Piezoelectric touch sensing in interactive displays gains increasing attentions due to its high force detection sensitivity and intrinsic mechanical-to-electrical conversion ability. However, the instable force-voltage responsivity induced by preload effect of piezoelectric materials reduces the force detection accuracy of secondary force touches, which is important to touch and haptic applications such as peek-pop. To address this issue, in this article, we present a preload effect elimination technique, in which the relationship between the piezoelectric coefficients and static preload is studied first, and then the detected secondary force touch is calibrated by using the previously applied static force information. Experimental results demonstrate that the force detection accuracy is boosted by 15.17% after applying the developed technique to secondary force touches with different preload values, potentially allowing the system to precisely interpret secondary force touch amplitude and hence enhancing the development of touch sensing in interactive displays.

Influence of High Pressure EGR System in reducing Nitrogen Oxide

A document by Stephen Ripley . Click on the document to view its contents.

ARM processors have taken over the mobile industry from a long time now. Future of data centers and the IT industry is estimated to make use of ARM Processors. Projects like Openstack on ARM are enabling use of ARM in data centers . Single board computers (SBCs) based on ARM processors have become the norm these days. Reason for their popularity lies in their cost effective and power efficient nature. There are hundreds of them available in the market having different sizes, compute power and prices. The reason for their popularity is largely due to the rise of new technology called IoT (Internet of Things) but there is another perspective where they can become handy. Low Price and Power Usage of single board computers makes them top candidate to be used for teaching many courses with hands-on experience in developing countries like Pakistan. Many boards support full Linux distributions and can be used as general-purpose computers while many of them are open hardware based. In this paper, we have reviewed the famous options available and tried to figure out which of them are better for teaching what kind of courses

Our novel disruptive deployment strategy focuses on using cost-efficient modules along with aggregated connectivity that target 6G data throughput and capacity requirement. From our perspective it would be a great benefit to operators and users. Cost effectiveness will be met through integration and volume. Indeed, cost efficiency would be derived from mass-market of sub-6GHz devices (already mainly COTS) and later toward mmW such as 5G-FR2 and near-THz. Our shared vision provides a great perspective for ubiquitous modularity and pervasive networks which are clear and challenging objectives of 6G roadmaps. Authors propose a vision and are grateful for open discussions.

In this work, the existence of a correspondence between fundamental programming language control structures and mathematical formulation is proven. The proposed interpretation is given through a well-defined logical circuit analytical expression. Relevant geometrical applications having wide implications in engineering branches are presented together with a new Penalty Method for constrained optimization problems handling.

This manuscript reports on a robot called Clio that we developed to facilitate basin-scale studies of ocean microbial communities and their biochemistry, to better understand how marine microorganisms regulate ocean and Earth system environmental cycles. Clio is designed to facilitate global-scale studies of ocean biochemistry, to move vertically through the water column with high precision, and specifically to return sensor data and samples from large swaths of the ocean ranging in depths from the surface to 6,000 m. Clio is capable of flexible, precise vertical motion that few other ocean robots can perform, and none to our knowledge over this depth range. We tested Clio extensively over several years, six cruises, and 26 dives, it is now fully operational and this manuscript describes all that we did to convince ourselves this was so. In June 2019, it completed its first large-scale ocean survey, and for which this manuscript will be the first data presentation.

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically.

We propose a combination of the Partial Element Equivalent Circuit method with metamodelling in order to achieve fast tolerance analysis of electromagnetic systems. The proposed model combination can be interpreted as a multifidelity modelling approach. This technique is inspired by the Multilevel Monte Carlo method and provides great benefits in terms of computational resources.

Following recent advancements in Terahertz (THz) technology, THz communications are currently being celebrated as key enablers for various applications in future generations of communication networks. While typical communication use cases are over medium-range air interfaces, the inherently small beamwidths and transceiver footprints at THz frequencies support nano-communication paradigms. In particular, the use of the THz band for in-body and on-body communications has been gaining attention recently. By exploiting the accurate THz sensing and imaging capabilities, body-centric THz biomedical applications can transcend the limitations of molecular, acoustic, and radio-frequency solutions. In this paper, we study the use of the THz band for body-centric networks, by surveying works on THz device technologies, channel and noise modeling, modulation schemes, and networking topologies. We also promote THz sensing and imaging applications in the healthcare sector, especially for detecting zootonic viruses such as Coronavirus. We present several open research problems for body-centric THz networks.

The aim of this study is to propose an information-theoretic framework for compressed sensing (CS)-independent component analysis (ICA) algorithms that can be used for the joint recovery of sparse biosignals. The proposed framework supports real-time patient monitoring systems that enhance the detection, tracking, and monitoring of vital signs remotely via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we present a new analysis of CS-ICA algorithms from an information theory perspective to compute the sampling rate required to recover sparse biosignals corrupted by motion artifacts and interference, which to the best of our knowledge, has not been studied before. Our analysis and examples indicate that the proposed approach helps to develop low-cost, low-power edge computing devices while improving data quality and accuracy for a given measurement. We also show that, under noisy measurement conditions, the CS-ICA algorithm can outperform the standard CS method, where a biosignal can be retrieved in only a few measurements. By implementing the sensing framework, the error in reconstructing biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.

The emerging technologies leveraging Data Center Networks (DCN) and their consequent traffic patterns impose more necessity for improving Quality of Service (QoS). In this paper, we propose Sieve, a new distributed SDN framework that efficiently schedules flows based on the available bandwidth to improve Flow Completion Time (FCT) of mice flows. In addition, we propose a lightweight sampling mechanism to sample a portion of flows. In particular, Sieve schedules the sampled flows, and it reschedules only elephant flows upon threshold hits. Furthermore, our framework allocates a portion of the flows to ECMP, so that the associated overhead can be mitigated in the control plane and ECMP-related packet collisions are fewer as well. Mininet has been used to evaluate the proposed solution, and Sieve provides better FCT up to 50% in comparison to the existing solutions like ECMP and Hedera.

Mitigation of substrate coupling effects in RF switch by localized substrate removal using laser processing

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic.

This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem A) through ever-changing network conditions (problem B) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem A|B). We introduce two stochastic models to create sequences of QoS-constrained messages (A) and to create ever-changing network conditions (B). In sequence, we sketch a control loop to shape A to B to test our hypothesis using model A|B, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems A, B and A|B.

We experimentally demonstrate integrated waveguide and micro-ring resonator polarizers incorporating two-dimensional layered graphene oxide films, with a high polarization dependent loss of 53.8 dB and a high polarization extinction ratio of 8.3 dB, respectively.

Version 2 of the paper “Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey”