Multiple Types Of Signals Research Articles

Bistable Soft Shells for Programmable Mechanical Logic.

Mechanical computing promises to integrate semiconductor-based digital logic in several applications, but it needs straightforward programmable devices for changing computing rules in situ. A methodology based on strain-governed, bistable soft shells that process digital information by interchanging their internal/external surfaces is proposed. This bistable behavior, explained via model-based design, safeguards robustness by working only once for each input pulse. Thus, these shells are leveraged to create a buffer and a NOT gate that lead to six fundamental gates (AND, OR, NAND, NOR, XOR, and XNOR). All these functions are integrated into a unique programmable device, making mechanically integrated circuits more adaptable with rule-changeable logic operations. This design ensures continuous processes and general applicability to multiple types of signals (a pressurized fluid can replace mechanical driving signals is shown). It also empowers more complex logic functions suitable for expanded applications, such as the half and full adders is addressed.

Organic Synaptic Transistors with an Ultra‐Short‐Term Weight‐Reconstruction for Processing Multiple Types of Signals

AbstractAn organic synaptic transistor (OST) that utilizes a multiple‐annealing process and an ion gate to achieve significant reductions in operating voltage and increases in transconductance is demonstrated. The OST exhibits an ultra‐short‐term plasticity (USTP) with a maximum retention time of only 20.7 ms, which does not increase with the number and duration of spikes. This is the shortest retention time yet achieved by an ion‐gel‐regulated synaptic transistor. In addition, OST‐integrated array exhibits a tunable weight plasticity and a short weight refresh time for a stable image resetting in ample time of <0.2 s. It is also sensitive to the frequency and amplitude of electrical inputs; a low‐frequency suppression and a nonlinear amplitude gain enables OST‐constructed filter for use in processing multiple types of signals. This work is a step toward constructing high‐performance and multifunctional artificial intelligent systems.

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%.

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time–frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

Hierarchical K-means clustering method for accelerated Lorentzian estimation (KALE) in chemical exchange saturation transfer-magnetic resonance imaging quantification.

Quantification of in vivo chemical exchange saturation transfer (CEST) magnetic resonance signals is challenging due to contamination from coexisting effects, including the direct water effect and asymmetric magnetization transfer. Fitting-based analysis allows the calculation of multiple types of signals from the line shape of Z-spectra. However, the conventional voxelwise method has several drawbacks, including its long computation time and its susceptibility to image noise and Z-spectra oscillations, and it is difficult to determine the initial fitting parameters. Herein, we propose a K-means clustering method for accelerated Lorentzian estimation (KALE) in CEST quantification. Briefly, voxels in CEST images are clustered into K groups according to their Z-spectra characteristics. A 'groupwise' fitting process is then performed with preset initial values, yielding a set of fitted spectra and fitted parameters for each group. With the updated initial values, each group is further clustered into subgroups, and groupwise fitting is performed again. This hierarchical K-means clustering and parameter updating process continues until the pixel number or intensity error meets the termination criteria. Voxelwise fitting could be further conducted to improve the quantification images (termed voxel-K) by utilizing the previous groupwise KALE results as the initial values (termed group-K). Incorporated with Lorentzian difference (LD) quantification, KALE was first optimized and evaluated on 5 healthy human brain datasets at 3 Tesla. Compared with traditional voxel-by-voxel LD quantification, the computation times of group-K and voxel-K were significantly reduced by ~85% and ~70%, respectively (P<0.001). Furthermore, the group-K images exhibited better denoising performance than traditional LD and voxel-K. KALE was further validated on six ischemic rat brains acquired at 7 Tesla, with both LD_group-K and LD_voxel-K displaying almost identical contrast maps with traditional voxelwise maps. When incorporated with the five-pool Lorentzian fitting (LF), KALE exhibited an improved contrast-to-noise ratio (CNR) for amplitude maps of each pool [P=0.003, 0.015, 0.047, and 0.047 for amide, nuclear Overhauser effect (NOE), magnetic transfer (MT) and guanidine amine, respectively] and improved fitting goodness (P=0.033). KALE quantification provides comparable or even superior contrast maps to traditional voxelwise fitting, with significantly reduced computation time. The 'smart' and hierarchical voxel-clustering and parameter updating process of KALE may facilitate more preclinical and clinical CEST applications.

Common Mental Disorders in Smart City Settings and Use of Multimodal Medical Sensor Fusion to Detect Them

Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.

The Effect of Prior Parameters in a Bayesian Approach to Inferring Material Properties from Experimental Measurements

A Bayesian formulation for inference of material properties from experimental signals is proposed with a normal likelihood quantifying noise and an inverse gamma prior expressing uncertainty about noise variance. Asymptotic analysis shows that, for large samples, the posterior distribution is maximized at the database signal that minimizes an L2 norm between the experimental data and database entries. The largest posterior probability tends to 1 as the number of data points on the signals tends to ∞. There exists a critical ratio between the mode of the inverse gamma prior and the true variance of the normally distributed noise. This ratio determines how the posterior probability varies with the shape parameter of the inverse gamma prior. Numerical results based on an analytical expression and simulations confirm the conclusions from the asymptotic analysis. These results show that posterior probabilities are insensitive to the choice of prior mode when the prior’s shape parameter is taken to be small. In spite of the use of interpolation in database construction, the approach is limited to cases where a small number of parameters is to be identified. When multiple signal types are analyzed simultaneously, a method using a weighted average of posterior distributions from the different signal types is proposed. This method is compared with a classical Bayesian approach that uses a joint likelihood. Effective choices of prior parameters are recommended.

Deep Learning Based Cognitive Radio Modulation Parameter Estimation

Automatic Modulation Classification (AMC) is a critical issue in electromagnetic spatial perception. Currently traditional recognition techniques are difficult to adapt to complex signal situations. Most existing modulation classification algorithms ignore the complementarity between different features and the importance of feature fusion. Based on this, we proposed a method for image feature fusion for AMC that fully uses the complementarity between different image features. The original signal is converted into an image by the Gramian Angular Field (GAF) method, and the GAF image is used as the input to the network, meanwhile the received signal is converted from the Inphase-Quadrature (I-Q) domain to the r-θ domain using the Accumulated Polar Feature conversion technique, and the original signal is feature coded from the r-θ domain and then converted into an image. The fused features of the two images are used as input to the neural network for model training to achieve automatic modulation classification of multiple types of signals. In the evaluation phase, the differences in the recognition effectiveness of the proposed method by different neural networks are discussed. Experiments show that the best performance is achieved using the Swin-Transformer network model, with a more than 90% recognition rate for the modulation method at signal-to-noise ratios greater than 4dB.

A female mimic signal type in the vibrational repertoire of male Enchenopa treehoppers

Abstract Animals vary in the complexity and size of the signal repertoire used in communication. Often, these behavioural repertoires include multiple signal types for the same process, for instance, courtship. In Enchenopa treehoppers (Hemiptera: Membracidae) mate-searching males produce plant-borne vibrational advertisement signals. Receptive females then respond to males with their own signals. Here we describe an additional signal type in the repertoire of these males. We collected nymphs in Wisconsin, USA, and recorded the spontaneous signalling bouts of adult males and duetting signals of females using laser vibrometry. Two-thirds of males produced the additional signal type, which differed in temporal and spectral features from the main male advertisement signals, whilst resembling female duetting signals in placement and acoustic features. Our findings suggest that this might be a female mimic signal. Overall, our findings highlight the diversity in the behavioural repertoire that animals may use for reproduction.

The use of weighted self-organizing maps to interrogate large seismic data sets

SUMMARY Modern microseismic monitoring systems can generate extremely large data sets with signals originating from a variety of natural and anthropogenic sources. These data sets may contain multiple signal types that require classification, analysis and interpretation: a considerable task if done manually. Machine learning techniques may be applied to these data sets to expedite and improve such analysis. In this study, we apply an unsupervised technique, the Self-Organizing Map (SOM), to high-volume data recorded by an in-mine microseismic network. This represents a good example of a large seismic data set that contains a wide range of signals, owing to the diversity of source processes occurring within the mine. The signals are quantified by extracting a number of features (temporal and spectral) from the waveforms which are provided as input data for the SOM. We develop and implement a weighted variant of the SOM in which the contributions of various different features to the training of the map are allowed to evolve. The standard and weighted SOMs are applied to the data, and the output maps compared. Both variants are able to separate source types based on the waveform characteristics, allowing for rapid, automatic classification of signals and the ability to find sources with similar waveforms. Fast classification of such signals provides practical benefit by automatically discarding waveforms associated with anthropogenic sources within the mine while seismic signals originating from genuine microseismic events, which constitute a small fraction of all signals, can be prioritized for subsequent processing and analysis. The weighted variant provides an exploratory tool through quantification of the contribution of different features to the clustering process. This helps to optimize the performance of the SOM through the identification of redundant features. Furthermore, those features that are assigned large weights are considered to be more representative of the source generation processes as they contribute more to the cluster separation process. We apply weighted SOMs to data from a mine recorded during two different time periods, corresponding to different stages of the mine development. Changes in feature importance and in the observed distribution of feature values indicate evolving source generation processes and may be used to support investigatory analysis. The weighted SOM therefore represents an effective tool to help manage and investigate large seismic data sets, providing both practical benefit and insight into underlying event mechanisms.

A relaxed support vector data description algorithm based fault detection in distribution systems

The power detection of the distribution network is essential for reliable and secure distribution. In this paper, a flexible dual-threshold SVDD fault warning algorithm with fault samples is proposed to deal with problems concerning complex network topology, accessible data, and missing fault data in the power grid. For the problems of complicated network topology and a wide variety of signal types, we propose to combine wavelet packet energy features with Spearman to extract electric signal features, and finally achieve accurate feature extraction of multiple signal types. In the case of the problems of untimely judgement and low accuracy of the original SVDD, a relaxed SVDD fault warning algorithm with fault samples is correspondingly proposed. We turn the original SVDD boundary into a double-layer boundary, and divides the hypersphere space into three regions to increase the sensitivity to the fault samples and lessen the risk of missed detection. Besides, an adaptive update strategy is developed, which reduces the computational effort of the model and is proven more applicable to the distribution. Finally, the method is applied to numerical examples and fault detection experiments, and the experimental results in turn verified its effectiveness and superiority.

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network.

Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Preliminary Design of Monitoring System of ITER Static Magnetic Field Test Facility

The structure and characteristics of the ITER static magnetic field test facility is introduced. The preliminary design scheme of the monitoring system of the facility is proposed. According to the characteristics of the facility that it includes several subsystems, multiple signal types and is suffered from electromagnetic interference, the solution given combines NI CompactRIO embedded system, NI PXIe and Ethernet exchange technology. It can realize signal acquisition, status monitoring, safety interlock, parameter setting and other function. Also, the scheme covers the methods to weaken the interference from the surrounding magnetic field on the system itself and improve the stability of the system. The preliminary design scheme complies with control, data acquisition and communication specifications from ITER, and meets the SIL level assessment requirements in the concept phase.

Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion.

Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors’ dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models.

Non-Vocal Behaviors Are More Frequent During the Decisive Negotiation Phases in Barn Owl Siblings.

Simple SummaryAnimal communication can involve the use of multiple types of signals. While vocal communication has been widely studied in natural populations, there is a dearth of knowledge about the possible role of vibrations or noises made by body movements in communication processes. By using experimental settings both under natural conditions and in the laboratory, we showed that barn owl nestlings (Tyto alba) produced various non-vocal noises. Movement noises were particularly frequent when nestlings were involved in the intense vocal interactions they use to negotiate the priority for access to the next food item delivered by parents. Body movements might therefore have a role in reinforcing vocal signals during competitive interactions among siblings.Animals produce vibrations or noises by means of body movements, which can play a role in communication. These behaviors enhance signal transmission or receiver attention and could be specifically used during turn-taking phases of a reciprocal exchange of signals. In the barn owl Tyto alba, nestlings vocalize one after the other to negotiate which individual will have priority access to the impending prey item to be delivered by the parents. Owlets adjust their vocalization to their own hunger level and to their siblings’ vocalization, withdrawing from the contest in front of highly vocal, and hence hungry, motivated nestmates. As sibling negotiation is a multicomponent display, we examined whether body movements could also be part of the negotiation process. To this end, we analyzed whether the vocalizations of one nestling affected its nestmate’s movements in three separate experiments: in natural nests, in the lab, and using a playback procedure. Nestling barn owls move in a variety of ways, such as repeated tapping of the floor with a foot, scratching the floor with claws, or flapping wings. Body movements were more frequent during the turn-taking phases of vocal interactions, when siblings emitted longer calls and at a greater rate. Once an individual monopolized vocal activity, siblings became less vocal and less active. Moreover, owlets produced more noisy body movements during the phases of vocal interactions which are crucial to prevail in negotiation. Non-vocal physical activities might reinforce vocal signals during sibling to sibling (sib–sib) interactions, or reflect owlets’ arousal, in the critical period during which they vocally settle which individual will dominate the competition.

Traffic noise differentially impacts call types in a Japanese treefrog (Buergeria japonica)

AbstractAcoustic noise from automobile traffic impedes communication between signaling animals. To overcome the acoustic interference imposed by anthropogenic noise, species across taxa adjust their signaling behavior to increase signal saliency. As most of the spectral energy of anthropogenic noise is concentrated at low acoustic frequencies, species with lower frequency signals are expected to be more affected. Thus, species with low‐frequency signals are under stronger pressure to adjust their signaling behaviors to avoid auditory masking than species with higher frequency signals. Similarly, for a species with multiple types of signals that differ in spectral characteristics, different signal types are expected to be differentially masked. We investigate how the different call types of a Japanese stream breeding treefrog (Buergeria japonica) are affected by automobile traffic noise. Male B. japonica produce two call types that differ in their spectral elements, a Type I call with lower dominant frequency and a Type II call with higher dominant frequency. In response to acoustic playbacks of traffic noise, B. japonica reduced the duration of their Type I calls, but not Type II calls. In addition, B. japonica increased the call effort of their Type I calls and decreased the call effort of their Type II calls. This result contrasts with prior studies in other taxa, which suggest that signalers may switch to higher frequency signal types in response to traffic noise. Furthermore, the increase in Type I call effort was only a short‐term response to noise, while reduced Type II call effort persisted after the playbacks had ended. Overall, such differential effects on signal types suggest that some social functions will be disrupted more than others. By considering the effects of anthropogenic noise across multiple signal types, these results provide a more in‐depth understanding of the behavioral impacts of anthropogenic noise within a species.

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

Purpose Brain–computer interface (BCI)-controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to the ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer-grade BCI devices are often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals. Materials and Methods This paper summarizes the development of a portable and cost-efficient BCI-controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. A short survey was used following the system usability scale (SUS), to measure the usability of the technology to be trialed in clinical settings. Results From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials. Implication on rehabilitation For the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton. This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement. This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating.

Your chemical coat tells me you are my delicacy: a predatory stink bug uses cuticular hydrocarbons to identify prey

Extensive studies have shown that cuticular hydrocarbons (CHCs) are among the major cues that allow many insects to identify interspecific and intraspecific variation between individuals. CHCs often have mutually nonexclusive functions that can provide multiple types of signals, while their role in predator–prey interactions has received little attention. Here, we used a predatory stink bug, Zicrona caerulea (Hemiptera: Pentatomidae), and one of its favorite flea beetle prey, Altica viridicyanea (Coleoptera: Chrysomelidae), to test the hypothesis that CHCs of prey are important chemical cues for a predator. Two-choice bioassays using dead beetles and glass dummies clearly indicated that the CHC profile of A. viridicyanea is the pivotal cue in prey identification for Z. caerulea. The results also suggested that the role of acoustic and visual cues can be ignored in prey recognition at close range.

Phenotypic integration and the evolution of signal repertoires: A case study of treefrog acoustic communication.

Animal signals are inherently complex phenotypes with many interacting parts combining to elicit responses from receivers. The pattern of interrelationships between signal components reflects the extent to which each component is expressed, and responds to selection, either in concert with or independently of others. Furthermore, many species have complex repertoires consisting of multiple signal types used in different contexts, and common morphological and physiological constraints may result in interrelationships extending across the multiple signals in species’ repertoires. The evolutionary significance of interrelationships between signal traits can be explored within the framework of phenotypic integration, which offers a suite of quantitative techniques to characterize complex phenotypes. In particular, these techniques allow for the assessment of modularity and integration, which describe, respectively, the extent to which sets of traits covary either independently or jointly. Although signal and repertoire complexity are thought to be major drivers of diversification and social evolution, few studies have explicitly measured the phenotypic integration of signals to investigate the evolution of diverse communication systems. We applied methods from phenotypic integration studies to quantify integration in the two primary vocalization types (advertisement and aggressive calls) in the treefrogs Hyla versicolor, Hyla cinerea, and Dendropsophus ebraccatus. We recorded male calls and calculated standardized phenotypic variance–covariance (P) matrices for characteristics within and across call types. We found significant integration across call types, but the strength of integration varied by species and corresponded with the acoustic similarity of the call types within each species. H. versicolor had the most modular advertisement and aggressive calls and the least acoustically similar call types. Additionally, P was robust to changing social competition levels in H. versicolor. Our findings suggest new directions in animal communication research in which the complex relationships among the traits of multiple signals are a key consideration for understanding signal evolution.

An F-Score-Weighted Indoor Positioning Algorithm Integrating WiFi and Magnetic Field Fingerprints

Indoor positioning systems have attracted much attention with the recent development of location-based services. Although global positioning system (GPS) is a widely accepted and accurate outdoor localization system, there is no such a solution for indoor areas. Therefore, various systems are proposed for the indoor positioning problem. Fingerprint-based positioning is one of the widely used methods in this area. WiFi-received signal strength (RSS) is a frequently used signal type for the fingerprint-based positioning system. Since WiFi signal distribution is nonstationary, accuracy is insufficient. Therefore, the performance of indoor positioning systems can be enhanced using multiple signal types. However, the positioning performance of each signal type varies depending on the characteristics of the environment. Considering the variability of the performances of different signal types, an F-score-weighted indoor positioning algorithm, which integrates WiFi-RSS and MF fingerprints, is proposed in this study. In the proposed approach, the positioning is first performed by maximum likelihood estimation for both WiFi-RSS and magnetic field signal values to calculate the F-score of each signal type. Then, each signal type is combined using F-score values as a weight to estimate a position. The experiments are performed using a publicly available dataset that contains real-world data. Experimental results reveal that the proposed algorithm is efficient in achieving accurate indoor positioning and consolidates the system performance compared to using a single type of signal.