Triple Simplex Matrix Completion for Expense Forecasting

Forecasting project expenses is a crucial step for businesses to avoid budget overruns and project failures. Traditionally, this has been done by financial analysts or data science techniques such as time-series analysis. However, these approaches can be uncertain and produce results that differ from the planned budget, especially at the start of a project with limited data points. This paper proposes a constrained non-negative matrix completion model that predicts expenses by learning the likelihood of the project correlating with certain expense patterns in the latent space. The model is constrained on three probability simplexes, two of which are on the factor matrices and the third on the missing entries. Additionally, the predicted expense values are guaranteed to meet the budget constraint without the need of post-processing. An inexact alternating optimization algorithm is developed to solve the associated optimization problem and is proven to converge to a stationary point. Results from two real datasets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art algorithms.

eWake: A Novel Architecture for Semi-Active Wake-Up Radios Attaining Ultra-High Sensitivity at Extremely-Low Consumption

In this work we propose a new scheme for semi-passive Wake-Up Receiver circuits that exhibits remarkable sensitivity beyond -70 dBm, while state-of-the-art receivers illustrate sensitivity of up to -55 dBm. The receiver employs the typical principle of an envelope detector that harvests RF energy from its antenna, while it employs a nano-power operation amplifier to intensify the obtained signal prior to the final decoding that is realized with the aid of a comparator circuit. It operates at the 868 MHz ISM band using OOK signals propagated through LoRa transceivers, while also supporting addressing capabilities in order to awake only the specified network's nodes. The power expenditure of the developed receiver is as low as 580 nA, remaining at the same power consumption levels as the state-of-the-art implementations.