With the explosive growth of cloud computing over the last two decades, there has been a corresponding increase in data center power requirements to meet those needs. While there are numerous additional aspects of data center power management that could be explored from an environmental and facilities perspective, this research study will primarily focus on the cloud computing infrastructure. The objective of this focus will be on answering the research question “What methods are available to optimize power consumption in a cloud computing environment?”

This contribution aims to propose a high-level architecture for IEnMS that incorporates IoT and Big Data. This is necessary because although IoT and Big Data have been considered in home energy management systems (HEMS), their implementation and applications in IEnMS are not well studied.

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, Multi-access Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a huge amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.

The non-inertiality of the rotating system of Faraday's unipolar generator forces us to address this problem using the principles of the general theory of relativity. The purpose of this study is to compare the theoretical quantitative estimates of induced electromotive force with the experimental results obtained from the corresponding measurements. The theoretical elaboration of this issue proves that the differences between the results of the general theory in relation to those of the special theory of relativity are some negligible terms, which are due to the non-inertiality of the rotating reference system. This result enables us to consider the theoretical estimates based on special and general relativity as equivalent, with quite satisfactory accuracy. Therefore, a very serious issue of inconsistency between the theory of relativity and the measurements of induced electromotive force emerges, since as already shown in the existing literature, this inconsistency, from the point of view of the special theory of relativity, is already proven.

Fine-grained frequencies have been used in several recent works to enhance network throughput as well as combat Cross Technology Interference (CTI) issues in licence free ISM bands. We observe that synchronous communication based strategies, due to the scope of inter-frequency capture-effect, are inherently more capable of supporting in-parallel communication over multiple channels even when the Center Frequency Distance (CFD) of the channels are very low (<5 MHz). In this work, we pursue an in-depth study of how fine-grained frequencies can be used in conjunction with synchronous communication to extract the maximum benefit from a very narrow band of available frequencies (e.g., 2- 5 MHz) for in-parallel intra-group communication. In this direction, we propose a simple and efficient group formation strategy to automatically define groups in a given WSN/IoT network to boost up in-parallel intra-group communication efficiency. Through extensive experimental evaluations in existing WSN/IoT testbeds, we show that in-parallel one-to-all dissemination in the groups formed through the proposed strategy can execute with upto 73% higher reliability while consuming upto 41% lower energy as compared to the same running among the groups formed through naive strategy with only 4 consecutive frequencies separated by 1 MHz and upto 20 groups.

We develop foundations for the emerging field of quantum antennas using relativistic quantum field theory. We show that the concept of “antenna” goes beyond electromagnetic waves. Any quantum radiation can be treated within quantum antenna theory, not only electromagnetic or acoustic radiation that dominated the field so far.

Contour velocity estimation and tracking from a fully event-based perspective.

Unsupervised cross-modal retrieval has received increasing attention recently, because of the extreme difficulty of labeling the explosive multimedia data. The core challenge of it is how to measure the similarities between multi-modal data without label information. In previous works, various distance metrics are selected for measuring the similarities and predicting whether samples belong to the same class. However, these predictions are not always right. Unfortunately, even a few wrong predictions can undermine the final retrieval performance. To address this problem, in this paper, we categorize predictions as solid and soft ones based on their confidence. We further categorize samples as solid and soft ones based on the predictions. We propose that these two kinds of predictions and samples should be treated differently. Besides, we find that the absolute values of similarities can represent not only the similarity but also the confidence of the predictions. Thus, we first design an elegant dot product fusion strategy to obtain effective inter-modal similarities. Subsequently, utilizing these similarities, we propose a generalized and flexible weighted loss function where larger weights are assigned to solid samples to increase the retrieval performance, and smaller weights are assigned to soft samples to decrease the disturbance of wrong predictions. Despite less information is used, empirical studies show that the proposed approach achieves the state-of-the-art retrieval performance.

With compact footprint, low energy consumption, high scalability, and mass producibility, chip-scale integrated devices are an indispensable part of modern technological change and development. Recent advances intwo-dimensional (2D) layered materials with their unique structures and distinctive properties have motivated their on-chip integration, yielding a variety of functional devices with superior performance and new features. To realize integrated devices incorporating 2D materials, it requires a diverse range of device fabrication techniques, which are of fundamental importance to achieve good performance and high reproducibility. This paper reviews the state-of-art fabrication techniques for the on-chip integration of 2D materials. First, an overview of the material properties and on-chip applications of 2D materials is provided. Second, different approaches used for integrating 2D materials on chips are comprehensively reviewed, which are categorized into material synthesis, on-chip transfer, film patterning, and property tuning / modification. Third, the methods for integrating 2D van der Waals heterostructures are also discussed and summarized. Finally, the current challenges and future perspectives are highlighted.

A document by Sen Zhan . Click on the document to view its contents.

Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper therefore proposes the Gaussian clouded logit adjustment by Gaussian perturbing different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in the classifier, we accordingly propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method.

Existing convolutional neural networks (CNNs) have achieved significant performance on various real-life tasks, but a large number of parameters in convolutional layers requires huge storage and computation resources which makes it difficult to deploy CNNs on memory-constraint embedded devices. In this paper, we propose a novel compression method that generates the convolution filters in each layer by combining a set of learnable low-dimensional binary filter bases. The proposed method designs more compact convolution filters by stacking the linear combinations of these filter bases. Because of binary filters, the compact filters can be represented using less number of bits so that the network can be highly compressed. Furthermore, we explore the sparsity of coefficient through L1-ball projection when conducting linear combination to avoid overfitting. In addition, we analyze the compression performance of the proposed method in detail. Evaluations on four benchmark datasets under VGG-16 and ResNet-18 structures show that the proposed method can achieve a higher compression ratio with comparable accuracy compared with the existing state-of-the-art filter decomposition and network quantization methods.

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the “ad hoc agent”) must successfully collaborate with a set of unknown agents (the “teammates”) without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not in agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a decision-theoretic module that is responsible for all task-related decision making (task identification, teammate identification, and planning). Second, a communication module that uses natural language processing in order to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.

Kinematic redundancy in robots makes it possible to execute several control tasks simultaneously. As some tasks are usually more important than others, it is reasonable to dynamically decouple them in order to ensure their execution in a hierarchical way or even without any interference at all. The most widely used technique is to decouple the system by feedback linearization. However, that requires to actively shape the inertia and consequently modify the natural dynamics of the robot. Here we propose a passivity-based multi-task tracking controller that preserves these inertial properties but fully compensates for task-space cross-couplings using external force feedback. Additionally, three formal proofs are provided: uniform exponential stability for trajectory tracking, passivity during physical interaction, and input-to-state-stability. The controller is validated in simulations and experiments and directly compared with the hierarchical PD+ approach and the feedback linearization. The proposed approach is well suited for safe physical human-robot interaction and dynamic trajectory tracking if measurements or estimations of the external forces are available.

Inter satellite link (ISL) is an effective way for the global navigation satellite system (GNSS) to reduce its dependence on ground infrastructure, which guarantees constellation orbit determination and satellite communication. When the number of onboard Ka-band ISL antennas is less than that of visible satellites, the inter-satellite link assignment of GNSS causes a problem. For the problem of inter-satellite link scheduling, considering that the result of the allocated link has a feedback effect on the subsequent link assignment as a priori knowledge, an adaptive topology optimization algorithm based on signed variance (ABSV) is proposed. In order to meet the requirements of communication and ranging performance, the time slot is divided into a communication time slot and a ranging time slot. Taking the waiting delay time of satellite communication and PDOP as measurement indexes, the proposed strategy is simulated for 10080 min. The results show that the ranging performance of this strategy is better than other recently published methods, which verifies the effectiveness of signed variance for adaptive link planning and is also beneficial to the survivability of constellation.

A non-contact passive intermodulation distortion (PIM) measurement method for antenna using high-level IM standard (HIMS) is described as proposed herein. A novel method to estimate coupling loss between sensing antenna and a sample antenna is proposed, after which a complete one-port non-contact measurement for antenna is realized. Measurements are conducted in three stages: measuring IM characteristics of HIMS, estimating coupling loss using HIMS, and estimating practical PIM level of a sample antenna. A high-level IM standard consisting of a Schottky barrier diode was used for this study. The method enables ready evaluation of practical PIM of a sample antenna from a complete one-port non-contact measurement with excellent stability and repeatability. Then the validity of the proposed method was confirmed experimentally using a printed dipole antenna. After presenting an applicable measurement method for array antenna incorporating the proposed method, its effectiveness was verified experimentally.

Children are the dream of parents. Children ADHD is a bygone and chronic disorder which leads to problems in children. If not solved in childhood stages will continue in future till adolescents. The disorder consequences are difficulty to study the tasks which are related to anxiety, depression and other psychological problems. Hence the disorder must be resolved in the early stage to control any type of consequences in future for our children. The medical field is an eminent area in today's world such as signal processing, Imaging, MRI, EEG etc. to diagnose and offer treatment. Even technology field too contributing to ADHD children by providing different techniques in different areas such as IoT, mobile, Robot, Application, virtual reality, augmented reality, machine learning techniques etc. to give diagnosis and treatment methods. The paper reviews and summarizes the set of features, diagnosis methods, treatment rules for ADHD children.

The semiconductor industry is heavily relying on outsourcing of design, fabrication, and testing to third parties. The threat of possibly malicious actors in this ramified supply-chain poses a risk for the integrity of integrated circuits (ICs) and hardware Trojans (HTs) are a heavily discussed topic in academia and the industry. A variety of pre- and post-silicon HT prevention and detection techniques has been suggested in prior works. Hardware reverse engineering has the potential to detect potential modification in physical layouts. Yet, there is no model to qualitatively and quantitatively rate the complex and expensive reverse engineering (RE) process addressing its inherent process aberrations and consequently provide a tool for layout verification. The ViTaL framework introduces a statistical validation technique, based on physical layout verification through RE and considers all potential sources of errors. The golden-model based framework is technology-agnostic, scaleable, and user input is optional. For the first time, results of fine pitch metallization layers of a CMOS 40nm process node IC are presented quantitatively and the limitations and possibilities are discussed.