Respiration Rate (RR) is an indicator of numerous conditions and its importance in condition evaluation and monitoring has been well established. This paper deals with RR estimation based on the photoplethysmogram (PPG), a signal that can be easily acquired by wearables and is already captured by pulse oximeters. PPG is modulated by respiration and thus statistical methods can be used to extract the respiratory information encoded in the signal. A critical preprocessing step of RR estimation is that of reliable PPG peak detection, which is a form of signal quality evaluation when incorporated in PPG beat segmentation. To demonstrate the importance of this step we compare three peak detection algorithms against three RR estimation methods and show that peak extraction based on beat morphological characteristics leads to better RR estimation. Furthermore, we propose a new PPG-based method fusion that leads to higher estimation accuracy. All methods are evaluated on CapnoBase, a benchmark database on respiration, the best method achieving a RMSE of 3.4 bpm for 60-second intervals.

Variability in single neuron responses presents a challenge in establishing reliable representations of visual stimuli essential for driving behavior. To enhance accuracy, integration of responses from multiple neurons is imperative. This study leverages simultaneous recordings from a large population (tens of hundreds) of neurons, achieved through in vivo mesoscopic 2-photon calcium imaging of the primary visual cortex (V1) in mice, under visual stimulus conditions as well as in resting state (absence of stimulus). The visual stimulus consisted of 16 distinct randomly shuffled directions of motion presented to the mice. We employed mutual information to identify neurons that contain the most significant information about the stimulus direction. As expected, neurons displaying high predictive power (HPP) in stimulus decoding exhibit elevated firing event rates during stimulus presentation. Furthermore, functional connectivity among HPP neurons during visual stimulation is denser and stronger compared to functional connectivity among other visually responsive neurons. Functional connections among HPP neurons appear to form independently of distance, suggesting a distributed yet highly coordinated network. In contrast, HPP neuron activity and functional connectivity differed significantly at resting state. Specifically, during the resting state, HPP neurons exhibited lower event rates and functional connectivity structure that was not significantly different from that of other visually responsive neurons. This suggests that HPP neurons are less susceptible to being driven simultaneously by internal brain states in the absence of a stimulus. Finally, the tuning properties of HPP neurons were unexpectedly diverse: while some were sharply tuned, others conveyed a similar amount of mutual information, despite exhibiting much weaker tuning. This study sheds light on the organization of neuronal ensembles important for decoding visual motion direction in mouse area V1, contributing to the understanding of information processing in mouse visual cortex.

In this paper, we explore covert communication in a heterogeneous network where a transmitter sends a confidential message to a receiver by utilizing two different radio frequency (RF) bands, low-very high frequency (low-VHF) and microwave frequency modalities. Since two RF modalities exhibit different characteristics in terms of channels and available bandwidths, it is important to efficiently leverage the modalities based on the wireless environment. Therefore, we develop a new algorithm that optimizes the transmit powers and bandwidths for the modalities and selects one modality with the goal of maximizing the detection error probability at a warden while guaranteeing a quality-of-service requirement of the receiver. We first derive a closed-form joint optimal power and bandwidth solution for a given modality, and then provide a modality selection method. From numerical simulations, it is validated that the proposed scheme achieves the optimal performance and the covertness can be enhanced by judiciously choosing one of the two modalities based on the channel condition.

Easily and flexibly deployable ad-hoc communication networks emerging in tactical military or even civilian contexts, frequently suffer from poor synchronization due to lack of coordinating infrastructure. In addition to synchronization issues, and especially in military settings, security from the aspect of detectability is also of crucial importance. Imperfect synchronization can be dealt with by making use of Quasi-Synchronous Code Division Multiple Access (QS-CDMA), relying on Loosely Synchronous Codes to maintain orthogonality in the presence of limited time delays. Security, in terms of low probability of detection (LPD) from the standpoint of a malicious adversary, can be improved (reduced detection) by employing randomization techniques that disrupt the inherent structure of the transmitted QS-CDMA signals. This is based on the fact that QS-CDMA signals are Cyclostationary, having (almost) periodic Auto-Correlation functions (ACF) due to eminent signal periodicities (such as spreading code repetition). In this paper, we propose techniques to disturb the ACF and equivalently the Spectral Correlation function, and reduce the Degree of Cyclostationarity (DCS), our LPD measure. Specifically, we investigate randomization via 1) random per symbol time dithering and 2) random selection of spreading sequences, as well as a hybrid approach combining time dithering and code randomization. In all proposed techniques knowledge of the randomization pattern is not required at the legitimate receiver. We derive the Spectral Correlation function of the QS-CDMA signal under the proposed randomization schemes and compare it to simulations. We show through analysis and extensive numerical simulations that the proposed technique can reduce the DCS by almost two orders of magnitude. We also show that enhanced LPD can be achieved using the proposed techniques while sacrificing a part of the reduced time synchronization requirement. We further analyze the implications of the friendly receiver not knowing the randomization pattern and present results on the resulting communication performance.

Ad-hoc flexibly deployable networks destined for tactical military or civilian applications often suffer from poor synchronization which may lead to unreliable communication. Quasi-Synchronous (QS) CDMA employing Loosely Synchronous (LS) codes allows weak synchronization due to the Zero-Correlation Zone of the code's correlation functions. Apart from reliability, an essential characteristic of military networks is having low probability of detection (LPD) in the presence of adversaries employing sophisticated detection techniques. Such techniques are based on the fact that manmade signals are cyclostationary, meaning that they have some periodic structure (e.g. spreading sequence repetition) that can be exploited for improved detection. Disturbing those recurring patterns can reduce the probability of detection measured in terms of the Degree of Cyclostationarity (DCS). We aim to achieve that by employing some techniques that will perturb the signal structure by randomly selecting spreading sequences, random time dithering or a combination of the two. We study the trade-off between the communication performance and DCS reduction of such a system under these different techniques and compare the results.