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This research article discusses the challenges faced by the Network-on-Chip (NoC) architecture due to increased integration density and proposes a novel fault-tolerant multi-application mapping approach called ”FANC.” The approach is based on Machine Learning (ML) and can provide solutions for unseen graphs and topologies without prior training. The proposed technique uses an ML-based model to extract relevant information from the search data and incorporate it into the search process. This results in a more robust model with a higher convergence rate and solution quality. The approach is evaluated using a variety of simulation parameters, such as communication cost, network latency, throughput, and power usage. Static simulations are performed in a Python programming environment, while dynamic simulations are performed with a SystemC-based cycle-accurate NoC simulator and the Orion2.0 Power tool. The results show that FANC reduces communication costs by 266%. It also improves network latency by 9%, throughput by 1%, and power consumption by 7%. The approach also simplifies and minimizes the search area in the design exploration process and can be used as an auxiliary component for other optimization algorithms.

AbstractIntelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges.

Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.

Sympatico–vagal balance is essential for regulating cardiac electrophysiology and plays an important role in arrhythmogenic conditions. Various noninvasive methods, including electrocardiography (ECG), have been used for clinical assessment of the sympatico–vagal balance. This study aimed to use a custom-designed wearable device to record ECG and ECG-based cardiac function biomarkers to assess sympatico–vagal balance during tonic pain in healthy controls. Nineteen healthy volunteers were included for the ECG measurements using the custom-designed amplifier based on the Texas Instruments ADS1299. The ECG-based biomarkers of the sympatico–vagal balance, (including heart rate variability, deceleration capacity of the heart rate, and periodic repolarization dynamic), were calculated and compared between resting and pain conditions (tonic pain). The custom-designed device provided technically satisfactory ECG recordings. During exposure to tonic pain, the periodic repolarization dynamics increased significantly (p = 0.02), indicating enhancement of sympathetic nervous activity. This study showed that custom-designed wearable devices can potentially be useful in healthcare as a new telemetry technology. The ECG-based novel biomarkers, including periodic repolarization dynamic and deceleration capacity of heart rate, can be used to identify the cold pressor-induced activation of sympathetic and parasympathetic systems, making it useful for future studies on pain-evoked biomarkers.

The effect of varying the amounts of metals Fe, Cu, K, and Mo was studied on a catalyst supported on activated carbon (AC), which is an item of novelty of this paper. The base-case catalyst contains 16% Fe, 0.9% K, 6% Mo, and 0.8% Cu relative to the AC support. For all of the catalysts used, alcohol production is small. The production of hydrocarbons depends upon the amount of Fe and other promoters used. The amount of Fe was increased from 0% to 32% on the catalyst containing base-case amounts of the other materials. While 0% Fe shows no activity towards Fischer–Tropsch synthesis (FTS), 32% Fe shows a marginal increase in FTS activity when compared with 16% Fe. Furthermore, the amount of K was increased from 0% to 1.8%, with the other metals in their base-case amounts. The selectivity of C1–C4 decreases with the addition of K, while the selectivity of C5+ increases. Analogously, the amount of Mo was increased from 0% to 12%. A small amount of Mo results in an increase in FTS activity but decreases with the addition of more Mo. Cu on the catalyst was increased from 0% to 1.6%, with 0.8% Cu proving optimum for FTS.