This paper introduces a novel method for decoding uplink messages in Internet of Things (IoT) networks that utilize packet repetition, such as Sigfox and LoRa. These protocols use packet repetition at different pseudo-random frequency hops to avoid collisions and channel fades, but they do not coherently combine signals across these random (a priori unknown) hops. This paper introduces a novel frequency alignment strategy that leverages the frequency-hopped repetition structure to decode signals at lower transmission power levels without requiring channel state information, providing a significant advantage over techniques that rely on channel estimates. The proposed approach is validated through extensive simulations and real-world experiments examining its effectiveness in both single-user and multi-user environments under conditions of high system load and interference. The results demonstrate that the proposed approach not only enhances the performance and reliability of IoT networks but also contributes to more energy-efficient communication for IoT Low Power Wide Area Networks (LPWANs), thus offering a substantial advancement in dependable IoT communications.

We consider the problem of learning smooth multivariate probability density functions. We invoke the canonical decomposition of multivariate functions and we show that if a joint probability density function admits a truncated Fourier series representation, then the classical univariate Fejér-Riesz Representation Theorem can be used for learning bona fide joint probability density functions. We propose a scalable, flexible, and direct framework for learning smooth multivariate probability density functions even from potentially incomplete datasets. We demonstrate the effectiveness of the proposed framework by comparing it to several popular state-of-the-art methods.

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).

The unprecedented growth in wireless Internet-of-Things and WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection– which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. This paper proposes a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary’s interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Experiments using a software radio testbed reveal that, for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible for signal to interference plus noise ratio (SINR) in the range of −20 to −40 dB. The approach works with unknown time-varying channels, digital or analog modulation, it is immune to carrier frequency offset, and, as a side-benefit, it provides means for accurate synchronization of the secondary user even at very low SINR.

Graphs are powerful abstractions that naturally capture the wealth of relationships in our interconnected world. This paper proposes a new approach for graph alignment, a core problem in graph mining. Classical (e.g., spectral) methods use fixed embeddings for both graphs to perform the alignment. In contrast, the proposed approach fixes the embedding of the 'target' graph and jointly optimizes the embedding transformation and the alignment of the 'query' graph. An alternating optimization algorithm is proposed for computing high-quality approximate solutions and compared against the prevailing state-of-the-art graph aligning frameworks using benchmark real-world graphs. The results indicate that the proposed formulation can offer significant gains in terms of matching accuracy and robustness to noise relative to existing solutions for this hard but important problem.