Demand-side flexibility which reacts to hourly price signals is expected to play an essential role in balancing intermittent electricity generation in the future carbon-neutral power system. This ...study investigates the price responsiveness of households and various influencing factors through a pricing experiment which was implemented as a reward scheme and involved 3746 Norwegian households. These households, characterised by a highly electrified energy usage such as electric heating and charging electric cars, were subjected to variable hourly price signals with a one-day advance notification over the course of three winter months. The study reveals that households reduced their electricity demand by, on average, 2.92% in hours with high prices, and the reduction did not diminish significantly after repeated interventions. Moreover, an increased response could be observed for price signals with a short peak price period and when prices exceeded a threshold of 15 NOK/kWh. These results suggest that despite the limited potential of manual demand response from households, it can still be relied upon and utilised to enhance power system operations and planning. Additionally, to fully exploit its potential, it is recommended to enhance price responsiveness through tailored price information and by incentivising investments in automatic response and energy storage.
•Households can respond to price signals without automatic electricity management.•Manual response is persistent over time, with minor fatigue observed.•Pricing experiments may educate for long-term energy use behaviour changes.•Price response should be included in power system planning and demand prognoses.•Incentives for increased demand flexibility and price responsiveness needed.
Except in the case of normal (i.e., Gaussian) distribution, it is very difficult to calculate the marginal probability distribution of ARMA signals. By using a particular form of modeling such ...signals with random coefficients we show that the problem can be solved and we present an algorithm allowing the numerical generation of ARMA signals with arbitrary specified marginal distribution function. Their properties are analyzed in computer experiments. The experimental results show in general a very good agreement with theoretical calculations.
Real-world phenomena that can be formulated as signals are often affected by a number of factors and appear as multi-component modes. To understand and process such phenomena, "divide-and-conquer" is ...probably the most common strategy to address the problem. In other words, the captured signal is decomposed into signal components for each individual component to be processed. Unfortunately, for signals that are superimposition of non-stationary amplitude-frequency modulated (AM-FM) components, the "divide-and-conquer" strategy is bound to fail, since there is no way to be sure that the decomposed components take on the AM-FM formulations which are necessary for the extraction of their instantaneous frequencies (IFs) and amplitudes (IAs). In this paper, we propose an adaptive signal separation operation (ASSO) for effective and accurate separation of a single-channel blind-source multi-component signal, via introducing a time-varying parameter that adapts locally to IFs and using linear chirp (linear frequency modulation) signals to approximate components at each time instant. We derive more accurate component recovery formulae based on the linear chirp signal local approximation. In addition, a recovery scheme, together with a ridge detection method, is also proposed to extract the signal components one by one, and the time-varying parameter is updated for each component. The proposed method is suitable for engineering implementation, being capable of separating complicated signals into their components or sub-signals and reconstructing the signal trend directly. Numerical experiments on synthetic and real-world signals are presented to demonstrate our improvement over the previous attempts.
This paper addresses simultaneous high-precision measurement and analysis of generic reference signals by using inexpensive commercial off-the-shelf software-defined radio hardware. Sine reference ...signals are digitally downconverted to baseband for the analysis of phase deviations. Hereby, we compare the precision of the fixed-point hardware digital signal processing chain with a custom single instruction multiple data x86 floating-point implementation. Pulse reference signals are analyzed by a software trigger that precisely locates the time where the slope passes a certain threshold. The measurement system is implemented and verified using the Universal Software Radio Peripheral (USRP) N210 by Ettus Research LLC. Applying standard 10 MHz and 1 PPS reference signals for testing, a measurement precision (standard deviation) of 0.36 and 16.6 ps is obtained, respectively. In connection with standard PC hardware, the system allows long-term acquisition and storage of measurement data over several weeks. A comparison is given to the dual-mixer time difference and time interval counter, which are state-of-the-art measurement methods for sine and pulse signal analysis, respectively. Furthermore, we show that our proposed USRP-based approach outperforms measurements with a high-grade digital sampling oscilloscope.
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from ...amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
Traditional likelihood-based and handcrafted feature-based methods for overlapped signals automatic modulation classification (OS-AMC) suffer from the uncertainty of the overlapped numbers in ...practical application scenarios, while existing deep learning methods still require a complex training process. In this letter, a deep learning approach with a hybrid network combining ConvNeXt and atrous self-attention transformer is proposed to solve this problem. Specifically, a reference signal-aided training is introduced to generate the decision threshold of the proposed network automatically, which omits the searching process of the decision threshold and makes the training process more efficient. The simulation results indicate that the proposed method can achieve superior classification accuracy with a simpler training process and lower computational complexity and memory cost.
The basic building blocks of communication are signals, assembled in various sequences and combinations, and used in virtually all inter- and intra-specific interactions. While signal evolution has ...long been a focus of study, there has been a recent resurgence of interest and research in the complexity of animal displays. Much past research on signal evolution has focused on sensory specialists, or on single signals in isolation, but many animal displays involve complex signaling, or the combination of more than one signal or related component, often serially and overlapping, frequently across multiple sensory modalities. Here, we build a framework of functional hypotheses of complex signal evolution based on content-driven (ultimate) and efficacy-driven (proximate) selection pressures (sensu Guilford and Dawkins 1991). We point out key predictions for various hypotheses and discuss different approaches to uncovering complex signal function. We also differentiate a category of hypotheses based on inter-signal interactions. Throughout our review, we hope to make three points: (1) a complex signal is a functional unit upon which selection can act, (2) both content and efficacy-driven selection pressures must be considered when studying the evolution of complex signaling, and (3) individual signals or components do not necessarily contribute to complex signal function independently, but may interact in a functional way.
Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are ...able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.
As coherent reception technology continues to move downstream the optical telecommunication infrastructure, the complexity of the involved transceiver technology can quickly introduce a ...techno-economic roadblock. Under this umbrella, we experimentally demonstrate a conceptually simple, single-polarization and analogue coherent homodyne receiver that builds on no more than an optically locked externally modulated laser. We evaluate this coherent homodyne receiver in the context of analogue radio-over-fiber transmission - a demanding application setting, where a small degradation in signal integrity is leading to large reception penalties. We conduct a continuous-mode characterization of the locking methodology, which enables homodyne detection and hence the transparent translation of electrical signals from the optical to the electrical domain during the coherent reception process. Furthermore, the locking dynamics are being investigated for packet-level reception at a 1 MHz frame rate and two time division multiplexed channels, which are sourced by two optical emitters with free-running laser sources. The radio-over-fiber transmission performance is evaluated for 64-ary quadrature amplitude modulated, orthogonal frequency division multiplexed radio with a short guard interval of 2.7 μs between the packet radio signals. A data rate of 0.5 Gb/s over 100 MHz radio bandwidth is obtained at an optical loss budget of >35 dB between transmitter and receiver, without resorting to digital signal processing resources for the purpose of signal recovery. Moreover, a small ~0.3% penalty in error vector magnitude between continuous- and packet-mode confirms the compatibility of the analogue coherent receiver in networks with fast locking requirements.
Proteins in the karyopherin-β family mediate the majority of macromolecular transport between the nucleus and the cytoplasm. Eleven of the 19 known human karyopherin-βs and 10 of the 14
S. cerevisiae ...karyopherin-βs mediate nuclear import through recognition of nuclear localization signals or NLSs in their cargos. This receptor-mediated process is essential to cellular viability as proteins are translated in the cytoplasm but many have functional roles in the nucleus. Many known karyopherin-β-cargo interactions were discovered through studies of the individual cargos rather than the karyopherins, and this information is thus widely scattered in the literature. We consolidate information about cargos that are directly recognized by import-karyopherin-βs and review common characteristics or lack thereof among cargos of different import pathways. Knowledge of karyopherin-β-cargo interactions is also critical for the development of nuclear import inhibitors and the understanding of their mechanisms of inhibition.
►Karyopherin-β (Kapβ) proteins transports protein cargos into the nucleus. ►Kapβs recognize nuclear localization signals or NLSs in their cargos. ►Of the 19 human Kapβs only two have well characterized NLSs. ►Many NLSs recognized by individual Kapβs are large and diverse in sequence. ►New approaches are needed to classify diverse NLSs recognized by most Kapβs.
PDF
