SNR) environment. The feasibility, and stability of the proposed method was validated by performing simulations in MATLAB on both a synthetic signal and a seismic signal generated using real dataset. It was found that the proposed method does well in preserving marine signals in low SNR environments, and has a superior output SNR.

Learning with Selective Forgetting

Lifelong learning aims to train a highly expressive model for a new task while retaining all knowledge for previous tasks. However, many practical scenarios do not always require the system to remember all of the past knowledge. Instead, ethical considerations call for selective and proactive forgetting of undesirable knowledge in order to prevent privacy issues and data leakage. In this paper, we propose a new framework for lifelong learning, called Learning with Selective Forgetting, which is to update a model for the new task with forgetting only the selected classes of the previous tasks while maintaining the rest. The key is to introduce a class-specific synthetic signal called mnemonic code. The codes are "watermarked" on all the training samples of the corresponding classes when the model is updated for a new task. This enables us to forget arbitrary classes later by only using the mnemonic codes without using the original data. Experiments on common benchmark datasets demonstrate the remarkable superiority of the proposed method over several existing methods.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles

Rewiring signaling networks imparts cells with new functionalities that are useful for engineering cell therapies and directing cell development. While much effort has gone into connecting extracellular inputs to desired outputs, less has been done to control the signal processing steps in-between. Here, we develop synthetic signal processing circuits in mammalian cells using proteins derived from bacterial two-component signaling pathways. First, we isolate kinase and phosphatase activities from the bifunctional histidine kinase EnvZ and demonstrate tunable phosphorylation control of the response regulator OmpR via simultaneous phosphoregulation by an EnvZ kinase and phosphatase. We show that modulation of phosphatase expression at the mRNA and protein levels via miRNAs and small molecule-regulated degradation domains, respectively, can effectively tune kinase-to-output responses. Further, we implement a novel phosphorylation-based miRNA sensor that effectively classifies cell types and enables cell type-specific kinase-output signaling responses. Finally, we implement a tunable negative feedback controller by co-expressing the kinase-driven output gene with the small molecule-tunable phosphatase, substantially reducing both gene expression noise and sensitivity to perturbations at the transcriptional and translational level. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

A chemogenetic platform for controlling plasma membrane signaling and synthetic signal oscillation

Chemogenetic methods that enable the rapid translocation of specific signaling proteins in living cells using small molecules are powerful tools for manipulating and interrogating intracellular signaling networks. However, existing techniques rely on chemically induced dimerization of two protein components and have certain limitations, such as a lack of reversibility, bioorthogonality, and usability. Here, by expanding our self-localizing ligand-induced protein translocation (SLIPT) approach, we have developed a versatile chemogenetic system for plasma membrane (PM)-targeted protein translocation. In this system, a novel engineered Escherichia coli dihydrofolate reductase in which a hexalysine (K6) sequence is inserted in a loop region (iK6DHFR) is used as a universal protein tag for PM-targeted SLIPT. Proteins of interest that are fused to the iK6DHFR tag can be specifically recruited from the cytoplasm to the PM within minutes by addition of a myristoyl-D-Cys-tethered trimethoprim ligand (mDcTMP). We demonstrated the broad applicability and robustness of this engineered protein–synthetic ligand pair as a tool for the conditional activation of various types of signaling molecules, including protein and lipid kinases, small GTPases, heterotrimeric G proteins, and second messengers. In combination with a competitor ligand and a culture-medium flow chamber, we further demonstrated the application of the system for chemically manipulating protein localization in a reversible and repeatable manner to generate synthetic signal oscillations in living cells. The present bioorthogonal iK6DHFR/mDcTMP-based SLIPT system affords rapid, reversible, and repeatable control of the PM recruitment of target proteins, offering a versatile and easy-to-use chemogenetic platform for chemical and synthetic biology applications.

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

Feature Extraction Based on Teager-Kaiser Energy Operation and Envelope Spectra for Fault Detection of a Reciprocating Compressor

In this paper, signal analysis techniques based on Teager-Kaiser energy operation and envelope spectra for fault detection of the discharge valve of a reciprocating compressor is proposed. The method can accurately identify the existing fault of vibration signal features that it simulated by the synthetic signals. A two-phase study was designed to explore the signals simulation and the experimental validation. Signals simulation, which is based on the operation of a reciprocating compressor, and experiment design, which uses three conditions. The first stage is to simulate the operation of the reciprocating compressor, which is to simulate a synthetic signal for the cycle and impact. The synthetic signal is composed of a noise, square wave, and pulse wave. In this study, the synthetic signal is signal-processed by the Teager-Kaiser energy operator and the envelope spectrum that they can effectively extract feature signal and the noise almost is eliminated. The second stage is applied to the signal processing technique proposed in the first stage. Experimental verification of experiment design by the different operating conditions of reciprocating compressor valves. Through the above analysis technology, it is proved that the synthetic signal can be eliminated the background noise to obtain the feature signal. The feasibility of the proposed approach is verified by simulation results, the experiment is to validate with the measurement signals from a six-cylinder reciprocating compressor under different valve conditions. Simulations and experimental results support the proposed technology positively.