In the polymer industry, where polymers are transformed into a wide range of plastic products, accurate quality assessment through melt flow testing remains critical but challenging. Predicting polymer melt characteristics such as flowability with machine learning (ML) algorithms offers opportunities to optimize processing, reduce raw material and labor costs, and ensure stable, high-quality production. Two key metrics, the melt mass-flow rate (MFR) and the melt volume-flow rate (MVR), serve as industry standards for material quality control and new mixture development, both defined by strict specifications for testing machine geometry and standardized protocols. This work presents a custom-built melt flow testing machine designed to meet these specifications, integrated with computerized automation for consistent execution of the test procedure. Using a representative dataset of materials, testing temperatures, and applied weights, multiple ML models were trained to predict MFR quickly and accurately. Comparative evaluation with cross-validation demonstrated that neural network regression achieved superior results, with a root mean square error (RMSE) of 0.3 g/10 min and a mean absolute percentage error (MAPE) of 3.2%. The results underscore the promise of combining tailored ML algorithms with custom-built testing equipment and automation to deliver faster and more precise melt flow characterization. This proof-of-concept not only advances polymer processing but also paves the way for smarter, more sustainable manufacturing. By enabling consistent quality in recycled polymers, optimizing resource consumption, and integrating ML within the industrial Internet of Things (IoT), this approach points toward a future of intelligent, efficient, and reliable polymer production.

In this work, 0.75-10 THz band is explored for non-terrestrial communications, by leveraging the improved atmospheric conditions at various altitudes. A channel model for aerial communications at THz band is proposed to calculate the common flat bands for frequency-selective path gain and the colored noise spectrums, both of which are highly affected by the atmospheric conditions. With capacity computation based on common flat bands, we consider aerial vehicles at different altitudes and distances, under realistic weather and channel fading conditions, due to beam misalignment and also multi path fading. An extensive capacity analysis is presented, considering equal and water-filling power allocation. It is shown that, when there is no fading, capacity for aerial links is several orders of magnitude larger than the sea-level capacity. When ergodic capacity is computed for the fading scenarios, it is shown that the impact of fading vanishes with altitude. Sea-level ergodic capacity is increased by an order of magnitude for drone-to-drone communications, providing several Tbps at 10 m range, while 10s of Tbps is achievable among jet planes and UAVs, and several 100s of Tbps is possible for satellites/cubesats at 1 km under fading, suggesting that THz band is a promising alternative for aerial communications.

OBJECTIVE: Ballistocardiogram (BCG) can be recorded using inexpensive and non-invasive hardware to estimate physiological changes in the heart. In this paper, a methodology is developed to evaluate the impact of additive noise on the BCG signal. METHODS: A statistical model is built that incorporates subject-specific BCG morphology. BCG signals segmented by electrocardiogram RR intervals (BCG heartbeats) are averaged to estimate a parent template and subtemplates leveraging the quasi-periodic nature of the heart. Noise statistics are obtained for subtemplates with respect to the parent template. Then, a synthesis algorithm with adjustable additive noise is devised to generate subtemplates based on the individual's parent template and statistics. For the example use of the synthesis algorithm, the average correlation coefficient between subtemplates and the parent template (subtemplate versus parent template approach) is tested as a signal quality index. RESULTS: A BCG heartbeat synthesis framework that incorporates an individual's BCG morphology and physiological variability was developed to quantify variations in the BCG signal against additive noise. The signal quality assessment of a person's BCG recording can be performed without requiring any a priori knowledge of the person's BCG morphology. A data-driven constraint on the required minimum number of heartbeats for a reliable template estimation was provided. CONCLUSION: The impact of additive noise on BCG morphology and estimated physiological parameters can be analyzed using the developed methodology without requiring prior statistics. SIGNIFICANCE: This paper can facilitate the performance evaluation of BCG analysis algorithms against additive noise.

Electromagnetic communication at the nanoscale has, to date, been studied in either the very high frequency (VHF) (30–300 MHz) or in the TeraHertz band (0.1–10 THz). The main focus of this paper is on electromagnetic communication in the VHF band and determining the bit error rate (BER) performance of nanoscale receivers utilizing a carbon nanotube (CNT). To determine BER performance, statistical characterization of an average-level detector output is obtained for two different receiver configurations: nanotube receiver, and tunneling nanotube receiver. For the nanotube receiver, the linear component not considered in the previous studies is included and shown to significantly affect the variance of the detector output and, also, exact analysis is performed with some significant differences to previous approximations. The tunneling nanotube receiver, for which no statistical characterization has been done to date, is analyzed. Extensive simulation studies are presented to confirm the accuracy of the theoretical results provided for the distribution of the average-level detector output. The feasible communication distances are investigated based on the BER performance by using realistic system parameters. The results reveal the potential usage of the VHF band in nanonetworks and highlight the importance of maximizing the charge at the CNT tip.

Molecular Communication (MC) is a nano-scale communication paradigm where the information is carried by molecular signals. To establish information transmission using molecules, biological nanomachines can be utilized as transmitters and receivers. A bacteria can be programmed as a biotransceiver by modifying their genetic code to implement biological circuits. In this paper, genetically engineered bacteria-based biotransceivers are investigated for MC, where bacteria can generate and respond to the molecular signals. A biochemical model of biological circuits is presented, and both analog and digital signaling are studied. The challenges in connecting basic biological circuits to build these blocks are revealed. A biotransceiver architecture is introduced that combines sensing, transmitting, receiving and processing blocks. Furthermore, biological circuit design framework is proposed for transmission of signals with M-ary pulse amplitude modulation. The biological circuits designed for biotransceiver are elaborated via numerical results based on biochemical parameters of the genetically engineered bacteria. The provided results show that using biological circuits, bacteria can function as a transmitter and receiver node for MC. Our work stands as a basis for future biotransceiver design for MC.

To perform spectrum handoff, cognitive radio (CR) nodes communicating with each other need to exchange licensed user detection information, i.e., perform spectrum coordination, over a common control channel. The spectrum coordination can be fulfilled either via existing cognitive radio interface with time division or via a separate dedicated radio, i.e., a common control interface (CCI), continuously. CR nodes with CCI can instantly exchange licensed user detection information and cease frame transmission, while spectrum coordination can only be performed after the frame transmission period without CCI. Nevertheless, the impact of CCI incorporation into CR nodes in terms of common performance metrics must be thoroughly assessed to evaluate the worthiness of additional radio cost. In this paper, an analytical framework is presented to assess the impact of CCI incorporation into CR nodes for spectrum handoff. The developed framework enables analyzing potential benefits and disadvantages of employing CCI for spectrum handoff, in terms of achievable delay, energy consumption, spectrum utilization and event estimation performance. Extensive performance evaluations are presented to illustrate the impact of CCI utilization on efficiency of spectrum handoff. The network and communication regimes that would yield having CCI favorable are characterized in terms of spectrum conditions and CR parameters.

Terahertz (0.06-10 THz) Band communication is envisioned as a key technology for satisfying the increasing demand for ultra-high-speed wireless links. In this paper, first, a unified multi-ray channel model in the THz Band is developed based on ray tracing techniques, which incorporates the propagation models for the line-of-sight, reflected, scattered, and diffracted paths. The developed theoretical model is validated with the experimental measurements (0.06-1 THz) from the literature. Then, using the developed propagation models, an in-depth analysis on the THz channel characteristics is carried out. In particular, the distance-varying and frequency-selective nature of the Terahertz channel is analyzed. Moreover, the coherence bandwidth and the significance of the delay spread are studied. Furthermore, the wideband channel capacity using flat and water-filling power allocation strategies is characterized. Additionally, the temporal broadening effects of the Terahertz channel are studied. Finally, distance-adaptive and multi-carrier transmissions are suggested to best benefit from the unique relationship between distance and bandwidth. The provided analysis lays out the foundation for reliable and efficient ultra-high-speed wireless communications in the (0.06-10) THz Band.

Molecular communication (MC) between a transmitter and a receiver placed in the chambers attached to a microfluidic channel is investigated. A linear end-to-end channel model is developed capturing the effects of the diffusion and the junction transition at the chambers, as well as the microfluidic channel shapes and the fluid flow. The spectral density of the propagation noise is studied, and the flat frequency bands are identified for the chambers and the microfluidic channel. This suggests that in certain microfluidic design choices, the spectral density of noise may end up naturally being flat. Motivated by this result, the additive white Gaussian noise (AWGN) model is developed based on the chamber, the microfluidic channel, and the fluid flow parameters for the end-to-end propagation noise. Furthermore, the molecular memory is modeled due to inter-diffusion among transmitted molecular signals. The effect of the molecular memory on the end-to-end propagation noise is also analyzed. To substantiate our analytical results, the ranges of physical parameters that yield a linear end-to-end MC channel are investigated. These results show the validity of the AWGN model for MC over microfluidic channels and characterize the impact of the microfluidic channel and chamber geometry on the propagation noise and memory.

Energy limitation of sensor nodes is the main constraint to be addressed while designing and implementing algorithms for wireless sensor networks (WSN). Recently, to mitigate battery depletion problem and extend network lifetime, wireless passive sensor networks (WPSN) have become a new field of interest. Modulated backscattering is an important communication technique for WPSN to enable unlimited lifetime for sensor nodes. Determination of required number and power level of RF sources for wireless power transfer to sensor nodes is crucial for energy-efficient distributed sensing operation. Furthermore, deployed RF sources can share spectrum opportunistically via incorporation of cognitive radio capability such that desired distributed estimation distortion can be achieved with minimum spectrum utilization by WPSN. Employment of RF sources that radiate power only when spectrum opportunities are available unveils passive opportunistic distributed sensing (PODS). In this paper, first, we model intercepted power by passive sensor from RF sources and reflected power by passive sensor at the sink, and effect of opportunistic access to licensed spectrum bands on instantaneous throughput of sensor nodes. Then, a power level control scheme for RF sources is proposed to achieve desired distortion level with minimum energy consumption while using opportunistic distributed sensing in WPSN. Achieved estimation distortion at sink with respect to number and power level of RF sources, and available spectrum opportunities is investigated, and energy saving provided by proposed power control scheme is assessed for various distortion requirements, channel noise levels, and available spectrum opportunities via simulation experiments.

Network virtualization has been proposed as a powerful and flexible way to support multiple heterogeneous architectures on a shared infrastructure. A major challenge in this respect lies in the resource allocation problem that allocates the resources of Infrastructure Providers (InPs) among Service Providers (SPs) fairly and efficiently. Because of the complexity in the interaction between InPs and SPs, this problem is more complicated and needs more investigation. This paper introduces an InP-SP-End user (ISE) system model in network virtualization environment, in which we present a bandwidth allocation scheme based on VCG (Vickrey-Clarke-Groves) to maximize the total revenue of SPs by inhibiting selfish action of SPs, and design a Q-learning strategy algorithm in order to obtain optimal bidding strategies for SPs. Simulations and experimental studies demonstrate the effectiveness and the fairness of the proposed scheme, and the convergence of the proposed algorithm.