No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals

Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.

The cis -2-dodecenoic acid (BDSF) quorum sensing system in Burkholderia cenocepacia

It has been demonstrated that quorum sensing (QS) is widely employed by bacterial cells to coordinately regulate various group behaviors. Diffusible signal factor (DSF)-type signals have emerged as a growing family of conserved cell-cell communication signals. In addition to the DSF signal initially identified in Xanthomonas campestris pv. campestris, B urkholderia d iffusible s ignal f actor (BDSF, cis -2-dodecenoic acid) has been recognized as a conserved DSF-type signal with specific characteristics in both signal perception and transduction from DSF signals. Here, we review the history and current progress of the research of this type of signal, especially focusing on its biosynthesis, signaling pathways, and biological functions. We also discuss and explore the huge potential of targeting this kind of QS system as a new therapeutic strategy to control bacterial infections and diseases.

Information Processing Methods of Electronic Warfare Events Based on Communication Technology

Traditional electronic countermeasure incident intelligence processing has problems such as low accuracy and stability and long processing time. A method of electronic countermeasure incident intelligence processing based on communication technology is proposed. First, use the integrated digital signal receiver to identify various modulation methods in the complex signal environment to facilitate the processing and transmission of communication signals, then establish an electronic countermeasure intelligence processing framework with Esper as the core, and flow the situation to the processing conclusion through the PROTOBUF interactive format Redis cache. The data can realize the intelligent processing of electronic countermeasure incidents. The experimental results show that the method proposed in this paper increases the recall rate by 5 to 20% compared with other methods. This method has high accuracy and stability for electronic countermeasure incident intelligence processing and can effectively shorten the time for electronic countermeasure incident intelligence processing.

MH370 Controlled Eastward Veering and Descent After the 6th Arc

Past expert analyses of communication signals from missing Malaysian Airlines MH370 reconciled Burst Frequency Offset (BFO) errors up to the 6th of 7 arcs for a southerly track. After the 6th arc, the Satellite Data Unit (SDU) power-up or reboot resulted in settling errors in the last two data points that were ignored (first search) and later bounded (second search). For the second search, investigators invoked a high-speed vertical descent to account for BFO errors for the south track fuel-starved scenario. Two searches disappointingly failed to find the implied violent-crash site. We report that interpretations were flawed in suggesting the plane dived vertically, as investigators did not recognize that BFO extrapolations implicitly implied mathematically that the plane was also cruising along the south track, but with no fuel. Our reanalysis used the “Penang Longitude” (PL) theory that predicted a similar southerly track to the 6th arc, and that MH370 subsequently veered eastwards and descended. Doppler Shifts from vertical motions were replaced with plausible horizontal veering and declination of a high-speed aircraft. Veering predicted by the PL theory plus controlled descent plausibly accounts for nominal 7th arc BFO discrepancies for the warm-reboot scenario. We conclude that the fuel-starvation scenario analyses wrongly implied a vertical high-speed crash that ignored the impossible implicit southerly cruise, with no fuel, assumption. Instead, MH370 was piloted to a precise glide landing under power, east of the 7th arc.

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

Spatial Transformer Network-Based Automatic Modulation Recognition of Blind Signals

Modulation recognition of communication signals plays an important role in both civil and military uses. Neural network-based modulation recognition methods can extract high-level abstract features which can be adopted for classification of modulation types. Compared with traditional recognition methods based on manually defined features, they have the advantage of higher recognition rate. However, in actual modulation recognition scenarios, due to inaccurate estimation of receiving parameters and other reasons, the input signal samples for modulation recognition may have large phase, frequency offsets, and time scale changes. Existing deep learning-based modulation recognition methods have not considered the influences brought by the above issues, thus resulting in a decreased recognition rate. A modulation recognition method based on the spatial transformation network is proposed in this paper. In the proposed network, some prior models for synchronization in communication are introduced, and the priori models are realized through the spatial transformation subnetwork, so as to reduce the influence of phase, frequency offsets, and time scale differences. Experiments on simulated datasets prove that compared with the traditional CNN, ResNet, and the CLDNN, the recognition rate of the proposed method has increased by 8.0%, 5.8%, and 4.6%, respectively, when the signal-to-noise ratio is greater than 0. Moreover, the proposed network is also easier to train. The training time required for convergence has reduced by 4.5% and 80.7% compared to the ResNet and CLDNN, respectively.

A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets

How neural networks evolved to generate the diversity of species-specific communication signals is unknown. For receivers of the signals one hypothesis is that novel recognition phenotypes arise from parameter variation in computationally flexible feature detection networks. We test this hypothesis in crickets, where males generate and females recognize the mating songs with a species-specific pulse pattern, by investigating whether the song recognition network in the cricket brain has the computational flexibility to recognize different temporal features. Using electrophysiological recordings from the network that recognizes crucial properties of the pulse pattern on the short timescale in the cricket Gryllus bimaculatus, we built a computational model that reproduces the neuronal and behavioral tuning of that species. An analysis of the model's parameter space reveals that the network can provide all recognition phenotypes for pulse duration and pause known in crickets and even other insects. Phenotypic diversity in the model is consistent with known preference types in crickets and other insects, and arise from computations that likely evolved to increase energy efficiency and robustness of pattern recognition. The model's parameter to phenotype mapping is degenerate-different network parameters can create similar changes in the phenotype-which likely supports evolutionary plasticity. Our study suggests that computationally flexible networks underlie the diverse pattern recognition phenotypes and we reveal network properties that constrain and support behavioral diversity.

Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.