Combination Of Different Signals Research Articles

The importance of relative customer-based label equity when signaling sustainability and health with certifications and tags

As a result of the increased crowding of the retail landscape with health and sustainability signals and hundreds of different certifications and claims, there is a growing need to determine the critical success factors and guidelines for professional practice. The current paper investigates how different combinations of signals impact consumers’ choice and willingness to pay (WTP). We identify and test two major certifications from a branding perspective. The results show that consumers will have a preference and higher WTP for fish filets with signals (certificates/tags or health/sustainability) that hold higher customer-based label equity (familiarity, understanding, trust) when shown in a choice-based situation. The results show the importance of a clear reference point, label equity (familiarity, understanding, trust), and customer values when using third-party certifications and/or simple taglines.

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

Bayesian search for gravitational wave bursts in pulsar timing array data

The nanohertz frequency band explored by pulsar timing arrays provides a unique discovery space for gravitational wave (GW) signals. In addition to signals from anticipated sources, such as those from supermassive black hole binaries, some previously unimagined sources may emit transient GWs (a.k.a. bursts) with unknown morphology. Unmodeled transients are not currently searched for in this frequency band, and they require different techniques from those currently employed. Possible sources of such GW bursts in the nanohertz regime are parabolic encounters of supermassive black holes, cosmic string cusps and kinks, or other, as-yet-unknown phenomena. In this paper we present BayesHopperBurst, a Bayesian search algorithm capable of identifying generic GW bursts by modeling both coherent and incoherent transients as a sum of Morlet–Gabor wavelets. A trans-dimensional reversible jump Markov chain Monte Carlo sampler is used to select the number of wavelets best describing the data. We test BayesHopperBurst on various simulated datasets including different combinations of signals and noise transients. Its capability to run on real data is demonstrated by analyzing data of the pulsar B1855 + 09 from the NANOGrav 9 year dataset. Based on a simulated dataset resembling the NANOGrav 12.5 year data release, we predict that at our most sensitive time–frequency location we will be able to probe GW bursts with a root-sum-squared amplitude higher than ∼5 × 10−11 Hz−1/2, which corresponds to ∼40M ⊙ c 2 emitted in GWs at a fiducial distance of 100 Mpc.

Optical Beamforming Network for Multi-Beam Operation With Continuous Angle Selection

We propose an optical beamforming network (OBFN) for multi-beam operation and continuous selection of the beam angles. The OBFN is based on the use of wavelength-division multiplexing elements for the generation and manipulation of the signals that correspond to different beams and different antenna elements (AEs), and on the use of optical delay lines for the introduction of tunable time delays to these signals. We analyze the operating principle of the proposed OBFN in the downlink and uplink directions of a wireless system, and we experimentally demonstrate its proof of concept in the downlink direction up to the photodetection stage, using a $2\times 4$ setup based on commercial components. The implemented OBFN has the potential to support the steering of two beams and the excitation of four AEs. Using microwave signals with 15-GHz carrier frequency, high-order modulation formats, and symbol rates up to 1 Gbaud, we modulate two optical carriers and investigate the performance of the OBFN in four experimental cases, employing different combinations of signals, steering angles, and antenna excitation schemes. Through the analysis of the expected radiation pattern and off-line measurements of the BER of the decoded signals at the intended steering angles, we validate the feasibility of the proposed OBFN. The vision for actual implementations of the proposed OBFN would be enabled by means of photonic integration techniques.

On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.

Characterizing the carrier-envelope offset in an optical frequency comb without traditional f-to-2f interferometry.

We present a new method to measure the frequency noise and modulation response of the carrier-envelope offset (CEO) beat of an optical frequency comb that does not make use of the traditional f-to-2f interferometry. Instead, we use an appropriate combination of different signals to extract the contribution of the CEO frequency without directly detecting it. We present a proof-of-principle validation realized with a commercial Er:fiber frequency comb and show an excellent agreement with the results obtained using a standard f-to-2f interferometer. This approach is attractive for the characterization of novel frequency comb technologies for which self-referencing is challenging, such as semiconductor mode-locked lasers, microresonator-based systems, or GHz repetition rate lasers.

Adaptive complex event processing for harmful situation detection

In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place.

A role for the hypoblast (AVE) in the initiation of neural induction, independent of its ability to position the primitive streak

The mouse anterior visceral endoderm (AVE) has been implicated in embryonic polarity: it helps to position the primitive streak and some have suggested that it might act as a “head organizer”, inducing forebrain directly. Here we explore the role of the hypoblast (the chick equivalent of the AVE) in the early steps of neural induction and patterning. We report that the hypoblast can induce a set of very early markers that are later expressed in the nervous system and in the forebrain, but only transiently. Different combinations of signals are responsible for different aspects of this early transient induction: FGF initiates expression of Sox3 and ERNI, retinoic acid can induce Cyp26A1 and only a combination of low levels of FGF8 together with Wnt- and BMP-antagonists can induce Otx2. BMP- and Wnt-antagonists and retinoic acid, in different combinations, can maintain the otherwise transient induction of these markers. However, neither the hypoblast nor any of these factors or combinations thereof can induce the definitive neural marker Sox2 or the formation of a mature neural plate or a forebrain, suggesting that the hypoblast is not a head organizer and that other signals remain to be identified. Interestingly, FGF and retinoids, generally considered as caudalizing factors, are shown here to play a role in the induction of a transient “pre-neural/pre-forebrain” state.

Gastrointestinal projection maps of the vagus nerve are specified permanently in the perinatal period

The vagal innervation of the proximal gastrointestinal (GI) tract is lateralized. To determine whether this pattern is specified as early as the perinatal period, neonatal rat pups were given unilateral cervical vagotomies. Separate groups received (1) transections below the left nodose ganglion, (2) left cervical resections that included removal of the nodose ganglion, or (3) sham surgeries. At 4 months of age, each animal’s vagal afferent projections from the unoperated side were mapped by injecting the nodose with WGA-HRP, preparing the stomach as wholemounts, and processing the tissue with tetramethyl benzidine. The two types of vagal afferent endings in GI smooth muscle, namely intraganglionic laminar endings and intramuscular arrays, were surveyed separately, and their regional distributions were mapped. Changes in the nucleus of the solitary tract (NST) and dorsal motor nucleus of the vagus (DMNX) were assessed with cell counts and area measurements. Neonatal loss of the vagus innervating one side of the GI tract, with or without ganglionectomy, did not cause the unoperated vagus to sprout to the denervated side. In addition, removal of the projections to the one side of the target organ did not produce a reorganization of the projection maps of the unoperated vagus within its normal or ipsilateral wall of the GI tract. Although the regional patterns of the unoperated ipsilateral vagus were not affected, the packing densities of both types of afferents supplied by this trunk were moderately reduced. The DMNX of the vagotomized side displayed extensive (∼83%) neuronal loss; the DMNX on the unoperated side as well as the NST on both sides exhibited limited (∼20–25%) losses. The lack of a peripheral projection field reorganization — except for a moderate down-regulation — after complete unilateral denervation suggests that both the laterality and the afferent terminal phenotypes (or target tissues) of the vagus in the proximal GI tract are specified by postnatal day one in the rat. The present results, taken together with other observations, also suggest that three different combinations of signals orchestrate the commitments of vagal afferents respectively to (1) the side of the organ, (2) the region within the organ wall, and (3) the accessory and innervated tissues that complex with the fully differentiated ending.

The Effect of Irradiance on Carboxylating/Decarboxylating Enzymes and Fumarase Activities in Mesembryanthemum crystallinum L. Exposed to Salinity Stress

Abstract: In Mesembryanthemum crystallinum plants, treated for 9 days with 0.4 M NaCl at low light intensities (80 ‐ 90 or 95 ‐ 100 μE m‐2 s‐1; λ = 400 ‐ 700 nm), no day/night malate level differences (Δmalate) were detected. At high light (385 ‐ 400 μE m‐2 s‐1) strong stimulation of PEPC activity, accompanied by a Δmalate of 11.3 mM, demonstrated the presence of CAM metabolism. This indicates that, to evolve day/night differences in malate concentration, high light is required. Salt treatment at low light induces and increases the activity of NAD‐ and NADP‐malic enzymes by as much as 3.7‐ and 3.9‐fold, while at high light these values reach 6.4‐ and 17.7‐fold, respectively. The induction of activity of both malic enzymes and PEPC (phospoenolpyruvate carboxylase) take place before Δmalate is detectable. An increase in SOD (superoxide dismutase) was observed in plants cultivated at high light in both control and salt‐treated plants. However, in salt‐treated plants this effect was more pronounced. Carboxylating and decarboxylating enzymes seem to be induced by a combination of different signals, i.e., salt and light intensity. Plants performing CAM, after the decrease of activity of both the decarboxylating enzymes at the beginning of the light period, showed an increase in these enzymes in darkness when the malate pool reaches higher levels. In CAM plants the activity of fumarase (Krebs cycle) is much lower than that in C3 plants. The role of mitochondria in CAM plants is discussed.