To design a reliable communication system utilizing millimeter-wave (mm-wave) technology, which is gaining popularity due to its ability to deliver multi-gigabit-per-second data rate, it's essential to consider the site-specific nature of the mmwave propagation. Conventional site-general stochastic channel models are often unsatisfactory for accurately reproducing the channel responses under specific usage scenarios or environments. For high-precision channel simulation that reflects sitespecific characteristics, this paper proposes a channel model framework leveraging a widely accepted 3GPP map-based hybrid channel modeling approach, and it provides a detailed recipe to apply it to an actual scenario using some examples. First, an extensive measurement campaign was conducted in typical urban macro and micro cellular environments using an inhouse dual-band (24/60 GHz) double-directional channel sounder. Subsequently, the mm-wave channel behavior was characterized, focusing on the difference between the two frequencies. Then, the site-specific large-scale and small-scale channel properties were parameterized. As an essential component for improving prediction accuracy, this paper proposes an exponential decay model for power delay characteristics of non-line-of-sight clusters, of which powers are significantly overestimated by deterministic prediction tools. Finally, using the in-house channel model simulator (CPSQDSIM) developed for grid-wise channel data (PathGridData) generation, a significant improvement in prediction accuracy compared with the existing 3GPP map-based channel model was demonstrated.

A stochastic compact model for resistive switching devices is presented. The motivation is twofold: first, introducing variability in a natural way, and second, accounting for the discrete jumps of conductance observed during set and reset transitions. The model is based on an event generation rate, and it is an "on-the-fly" procedure because events are randomly generated as the simulation proceeds in time. For the generation of events, we assume a mixed non-homogeneous Poisson process. Before considering resistive switching, we deal with the generation of successive breakdown events in metalinsulator-semiconductor structures. This confirms the validity of the approach by comparing with experimental data in which discrete events are evident. To deal with resistive switching, we transform a previous compact model into a stochastic model. Comparison with experiments in TiN/Ti/HfO2/W devices show the validity of the approach. Current-voltage loops and potentiationdepression transients in pulsed experiments are captured with a single set of parameters. Moreover, the model is an adequate framework to deal with both cycle-to-cycle and device-to-device variability.

Predictive monitoring on distributed critical infrastructures (DCI) is the ability to anticipate events that will likely occur in the DCI before they actually appear, improving the response time to avoid the rise of critical incidents. Distributed into a region or country, DCIs such as smart grids or microgrids rely on IoT, edge-fog continuum computing and the growing capabilities of distributed application architectures to collect, transport, and process data generated by the infrastructure. We present a model-agnostic distributed architecture for the inference execution of machine learning window-based prediction models of predictive monitoring applications to be used in this context. This architecture transports the events generated by the DCI using event streams to be processed by a hierarchy of nodes holding predictive models. It also handles the offloading of inferences from resource-scarce devices at lower levels to the resourceful upper nodes. Therefore, the timing requirements for setting predictions before they occur are met.

This paper presents a method for obtaining distribution grid models based on available measurements from the grid. In particular, we propose a nonlinear approximation of the DistFlow model [1], which includes line losses between nodes and is parameterized by the unknown line parameters, namely the impedance between nodes. Based on measured voltage magnitude at each node, and net injected real and reactive powers at each node, we estimate the unknown line parameter values. To achieve this, we formulate a maximum-likelihood problem that we solve using an expectation-maximisation (EM) approach. In particular, we tailor the EM approach for the proposed nonlinear grid model, and we provide a numerically robust implementation of the resulting algorithm. The proposed method is demonstrated on the IEEE 37-node test feeder network, where we compare to state-of-the-art methods and show that the proposed approach achieves a 70% reduction in voltage error and more than 10, 000 times lower error for state variables. A final comparison uses data from a real network, and we show that the proposed approach achieves parameter estimates that are up to 100 times better than competing approaches.

The actual paper presents an in-depth study and experimental development of a class of rotorcraft, named as x-tilt, that features four tilting rotors. Initially, the equations of motion modeling the aerial robot are presented based on the Euler-Lagrange formulation. The model includes the aerodynamic effects induced by the rotorcraft's relative motion and propellers. For control purposes the aforementioned model is split into a nominal model and lumped disturbance terms, the latter encompassing endogenous and exogenous uncertainties. In this vein, the actual work propose a robust navigation strategy targeting a specific performance profile whose problem is formulated through the model predictive control (MPC) framework. To this end, two schemes are proposed, (i) an integral MPC and a (ii) MP sliding-mode Control (MPSMC). Both control schemes are linked to a extended-state Linear Kalman Filter (ES-LKF) that furnishes the states and lumped disturbance estimates. Moreover, a high-fidelity simulation is presented in detail to validate the effectiveness of the proposed controller within a realistic scenario. We finally present the experimental stage to validate the tilting-rotor configuration as well as the integral MPC.

This paper reviews about the field of data science and the importance of it with the today's rapidly evolving landscape of technology. We start the survey by introducing the fundamental concepts of data science including history and how we collect data and why we need data science for and how we use them effectively to use them for processing, storing, and analyzing such as machine learning, data visualization. And we explore about the different domains we use data science and the challenges, advantage and disadvantages we face while using them. And finally, we are going to discuss about the prospects and implications of data science and how we can use data science to overcome the current and future challenges. The need of careers and the paths of careers are also discussed as they are essential and growing rapidly.

Distinguished from most hyperspectral Anomaly Detection(AD) methods based on trainable parameter networks, the recently proposed method called AETNet eliminates the need for parameter adjustments or retraining on new test scenes by training an anomaly enhancement network on background data with false anomalies. In this letter, we achieve this by proposing a novel training and inference framework that enhances the network's background spectral feature extraction capability without any data augmentation. During training on background data, the complete network is trained using the reverse distillation framework with a spectral feature alignment mechanism to improve the network's background feature expressiveness. For inference, a pruned network is applied, composed solely of components most relevant to expressing features in the spectral dimension. This effectively reduces redundant information, enhancing both inference efficiency and anomaly detection accuracy. Experimental results demonstrate that our method outperforms state-of-theart methods on the HAD100 dataset, striking an optimal balance between detection accuracy and inference speed. Our code is available at https://github.com/cristianoKaKa/FERD.

The application of millimeter-Wave (mmWave) Radar sensors for people monitoring raised a lot of interest in the context of Active Assisted Living (AAL), especially since the processing of Radar signals can provide interesting information about the observed subjects. Correct recognition of the ongoing behavior, however, cannot disregard from detecting where the subject is positioned. Detection approaches, based on Constant False Alarm Rate (CFAR) algorithms, sometimes fail to correctly identify the presence of targets within the observed scenario, especially in complex environments such as indoors. This paper proposes the use of a mmWave Multiple Input Multiple Output (MIMO) Radar in combination with a You Only Look Once (YOLO) neural network-based algorithm for the detection of moving people in indoor environments by processing all the data cube information at the same time. Results are validated through experimental tests which involve subjects walking in linear or random mode, different Radar configurations, and different indoor environments. By exploiting at the same time information such as the angle, Doppler, and range distance of the target, the proposed approach proves to be very effective in the examined scenarios. Experimental results will be discussed in this work to demonstrate the effectiveness of the proposed method.

Whereas robots are and will be increasingly present in all areas of society, robotics can be limited by its narrow perspective, often neglegecting discussions about its own goals, epistemology, ethics, and socio-political impacts. In order to try to adress this issue, this article proposes a holistic analysis of robots and robotics. Taking into these blind spots, raising new challenges. In response, the second part proposes a set of guidelines : a new epistemology, a metaethical framework, an organization method and a template design in order to answer these challenges.

In an era where the drumbeats of technological advancement echo through the corridors of military strategy, my research takes a deep dive into the storied pasts of military legends-General John J. Pershing, General George S. Patton Jr., and General Norman Schwarzkopf-to juxtapose their timeless strategies with the burgeoning field of Artificial Intelligence (AI) in warfare. This comparative analysis, crafted with the meticulousness of a New York Times feature, seeks to unravel the complex tapestry of leadership, tactical innovation, and the human element that defined the battlegrounds of the 20th century, and to critically examine how AI might redefine the very fabric of military operations in the future.

Sharing basic safety messages (BSMs) among connected vehicles (CVs) in a timely and reliable manner is of paramount importance in vehicular networks. When CVs are connected through ad hoc networks, the timely delivery of BSMs is very challenging due to the randomness in medium access control (MAC) and may lead to collision, especially in crowded networks. Besides, although the channel acquisition in conventional methods via transmission and reception of control signals results in collision-free message delivery, it adds high overhead cost. In this paper, we propose an efficient MAC scheme to carefully address these issues by improving communication efficiency and reducing the signaling overhead. The proposed scheme dedicates each time slot to only one CV and consequently is collision-free. Since BSMs contain similar information of a CV, we adopt the age of information (AoI) as the performance metric. We derive mathematical expressions for the MAC delay and AoI of the collision-free scheme by proposing a two dimensional Markov model. We compare the performance of proposed scheme with IEEE 802.11p standard and another lowcomplexity random scheme. AoI, delay, and collision rate are evaluated by OPNET network simulator, which provides realworld implementation scenario. Simulations results show that the collision-free scheme performs significantly better than IEEE 802.11p in highly congested networks. As an example, for a dense scenario where BSMs are generated every 10 ms, AoI of collisionfree scheme is about 50 ms while those of IEEE 802.11p and random scheme are about 140 and 150 ms, respectively, which are assumed too high for safety applications. Besides, the results show almost perfect match between mathematical derivations and results obtained by OPNET.

Incisive selection of the LCL filter parameters for a grid-connected inverter (GCI) is crucial to meet the grid interconnection standards with a reduced hardware footprint. Various design methods are available in the literature for selecting the LCL filter parameters. While the grid-side inductor of the LCL filter can utilize an iron core and follow the standard grid frequency inductor design, the inverter-side inductor design needs attention since it has significant switching frequency harmonics. This paper presents an extensive discussion on the design of the inverter-side inductor for GCIs. The inverter-side inductor (L i) is calculated based on the allowable inverter peak-peak ripple current to reduce the losses due to the ripple component. The value or size of L i depends on the inverter configuration, switching technique, and the application. The initial sections of the paper present a comprehensive analysis, comparing the value and hence the size of L i for different wiring configurations and applications. Closed-form expressions are developed for L i and are used in selecting the minimum value of L i. The suitability of an amorphous core for the inverter-side inductor is discussed. The amorphous-core inductor designs in literature can lead to a wide variation of inductance with current and have been analyzed to cause differential and common mode noise. To address this, a novel amorphous-core inductor design is proposed in the later sections of this work. The proposed approach ensures a minimal variation in the inductance over the operating current range. Experimental results are provided to support the various theoretical assertions.

A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.

The Internet of Things (IoT) enables billions of smart devices to capture, process, and transform data to improve decision-making. IoT demands a critical mobile edge computing (MEC) ecosystem to provide dependability. It requires an efficient and distributed architecture with multiple IoT communication protocols. In this way, intelligent middleware is needed to achieve the efficiency, throughput, and reliability of data delivery on different protocols without interference from the local setup of the device. This paper proposes a modular and interoperable middleware called MiddleFog to select the most appropriate communication protocol among MQTT and CoAP dynamically. Also, the approach minimizes communication limitations caused by latency, package loss, and low network throughput between MEC and Cloud. The initial evaluations show a message loss rate lower than 25% for small messages, and performance improves around 48% for medium-sized delivery messages.

The Collatz conjecture, a longstanding mathematical puzzle, posits that, regardless of the starting integer, iteratively applying a specific formula will eventually lead to the value 1. This paper introduces a novel approach to validate the Collatz conjecture by leveraging the binary representation of generated numbers. Each transition in the sequence is predetermined using the Collatz conjecture formula, yet the path of transitions is revealed to be intricate, involving alternating increases and decreases for each initial value. The study delves into the global flow of the sequence, investigating the behavior of the generated numbers as they progress toward the termination value of 1. The analysis utilizes the concept of probability to shed light on the complex dynamics of the Collatz conjecture. By incorporating probabilistic methods, this research aims to unravel the underlying patterns and tendencies that govern the convergence of the sequence. The findings contribute to a deeper understanding of the Collatz conjecture, offering insights into the inherent complexities of its trajectories. This work not only validates the conjecture through binary representation but also provides a probabilistic framework to elucidate the global flow of the sequence, enriching our comprehension of this enduring mathematical mystery.

Chemical process design is the search for an optimal manufacturing protocol to perform chemical operations. For transient processes such as crystallization, the optimal conditions can change over time, requiring a dynamic strategy. Model-free deep reinforcement learning is an approach that can be used to identify the best sequence of states with respect to a predefined reward function. In this work, proximal policy optimization is applied in a simulated environment to identify operational strategies that are optimal with respect to the desired particle properties in unseeded batch cooling crystallization processes of paracetamol in ethanol. For this purpose, the corresponding Markov decision process is formulated, and it is shown that the method is promising for the development of novel routes that allow the tuning of particle size (623 μm) and provide high yields (96%) within a defined period of time (12 h).

Motor unit (MU) decomposition, generally, requires a time-consuming and labor-intensive manual inspection/editing process from human operators to ensure high accuracy. In this study, we propose and validate a rule-based auto-editing method that could potentially substitute the manual process. Methods: The proposed auto-editing framework (autoeditor) consists of four main rules for adding and removing spikes based on the height of the innervation pulse train (IPT) and the regularity of the firing rate of the identified motor unit. The rules were optimized and validated based on an open-source database including raw MU spike trains estimated from the convolution kernel compensation method and the manually edited MU spike trains from eight human operators. Results: Across 110 motor units, the average rate of agreement between the auto-editor and human operators reached 99.2% after the auto-editor corrected more than 10 edits for each motor unit on average from the raw spike trains. More importantly, the characteristics of the motor unit behaviors, including the MU firing rate and recruitment threshold, were consistent across human operators and the proposed auto-editor. Conclusion: With a simple but effective rulebased auto-editing framework, comparable performance in MU refinement was achieved as human operators. Significance: The proposed auto-editing framework has the potential to standardize the MU editing practice, lower the requirements for expert knowledge and specialized training for MU decomposition, and provide an expandable framework allowing contributions from the community.

A document by Sourasekhar Banerjee. Click on the document to view its contents.