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Made available in DSpace on 2022-04-29T08:31:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas Agulhas Negras Military Academy [Academia Militar das Agulhas Negras] Rodovia Presidente Dutra, km 306, s/n Escola Preparatória de Cadetes do Exército Avenida Papa Pio XII 350 Jardim Chapadão Campinas - SP Brazil, Avenida Papa Pio XII, 350, Jardim Chapadão, Campinas - SP Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas

Edible oils form an integral component in the human diet as well as in the industrial preparations. Any adulteration in oil is concerning from nutritional as well as economical perspective. Although analytical techniques exist for detection of adulterants in oil, most of them require sophisticated instruments, and trained professionals. Microfluidic based lab-on-a-chip technologies have rapidly progressed over the recent years and offer promising alternatives in this regard. In this work, we report development of a paper-based microfluidic lab-on-a-chip for detection of palm oil contamination in sunflower oil. We use the paper-based microfluidic colorimetric detection approach that indicates the presence of adulteration in a visual manner. The data is analyzed using digitally acquired images along with ultraviolet–visible spectrophotometry. The paper-based microfluidic device is fabricated and tested for 10–50% (v/v) palm oil adulteration in sunflower oil. Further, we employ the device for free fatty acid content tests in oil samples. Visual signal acquired from the device is optimized by varying phenolphthalein proportions. The stability of visual colorimetric signal is examined over time and is found to remain steady over the trial period of six days considered in this work. The integration of the detection device with smartphone imaging can prove to be useful for onsite data collection. We believe the approach presented here can be extended to food quality monitoring portable platforms.

The waste of paper or the paper dump is a serious problem and its occurrence is widespread in various institutions. Wastepaper can be recycled to produce some important and usable products that are ecological and biodegradable. For example, it is used to extract new paper from the old by chemical decomposition. Other products such as paper plates, paper cups, paper spoons, tiles, toys, crafts, etc. can be formed from the slurry. This project also initiates a mould that makes cups and plates from the wastepaper management circuit. With learning institutions being the main consumers, most paper are usually eliminated after its use. The waste remains undeveloped and unused, although is a valuable resource. Studying the design of a manual, economical and efficient paper recycling machine, it can be surmised that the design uses the integration of the knowledge gained about the recycling technology that exists manually from the paper recycling machines used to form a cheap but effective paper recycling system. The advantages of the machine are not only of recycled paper, but also the advantages of the interaction of the manual drive system, which will also reduce the high cost. As the design is not 100% effective, the transmission, belt and chain transmission correspond to the estimated 90% efficiency using the 90% for the design. The power consumption for the design is 450 W and since an average user can produce 100 W constant, it takes 5 people to drive the machine.

Advancements in technology have led to the use of smartphones as biosensor detectors. However, it is difficult to obtain stable color information of colorimetric sensors using multiple smartphones owing to different light conditions and image correction operations. In this study, we attempted color detection of the colorimetric paper chip using smartphone-embedded light-emitting diode (LED) and simple transformation calculation. This method enables the acquisition of stable color information using different smartphones by reducing the influence of external light sources. Moreover, we studied characteristics such as convergence and distinction of detection result details by the manufacturers and smartphones. These findings suggested the necessity of classification criteria for the use of smartphones to achieve optimal detection. In addition, white and black references were used to set the standard for color correction of various light sources. Based on our results, we conclude that this approach presents the possibility of smartphone-based colorimetric detection in practical applications.

The interest in and use of microfluidic paper-based analytical devices (μPADs) has grown exponentially over the last decade. Cellulose, and modified cellulose, has been used for centuries for making chemical measurements but devices made with this material traditionally suffered from either poor detection limits and/or limited ability to provide quantitative measurements. μPADs address these problems by patterning paper to create microfluidic channel networks that can direct flow to different regions of the device without the need for external pumps used in most traditional microfluidic devices. Furthermore, because the devices are made from cellulose or modified cellulose using inexpensive manufacturing methods, the devices can be inexpensive and disposable. The ability to carry out multiplexed analysis without external pumps using inexpensive, disposable devices makes μPADs ideal for point-of-care (POC) analysis. The interest in μPADs has led to a number of excellent comprehensive reviews in the field; in this review, we do not attempt to reference the entire field but instead seek to highlight major developments as well as areas that will be important for future development of this field.

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

Paper recycling is the process of turning waste paper into new paper products. The main objective of the current research work is to analyze the effectiveness of stainless steel based compact paper recycling machines for small scale industries and institutions such as schools, colleges, printing media, etc. At present paper recycling technology is being implemented in large scale industries as it requires enormous space and cost involved. In the current study, an attempt is made to design a compact recycling machine that is affordable for small scale industries and institutions to possess their paper recycling units. The mechanism comprises of hydro pulper, mould box, conveyors, rollers, and driers. It is capable of recycling the used papers and produce papers, cardboards, and egg trays. The thickness of the paper is controlled by setting the distance between the two finishing rollers using a spring set up. The process of recycling is broadly divided into three main categories that include collection of waste, manufacturing of new products, and purchasing. The main advantage of the machine is waste paper be converted into a useful product and thus reduce deforestation. The cost of recycling is reduced and is affordable by small industries. The simplicity of the operation of this machine ensures that a high level of technical skill is not essential for the process. The durability of the machine is high provided if adequately maintained. Since recycling is good for the environment, and if more and more people make use of it, it will address the problem of global warming.

At present, the tissue paper manufacturing is mostly based on the dry crepe technology. During the last decade, the manufacturers have introduced new tissue machines concepts that increase the softness, bulk, and absorption capacity. Such machines produce a strong regular three-dimensional (3D) structure to the sheet before the Yankee cylinder. At present, the quality of the 3D structure is not evaluated, or it is evaluated only subjectively at the mill. This is mostly because of the difficulties to separate reliably the regular 3D pattern from other variations. This paper introduces a frequency analysis based method which separates the surface profile variances in tissue paper to the creping, to the regular 3D pattern and to the residual variation. The 3D surface profiles and their variances were determined online with the photometric stereo method. We show that the introduced analysis method evaluates the variance portions reliably and the results are consistent with the visual perception of the 3D surfaces. In one particular product, the regular 3D pattern explains 74 % of total surface variance; the creping explains 10 % and residual variations 16 %. Furthermore, the creping and residual variances are quite stable over time whereas the variance of the regular 3D pattern fluctuates significantly.

International audience; Despite predictions of the paperless office, global demand for printing and writing paper remains strong, and paper appears to be here to stay for some time. Not only firms, but also governments, libraries, and archives are in possession of large collections of legacy documents that still must be sorted and scanned. In this study, terahertz-based techniques are demonstrated to address several routine tasks related to the automated paper handling of unsorted legacy documents. Specifically, we demonstrate terahertz-based counting of the number of sheets in unconsolidated paper stacks, as well as locating stapled documents buried in paper stacks.

In this paper, we study the state-dependent Gaussian wiretap channel with action encoder and noncausal state at the transmitter, which is also called the action-dependent dirty paper wiretap channel. Two lower bounds on the secrecy capacity of this model are provided, and we find that the two bounds have their advantages and disadvantages, i.e., none of the two always performs better than the other. In addition, we find that taking action on the state helps to increase the achievable secrecy rate of the dirty paper wiretap channel.

In the last decade, much effort has been invested in using methanol as an oil-soluble chemical marker for assessing the condition of insulating paper. The use of this marker as paper life index presents many advantages particularly with new transformers insulated with thermally upgraded papers. However, until now, no interpretation model has been available for its extensive use by the transformer community. In this paper, a methanol-based interpretation model is presented for the first time using postmortem paper analysis on core-type transformers. This model allows the evaluation of the average degree of polymerization of a transformer's cellulose winding. Furthermore, threshold values based on the methanol concentration are given using this approach. Finally, model validation was performed on a limited number of papers taken from transformers being assessed in the repair shop.

Rewinding machines are used in the paper converting industrial sector to unroll paper veils from large source reels and wind them around smaller rolls, for the production of paper logs to be used in household activities. Many different types of rewinding machine exist, and they are mostly based on stapling devices which automatically unroll a large coil of paper, roll it up around a new log, stop the veil allowing it to be torn off, unload the completed paper log, and reload a new paper core to be rolled up. Winding units generally include a primary and a secondary roller, supplying the desired rotational speed for winding, and a pressure unit consisting of a pressure roller in contact with the paper log being rolled up, which assures adequate containment and avoids the paper log to escape. All known systems have some mechanical limitations, mainly due to the contact of the pressure roller with the paper log, causing instabilities and vibrations, particularly when working with soft paper veils. This limits the maximum possible winding speed. The aim of this paper is it to present a detailed kinematic analysis of a novel pressure unit which overcomes the aforementioned limitation.

Traction transformer is vital equipment in high-speed railway. Insulation status decides its safety and reliability, and the frequency-domain dielectric spectrum (FDS) test is one of the most effective methods reflecting the changing of insulation status. For field applications, the following problems should be addressed: 1) how to obtain the result of paper insulation from the combined result of the oil–paper insulation system and 2) how to discriminate the defects of insulation paper between aging and damp. In this article, the first problem was transferred to a nonlinear equation set, and a cuckoo search algorithm optimized by the differential evolution algorithm and the quadratic interpolation (QI) method (CS-DQ algorithm) was proposed to solve it. Then, the insulation states were discriminated by establishing a multiclass least-squares support vector machine (LS-SVM) model, in which the CS-DQ algorithm was also used. Finally, a diagnostic approach for the insulation paper in the traction transformer was proposed. The results in the laboratory show that the pure result of insulation paper can be obtained, and the insulation states can be discriminated effectively by using the proposed approach. Meanwhile, the proposed CS-DQ algorithm has a better performance than the conventional CS algorithm. The results of field testing also verify the proposed approach.

International audience

The clustering coefficient has been widely used for identifying the local structure of networks. In this paper, the weighted spectral distribution with 3-cycle (WSD3) that is similar (but not equal) to the clustering coefficient is studied on evolving social networks. It is demonstrated that the ratio of the WSD3 to the network size (i.e., the node number) provides a more sensitive discrimination for the size-independent local structure of social networks in contrast to the clustering coefficient. Moreover, the difference of the WSD3’s performances on social networks and communication networks is investigated, and it is found that the difference is induced by the different symmetrical features of the normalized Laplacian spectral densities on these networks.

Agriculture is the important aspect of country economy. Farmers employ laborers for various purposes of works to be carried out on their farm. One of the important works is to make a ridge and furrow in their agricultural fields. In general, farmers use tractors, plowing machines, and other manual methods for making ridges and furrows in the agricultural field which increases the cost of operation to produce ridges and furrows. Further, the manual method of producing ridges and furrows needs more labor and takes more number of days to complete the work. Work. It also needs a lot of physical work. [1] . Plowing machines are costly and use power. This machine is highly economical and efficient which would replace the existing manual method of removal of furrows. Further, the machine is operated by freely available solar energy. The new machine is capable of reducing the physical work and also speed up the removal of wastages along the furrow. This furrow formation machine is mainly designed and fabricated to dug the tunnel and to form the furrows along the boundary of the land and along both the sides of the crops.

The aim of this study is to design a fully automatic Sundry board machine for reduction of congestion in the cardboard making process and increase the quality and quantity of production. For this purpose, the manual cutting and transfer of sheet is replaced by automated mechanism. The mechanisms include the incorporation of the cutting arrangement in the cutting drum itself, deployment of air pressure to separate the sheet edge from the cutting drum, a cross cutting mechanism and a mechatronic system to control these all mechanism. The initial review is done on the semi-automatic machine followed by the requirement definition. The required components are brainstormed and individually developed. A time based relation is designed such that a microcontroller can be programmed later on for the synchronism of all the components. Thisrelation is visually represented and the entire system is now ready for assembling.

This paper presents an innovative method devised to study some of the harmful effects of moisture on power transformers, considering the possible formation of bubbles and free water. Its novelty stems from the fact that it comprises: (a) a rating system from “A” to “E” and their respective recommended actions and (b) an analysis that involves varying the different levels of electrical loads applied to the equipment. The importance of this work is confirmed when power transformers are exposed to unfavorable conditions that can critically affect their insulation. To this end, a tool to study moisture was developed, which uses the finite difference method, based not only on the experience of experts/analysts but also on existing studies in the area and on the analysis of a database of actual field tests. The contribution of this work is an efficient method, implemented via a computational algorithm, which provides a technique for the real-time or offline rating of power transformers, helping maintenance teams to diagnose problems and decide whether or not to repair the equipment. The tool provided the satisfactory results, which are presented in a case study of a real transformer operating at the local electric utility company. These results were obtained by applying different levels of electrical loads, for which thermal simulations were performed.

Power networks, especially distribution networks, have been undergoing substantial changes since the application of new technologies. Technology development in the early part of the 21st century has opened up new horizons for automated, efficient and reliable power grids. New technologies, while enhancing the capability of the electrical networks and providing opportunities and innovative solutions to the challenges of future networks, can also cause drawbacks that should be investigated and taken into account. Distributed generation (DG) is one of the new technologies that improves the operation of power grids. Despite tangible benefits that integration of DG units brings to electrical grids, their notable impacts on protection systems of power networks raise many challenges and concerns on how a fault should be detected and isolated in active distribution networks. Many attempts have been made to investigate the downside of the interconnection of DG units and methods to mitigate their impacts have been proposed. This study reviews the impact of DG integration on protection systems addressed in other research works and recapitulates suggested methods provided by scholars.

In this research, a security paper based on frequency selective structure technologies was designed and fabricated using selective wave reflection characteristics to prevent the offline leakage of confidential documents. Document leakage detection systems using security papers detect security papers using transceiving antenna gates. For the application of such systems, the structure must be designed with excellent reflection performance and stability at the angle of incidence. For this purpose, a loop and patch-type frequency selective structure based on a four-legged element structure was designed to have X-band frequency reflection characteristics. This design was based on optimized variables and was realized through the screen printing method using silver ink on A4 paper. It was verified that both the design and simulation results matched well. To verify its actual applicability, a detector module operable at 10 GHz was manufactured to observe both the security paper detection range in relation to distance with a signal strength of &minus, 10 dBm and the detection area in relation to the number of times that the security paper had been folded.

Due to the long-term operation, the transformer oil-paper insulation system will gradually experience aging and degrading, resulting in the insulation failure. And the insufficient research about the accurate diagnosing method of aging condition will severely threaten the security and stability of the transformer. At present, the diagnostic criteria for the aging of transformer insulation is only limited to the degree of polymerization of the insulating paper/pressboard and furfural in the oil without a comprehensive diagnostic scheme. Based on the existing theory, for the oil-paper insulation system after undergoing the accelerated thermal aging test, the degree of polymerization of the insulation paper, frequency domain dielectric properties, furfural, CO, CO 2 were firstly tested in the laboratory. Then, principal component analysis (PCA) was used to integrate the measured multiple parameters and obtain a comprehensive evaluating index of the insulation aging condition, and the establishment of the degree of polymerization and the evaluating index was conducted, which was in good linear relationship. According to the evaluating index and the quantitative relationship with the degree of polymerization, a diagnostic method that can be applied to the aging assessment of transformer insulation in the field is put forward, which makes up for the deficiency of the current diagnosis method only including single assessment factor and is of certain practical significance.

In this study mainly concentrated on the automation method to produce new equipment. The Paper punching machine is one of the important equipment for to make holes for spiral binding process. The existing mechanism is a difficult one to form a hole based on the increasing of paper count, much more human effort is also needed. This study and design is to improve the equipment in the sense of automation, the automation help to improve spiral binding works with produce bulky spiral binding in the short period and also reduce the human effort. In this study replacing of pedal operated mechanism and introduces electric motor with drive mechanism, reduction gear box and cam mechanism.

The service life of a transformer depends on the ageing degree of the internal oil–paper insulation. In order to reliably predict the remaining service life of oil–paper insulation for a single transformer, this study proposes a method based on monitoring the performance degradation data of oil–paper insulation. In this study, the remaining life prediction model of oil–paper insulation is established according to the Wiener stochastic process. The parameters of the model are iteratively updated by using the Bayesian rule and maximum expectation algorithm. The objective of remaining service life prediction of transformer oil–paper insulation is achieved. In the laboratory, multiple groups of oil–paper insulation samples undergo an ageing test at 130°C. Certain amount of performance degradation data is obtained by continuously monitoring the oil–paper insulation. Along with the prediction method of remaining life, the accuracy of the remaining life prediction of transformer oil–impregnated paper is verified when the model parameters are updated. This is a significant guiding factor for predicting the remaining life through the performance degradation data of the continuously operating transformer.

An original partial discharge generated in oil insulation identification methodology based on simultaneously conducted measurements using electrical method, ultra high frequency method and acoustic emission method is presented in the paper. Three different partial discharge model sources as well as measuring instruments commonly applied for partial discharge detection in electrical power transformers are yielded within a laboratory research. Total of 45 scenarios, including proposed spark gap configurations, selected supply voltage levels and UHF frequencies are analyzed during measurements series. Furthermore, form among total of 93 descriptors assigned for every applied partial discharge model source configuration there are 24 proposed as potentially useful for partial discharge identification applications with their 95% confidence bounds. Attempt of discriminative descriptors selection for partial discharge source analysis in on-site transformer applications as well as a proposal of unique descriptors according to every selected spark gap configuration that could be potentially useful for partial discharge identification purposes are the main purpose of the presented paper. The proposed methodology verification on a real life transformer with particular consideration of the selected descriptors potential utility in the fields of partial discharge detection and identification in electrical power industry applications confirmed a proposed methodology usefulness.

As Machine Learning (ML) becomes pervasive in the era of artificial intelligence, ML specific tools and frameworks are required for architectural research. This paper introduces NeuroMeter, an integrated power, area, and timing modeling framework for ML accelerators. NeuroMeter models the detailed architecture of ML accelerators and generates a fast and accurate estimation on power, area, and chip timing. Meanwhile, it also enables the runtime analysis of system-level performance and efficiency when the runtime activity factors are provided. NeuroMeter’s micro-architecture model includes fundamental components of ML accelerators, including systolic array based tensor units (TU), reduction trees (RT), and 1D vector units (VU). NeuroMeter has accurate modeling results, with the average power and area estimation errors below 10% and 17% respectively when validated against TPU-v1, TPU-v2, and Eyeriss.Leveraging the NeuroMeter’s new capabilities on architecting manycore ML accelerators, this paper presents the first in-depth study on the design space and tradeoffs of “Brawny and Wimpy” inference accelerators in datacenter scenarios with the insights that are otherwise difficult to discover without NeuroMeter. Our study shows that brawny designs with 64x64 systolic arrays are the most performant and efficient for inference tasks in the 28nm datacenter architectural space with a 500mm2 die area budget. Our study also reveals important tradeoffs between performance and efficiency. For datacenter accelerators with low batch inference, a small $(\sim 16$%) sacrifice of system performance (in achieved Tera OPerations per Second, aka TOPS) can lead to more than a 2x efficiency improvement (in achieved TOPS/TCO). To showcase NeuroMeter’s capability to model a wide range of diverse ML accelerator architectures, we also conduct a followon mini-case study on implications of sparsity on different ML accelerators, demonstrating wimpier accelerator architectures benefit more readily from sparsity processing despite their lower achievable raw energy efficiency.

In this invited paper, applications for Machine Learning (ML) in several areas of semiconductor manufacturing and test are reviewed and potential opportunities are discussed.

The recent Meltdown and Spectre attacks took the community by surprise. Rather than exploiting an incorrect implementation of the ISA, these attacks leverage the undocumented implementation-specific speculation behavior of high-performance microarchitectures to affect the extra-architectural state of the machine (e.g., caches).Inspired by these novel speculation-based attacks, we argue it is time to rethink the traditional ISA layers. Programmers and security professionals need a framework to reason about the effects of speculation and other microarchitectural performance optimizations. We propose judiciously extending the ISA to include the extra-architectural state so that an ISA implementation either completely squashes all system state changes caused by mis-speculated instructions or the potential changes are rigorously documented. We hope this new framework will give architects and security researchers tools to reduce the likelihood of future surprise vulnerabilities.

A summary of contributions made by significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented. The 27 papers chosen represent those which have most strongly influenced theory and practice in the field.

Folded paper has a long history in origami artwork and is used by engineers to rapidly prototype designs of adaptive structures and mechanical metamaterials. However, engineers should be cautious when using paper to investigate the mechanical properties of origami structures. We show that the non-linear, pseudo-plastic behaviour of paper folds complicates the modelling of the mechanical properties of an origami structure. However, this pseudo-plastic behaviour could also present a new design space to tailor the mechanical properties of origami by controlling the rest angles of the folds.

Anti-counterfeiting technique is a research hotspot in information security domain. Common techniques are usually based on artificial physical cloneable feature which is forgeable. In this paper, we proposed an anti-counterfeit method based on microscopic translucent texture of paper (MTTP). MTTP is image of internal fiber pattern of paper (IFPP) captured by microscope under translucent light source. The IFPP has property of uniqueness and randomness, and the fiber scale of IFPP is at sub-micron or micron level. Then the IFPP is unable to be forged. Furthermore, fiber texture in IFPP is stable under disturbances (including illumination intensity change and surface damage), which guarantees the stability of the MTTP. Experiment results show that the authentication accuracy achieve 100% in our dataset (totally 13,4550 pairs of MTTPs) and the false reject rate achieves the order of magnitude of 10-136 in ideal condition and 10-59 even in condition of illumination change or surface damage. Above results indicate that our method has rather high security and reliability.

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe. A critical challenge to our—and any—autonomous machine is accurate and efficient localization under resource constraints, which has fueled specialized localization accelerators recently. Prior acceleration efforts are point solutions in that they each specialize for a specific localization algorithm. In real-world commercial deployments, however, autonomous machines routinely operate under different environments and no single localization algorithm fits all the environments. Simply stacking together point solutions not only leads to cost and power budget overrun, but also results in an overly complicated software stack.This paper demonstrates our new software-hardware co-designed framework for autonomous machine localization, which adapts to different operating scenarios by fusing fundamental algorithmic primitives. Through characterizing the software framework, we identify ideal acceleration candidates that contribute significantly to the end-to-end latency and/or latency variation. We show how to co-design a hardware accelerator to systematically exploit the parallelisms, locality, and common building blocks inherent in the localization framework. We build, deploy, and evaluate an FPGA prototype on our next-generation self-driving cars. To demonstrate the flexibility of our framework, we also instantiate another FPGA prototype targeting drones, which represent mobile autonomous machines. We achieve about $2 \times$ speedup and $4 \times$ energy reduction compared to widely-deployed, optimized implementations on general-purpose platforms.

Several network growth models have been proposed in the literature that attempt to incorporate properties of citation networks. Generally, these models aim at retaining the degree distribution observed in real-world networks. In this work, we explore whether existing network growth models can realize the diversity in citation growth exhibited by individual papers – a new node-centric property observed recently in citation networks across multiple domains of research. We theoretically and empirically show that the network growth models which are solely based on degree and/or intrinsic fitness cannot realize certain temporal growth behaviors that are observed in real-world citation networks. To this end, we propose two new growth models that localize the influence of papers through an appropriate attachment mechanism. Experimental results on the real-world citation networks of Computer Science and Physics domains show that our proposed models can better explain the temporal behavior of citation networks than existing models.

Two-dimensional (2D) materials represent an emerging technology for transistor electronics in view of their attractive electrical and mechanical properties. This work investigates molybdenum disulfide (MoS2) based field-effect transistors (FETs) fabricated on paper substrate for designing both digital and analog circuits, such as inverter, current mirror, and Physical Unclonable Function (PUF) bitcell. Electrical measurements of fabricated devices are exploited to setup a look-up-table (LUT)-based Verilog-A model to be integrated in a commercial circuit simulator. Obtained results prove the potential of paper-based MoS2 FETs as building blocks of next-generation integrated circuits for a wide range of practical applications.

By integrating both energy harvesting and backscatter communication technologies, so-called ‘battery-free tag’ emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals.At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

While there are several frameworks for CNN inference on mobile GPUs, they do not achieve real-time processing for the most of the CNNs that aim at reasonable accuracy since they all employ kernel-by-kernel execution model and do not effectively support INT8 quantization yet. In this paper, we reveal that mobile GPUs suffer from large kernel launch overhead unlike server GPUs, and then propose an on-device deep learning inference framework that can achieve real-time inference of CNNs on mobile GPUs by removing kernel launch overhead and by effectively exploiting INT8 quantization. We have evaluated the proposed framework with a state-of-the-art CNN based face detector (RetinaFace), and observed up to 2.01X of speedup compared to ARM Compute Library (ACL) on a commodity smartphone.

Value Prediction was proposed to speculatively break true data dependencies, thereby allowing Out of Order (OOO) processors to achieve higher instruction level parallelism (ILP) and gain performance. State-of-the-art value predictors try to maximize the number of instructions that can be value predicted, with the belief that a higher coverage will unlock more ILP and increase performance. Unfortunately, this comes at increased complexity with implementations that require multiple different types of value predictors working in tandem, incurring substantial area and power cost. In this paper we motivate towards lower coverage, but focused, value prediction. Instead of aggressively increasing the coverage of value prediction, at the cost of higher area and power, we motivate refocusing value prediction as a mechanism to achieve an early execution of instructions that frequently create performance bottlenecks in the OOO processor. Since we do not aim for high coverage, our implementation is light-weight, needing just 1.2 KB of storage. Simulation results on 60 diverse workloads show that we deliver 3.3% performance gain over a baseline similar to the Intel Skylake processor. This performance gain increases substantially to 8.6% when we simulate a futuristic up-scaled version of Skylake. In contrast, for the same storage, state-of-the-art value predictors deliver a much lower speedup of 1.7% and 4.7% respectively. Notably, our proposal is similar to these predictors in performance, even when they are given nearly eight times the storage and have 60% more prediction coverage than our solution.

The low carrier mobility of (printed) semiconductors and high variations of the printed/flexible electronic elements are some of the most difficult challenges for the practical realization of complex printed/flexible electronic circuits. Although contemporary circuit designs accommodate some degree of process variations in conventional silicon processes largely by negative feedback, negative feedback is largely inapplicable in printed/flexible electronics. This is because the gain of the transistors is too low in electronic circuits to realize high open-loop gain, but also because negative feedback is inapplicable to digital circuits in the traditional sense. In this paper, we describe the co-design between the first three supply chains of Printed/Flexible Electronics – the co-design between our modified semiconductor, Fully-Additive Low-Temperature All-Air Low-Variation Printed/Flexible Electronics printing process and a process-variation-tolerant digital circuit design methodology. The first two co-design supply chains pertain to a low-cost screen-printing process while the last co-design chain pertains to the Quasi-Delay-Insensitive asynchronous-logic digital design methodology (vis-a-vis synchronous-logic) that is self-timed, hence virtually tolerant to any variations. We will also delineate our measurements on digital circuit based on codesign of the aforesaid supply chains, depicting the merits of our modified semiconductor, low-variation printing process, and the ensuing functional printed digital circuit.

The Uplink Wavefront Corrector System (UWCS) is a pathfinder instrument to demonstrate the uplink correction by Adaptive Optics techniques; this novel application can be directly usable in two fields: Free-Space Optical Communications and the generation of Laser Guide Stars. A Rayleigh LGS is propagated to the sky while the atmospheric wavefront aberrations are measured by a Shack-Hartmann WFS with 12 x 12 sub-apertures using a Natural Guide Star as a reference. The laser upwards propagation path is then pre-compensated by a 97-actuator deformable mirror. A scoring camera is attached to the finder telescope, next to the main aperture, in order to show the overall result, which is assessed in terms of beam power concentration. Present paper described the design process of the UWCS and its integration and testing in the Optical Ground Station telescope, at Teide Observatory (Spain).

With fast switching GaN any parasitic gate loop inductance degrades the switching performance and may lead to false turn-on as well as gate voltage overshoot. Two approaches to overcome these challenges in driving GaN transistors are discussed in this paper. In a discrete silicon based driver, the gate loop inductance is actively utilized for a resonant gate drive approach. In a second implementation, the gate loop inductance is reduced close to zero by GaN-on-Si monolithic integration of the power transistor and the driver on one die. It includes an integrated supply voltage regulator circuit that generates the gate drive voltage out of the high-voltage switching node. The results show fast and robust switching behavior with minimal ringing.

Physically unclonable function (PUF) is a technology to generate a device-unique identifier using process variation. PUF enables a cryptographic key that appears only when the chip is active, providing an efficient countermeasure against reverse-engineering attacks. In this paper, we explore the data conversion that digitizes a physical quantity representing PUF’s uniqueness into a numerical value as a new attack surface. We focus on time-to-digital converter (TDC) that converts time duration into a numerical value. We show the first signal injection attack on a TDC by manipulating its clock, and verify it through experiments on an off-the-shelf TDC chip. Then, we show how to leverage the attack to reveal a secret key protected by a PUF that uses a TDC for digitization.

Next generation PON physical layer, targeting 50 Gbit/s/lambda, has to deal with optoelectronics bandwidth limitation. In this invited paper, we review the resulting required bandwidths and discuss the trade-off between receivers with or without equalization.

This talk will review our recent work on a high-speed avalanche photodiode (APD). Recent results show 100-Gbit/s PAM4 40-km transmission with receiver sensitivity exceeding -14 dBm when the APD is combined with in-house SOA-integrated EML.

This Special Issue presents a collection of articles addressing the contemporary challenges in the areas of the ship energy systems modelling and optimisation [...]

Photonic integrated circuit (PIC), as a promising alternative to traditional CMOS circuit, has demonstrated the potential to accomplish on-chip optical signal transmission and computations in ultra-high speed and/or low power consumption. One of the critical challenges of PIC, however, is that its scalability and robustness are limited by cascaded optical power loss and noise error. In this paper, we analyze the scalability and noise robustness challenges facing photonic integrated circuits, for two representative PIC applications: logic computing and neural networks. Automated design algorithms and learning methodologies are proposed to resolve these issues.

Electrowetting-on-dielectric (EWOD) is a mechanism that allows physical handling of liquids with only electrical signals, such as digitizing a liquid into tiny droplets and manipulating them on a chip, thus enabling “digital microfluidics”. As an elegantly simple platform free of pumps or valves, EWOD digital microfluidics has been attracting high research interest in the past two decades and has recently been transitioned to a few commercial products in displays and biochemistry. However, the number of labs utilizing this technology is still small, due to the difficulty in translating design intent to manufactured devices. Accordingly, in this paper, we propose a cloud-based open-source EWOD cybermanufacturing ecosystem that enables an automatic translation from user requirements to manufactured digital microfluidics. The incubation cyber ecosystem is to be accessible to a wide range of end users, allowing researchers, entrepreneurs, students, and hobbyists alike to focus on their own ideas and applications without having to master the subtleties of EWOD engineering and manufacturing. This can be thought of as an “operating system” for EWOD community, similar to Windows and macOS for people with no computer hardware background. The proposed cyber ecosystem aims to ease the process of designing and eliminate the burden of fabricating EWOD chips so that the user pool is increased and more applications found for EWOD digital microfluidics.

Micro aerial vehicle (MAV) gradually gains plentiful attention in recent years. However, it is still a full challenge to design MAVs with high aerodynamic performance because aerodynamic efficiency of wings deteriorates sharply at low Reynolds numbers. Under this circumstance, we focus on one unconventional configuration with annular wing, which can be folded from a square paper. The annular paperplane configuration is described with geometric parameters, and the longitudinal and lateral aerodynamic characteristics are studied with RANS method.

This paper investigates duobinary signals and their applications in non-orthogonal multi-carrier systems to enhance spectral efficiency. In duobinary transmission schemes, the signal spectrum is reshaped by introducing controlled correlation, which can be eliminated at the duobinary decoder. For the first time, we propose the idea of combining duobinary transmission technique and the fast orthogonal frequency division multiplexing (Fast-OFDM) system with three subcarriers, with experimental results presented. The proposed system is capable of achieving three times the data rate of single-carrier ASK scheme with the same bandwidth. Results show that bit error rate (BER) performance of the proposed duobinary-based Fast-OFDM system is slightly worse than the ASK system. In addition, we also tested various 3-PSK constellation patterns designed for the duobinary signal to achieve performance improvement.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

This paper outlines the importance of gaining a good knowledge of the underwater channel. The channel is discussed in terms of absorption and scattering with an explanation of the processes involved in both. Then a review of the various techniques used to model these processes is presented. Some results obtained via a Monte-Carlo simulation of the channel are presented and discussed, showing how channels with similar parameters but different scattering conditions can have very different responses.