Question: A big data viewpoint and obstacles in next generation network systems.

From the Vantage Point of the Clouds: A Berkeley Perspective on Cloud Computing
Envisioned as the ultimate IT utility, Cloud Computing holds the potential to radically reshape significant segments of the technology industry. It stands to make software services even more appealing and influence the design and procurement of IT hardware. Innovators with fresh ideas for internet services no longer face the daunting financial barriers associated with hardware investment or the operational costs of service deployment. They no longer need to worry about overestimating the demand for their service, resulting in wasted resources, or underestimating popularity, potentially alienating customers and missing out on revenue. Additionally, companies with substantial batch processing requirements can achieve faster results without incurring additional costs for scaling up, as the cost of using a thousand servers for an hour is equivalent to utilizing one server for a thousand hours. This unprecedented scalability without a premium for large-scale operations marks a transformative moment in IT history.

Cloud Computing encompasses not only the internet-delivered applications as services but also the hardware and systems software infrastructure in data centers that enable these services. These services, often referred to as Software as a Service (SaaS), are supported by what we term a Cloud, referring specifically to the data center hardware and software. A Public Cloud, available on a pay-as-you-go basis to the general public, offers Utility Computing services. Conversely, an organization's internal data center, not accessible to the public, is called a Private Cloud. Therefore, Cloud Computing is the amalgamation of SaaS and Utility Computing, excluding Private Clouds.

Individuals or organizations can be users or providers of SaaS or Utility Computing services. Our focus is on SaaS Providers (Cloud Users) and Cloud Providers, areas that have received less scrutiny compared to SaaS Users. In terms of hardware, Cloud Computing introduces three novel dimensions:

This perspective highlights the transformative potential of Cloud Computing, emphasizing its utility in making IT resources more accessible and cost-effective, particularly for developers and businesses with variable or unpredictable service demands. The discussion also delineates the distinction between Public and Private Clouds, underscoring the role of Cloud Providers and SaaS Providers in this evolving landscape.

In this paper, we apply the concept of a software-defined data plane to defining new services for mobile virtual network operators (MVNOs) to empower them with the capability of tailoring fine-grained subscription plans that can meet users' demands. For this purpose, we have recently proposed the concept of application-and/ or device-specific slicing that classifies application-and/or device-specific traffic into slices and introduced various applications of our proposed system [Optical Fiber Communication Conf. (OFC), 2016, paper Th3I.4]. This paper reports the prototype implementation of the proposal into a real MVNO connecting customized smartphones so we can identify applications/devices from the given traffic with 100% accuracy. We first characterize the traffic patterns of popular applications to build a detailed understanding of how network resources are utilized for real users and applications and what their demands on different network resources are. Then, we classify traffic according to the devices and find that the flow characteristics of LTE RAN slices are very similar to those of 3G. We discover that most of the bandwidth is consumed by those flows with the duration of several hundreds of seconds, and the flow size is larger than 5 Mbytes in both LTE and 3G MVNO networks.

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

Large-scale hybrid networks that include wireless, wired, and satellite based communications are becoming common in both military and commercial situations. This paper describes a scalable simulation environment called GloMoSim (for Global Mobile Information System Simulator) that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. The paper also presents a set of case studies that evaluate the performance of large wireless networks with thousands of nodes and compares the impact of different lower layer protocols on the performance of typical applications.

The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization

This paper discusses novel communication network topologies and components and describes an evolutionary path of bringing Ethernet into automotive applications with focus on electric mobility. For next generation in-vehicle networking, the automotive industry identified Ethernet as a promising candidate besides CAN and FlexRay. Ethernet is an IEEE standard and is broadly used in consumer and industry domains. It will bring a number of changes for the design and management of in-vehicle networks and provides significant re-use of components, software, and tools. Ethernet is intended to connect inside the vehicle high-speed communication requiring sub-systems like Advanced Driver Assistant Systems (ADAS), navigation and positioning, multimedia, and connectivity systems. For hybrid (HEVs) or electric vehicles (EVs), Ethernet will be a powerful part of the communication architecture layer that enables the link between the vehicle electronics and the Internet where the vehicle is a part of a typical Internet of Things (IoT) application. Using Ethernet for vehicle connectivity will effectively manage the huge amount of data to be transferred between the outside world and the vehicle through vehicle-to-x (V2V and V2I or V2I+I) communication systems and cloud-based services for advanced energy management solutions. Ethernet is an enabling technology for introducing advanced features into the automotive domain and needs further optimizations in terms of scalability, cost, power, and electrical robustness in order to be adopted and widely used by the industry.

In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84 * 10^-5 false positive ratio. It outperforms the other failure prediction methods for computers and ISP devices.

The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.

Network Functions Virtualisation (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating Network Functions (NFs) from traditional middleboxes, NFV is expected to lead to reduced CAPEX and OPEX, and to more agile services. However, one of the main challenges to achieving these objectives is on how physical resources can be efficiently, autonomously, and dynamically allocated to Virtualised Network Functions (VNFs) whose resource requirements ebb and flow. In this paper, we propose a Graph Neural Network (GNN)-based algorithm which exploits Virtual Network Function Forwarding Graph (VNF-FG) topology information to predict future resource requirements for each Virtual Network Function Component (VNFC). The topology information of each VNFC is derived from combining its past resource utilisation as well as the modelled effect on the same from VNFCs in its neighbourhood. Our proposal has been evaluated using a deployment of a virtualised IP Multimedia Subsystem (IMS), and real VoIP traffic traces, with results showing an average prediction accuracy of 90%. Moreover, compared to a scenario where resources are allocated manually and/or statically, our proposal reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.

The use of a data network in a control loop has gained increasing attentions in recent years due to its cost effective and flexible applications. One of the major challenges in this so-called networked control system (NCS) is the network-induced delay effect in the control loop. Network delays degrade the NCS control performance and destabilize the system. A significant emphasis has been on developing control methodologies to handle the network delay effect in NCS. This survey paper presents recent NCS control methodologies. The overview on NCS structures and description of network delays including characteristics and effects are also covered. r 2003 Published by Elsevier Ltd.

Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.


Question: Initial showcase of 28 GHz and 39 GHz transmission lines and antennas on glass substrates for 5G module applications.

This paper outlines the advancement in silicon-based automotive radar systems for the 76-81 GHz frequency range. The journey from SiGe bare-die chips in 2009 to the present availability of packaged monolithic microwave integrated circuits (MMICs) for cost-effective radar applications is detailed. The future of SiGe BiCMOS technology promises the development of highly integrated single-chip radars, offering enhanced performance with reduced power consumption. This technological leap will significantly contribute to the democratization of automotive radar safety, making it accessible to a broader audience and marking a crucial milestone in the path towards autonomous driving.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
The paper presents an innovative evolutionary recurrent network design that utilizes a novel evolutionary learning algorithm, a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), termed HGAPSO. HGAPSO employs crossover, mutation, and PSO to generate the next generation of individuals, with the concept of elite strategy being central to its approach. The best-performing individuals are enhanced through PSO, simulating natural maturation. These enhanced elites form half of the next generation, while the other half is created through crossover and mutation operations on the enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design, with a fully connected recurrent neural network designed for a temporal sequence production problem and a Takagi-Sugeno-Kang-type recurrent fuzzy network for dynamic plant control. Comparative analysis demonstrates HGAPSO's superior performance in these network design challenges.

**Similar Documents**: The paper introduces a groundbreaking evolutionary learning algorithm, HGAPSO, which integrates genetic algorithm (GA) and particle swarm optimization (PSO) for the automated design of recurrent neural/fuzzy networks. HGAPSO innovates by employing crossover, mutation, and PSO in the creation of the next generation, focusing on the enhancement of the top-performing individuals through PSO, mirroring the process of natural maturation. These refined elites make up half of the next generation, while the remaining individuals are generated through crossover and mutation on the enhanced elites. HGAPSO's application to recurrent neural/fuzzy network design is exemplified through a fully connected recurrent neural network for temporal sequence production and a Takagi-Sugeno-Kang-type recurrent fuzzy network for dynamic plant control. Comparative studies confirm HGAPSO's superior performance in addressing recurrent network design issues.

A novel design of dual broadband high gain substrate integrated waveguide (SIW) cavity backed slot array antenna is described. The SIW cavity is design in dominant mode TE10. The proposed antenna is design by two pair of λg/2-resonators cavity backed slot on broad wall of single layer substrate integrated waveguide. To place two pair unequal slot length in stair nature on broad-wall of SIW cavity are responsible to broadening the bandwidth at two different frequency band and also due to arraying nature of slot enhanced the gain. The proposed antenna have Lower frequency bandwidth 4.4% (9.58 GHz–10.02 GHz) and higher frequency bandwidth is 9.49% (10.84 GHz–11.92 GHz) with 10 dB gain at lower band and 9.4 dB gain at higher band. The proposed antenna has high gain, directive radiation field with high FTBR.

A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.

Although all existing air-filled substrate integrated waveguide (AFSIW) topologies yield a substrate-independent electrical performance, they rely on dedicated, expensive, laminates to form air-filled regions that contain the electromagnetic fields. This paper proposes a novel substrate-independent AFSIW manufacturing technology, enabling straightforward integration of high-performance microwave components into a wide range of general-purpose commercially available surface materials by means of standard additive (3-D printing) or subtractive (computer numerically controlled milling/laser cutting) manufacturing processes. First, an analytical formula is derived for the effective permittivity and loss tangent of the AFSIW waveguide. This allows the designer to reduce substrate losses to levels typically encountered in high-frequency laminates. Then, several microwave components are designed and fabricated. Measurements of multiple AFSIW waveguides and a four-way power divider/combiner, both relying on a new coaxial-to-air-filled SIW transition, prove that this novel approach yields microwave components suitable for direct integration into everyday surfaces, with low insertion loss, and excellent matching and isolation over the entire [5.15–5.85] GHz band. Hence, this innovative approach paves the way for a new generation of cost-effective, high-performance, and invisibly integrated smart surface systems that efficiently exploit the area and the materials available in everyday objects.

A planer micro-strip to waveguide transition structure is given in this paper. Coupled patch on the bottom of the substrate is excited by the micro-strip feed-line on the top, which realizes the transformation from the TE10 mode in the waveguide to the quasi-TEM mode on the micro-strip line. Modeling and simulation of the structure are conducted for the design by using electromagnetic simulation software HFSS. The measurement of the manufactured structure exhibits an insertion loss less than 0.5dB within 33GHz~37GHz. Compared to traditional waveguide E-plane probe or H-plane probe transition, this design has properties of low profile and easy integration.

Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.

We evaluate the possibility of using a finline orthomode transducer (OMT) at millimeter wavelengths. A finline OMT has low loss, low cross-polarization, and good return loss over a full waveguide band. We propose a novel finline OMT structure for millimeter-wavelengths and present results at X-band.

Left-hand and right-hand circularly polarized (LHCP and RHCP) substrate integrated waveguide (SIW) antenna arrays are presented at the 28-GHz band for millimeter-wave (mm-wave) applications. Two types of circularly polarized (CP) antenna elements are designed to achieve respective LHCP and RHCP performance. Eight-element LHCP and RHCP antenna arrays have been implemented with feeding networks and measured. Based on the measurement, the LHCP and RHCP antenna arrays have impedance bandwidths of 1.54 and 1.7 GHz within $| S_{\rm{11}}| < - \text{10 dB}$, whereas 3-dB axial-ratio bandwidths are 1.1 and 1.3 GHz, respectively. Each of the fabricated LHCP and RHCP antenna arrays is accomplished with a gain up to 13.09 and 13.52 dBi. Most of the measured results are validated with the simulated ones. The proposed CP antenna arrays can provide low-cost, broadband characteristics, and high-gain radiation performance with CP properties for mm-wave applications.

The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ0/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.

In this paper an innovative antenna system for car2car applications is described. The antenna system is based on an active antenna module containing the antennas as well as the complete car2car chip hardware and software. This module is very small, so that it can be integrated e.g. into a shark fin antenna on the cars rooftop. Moreover additional modules can be mounted somewhere else in the car and connected with the main module via Ethernet. This allows high flexibility and re-configurability of the system. Two test cars have been equipped with the new car2car demonstrator systems and measurements have been performed in the 3D measurement chamber as well as in dynamic outdoor scenarios. The new system shows excellent performance in measurements and field trials.

A dual-band textile antenna for multiple-input-multiple-output (MIMO) applications, based on substrate-integrated waveguide (SIW) technology, is designed. The fundamental SIW cavity mode is designed to resonate at 2.4 GHz. Meanwhile, the second and third modes are modified and combined by careful placement of a via within the cavity to enable wideband coverage in the 5-GHz WLAN band. The simple antenna topology can be fabricated fully using textiles in a planar form, ensuring reliability and comfort. Numerical and experimental results indicate satisfactory antenna performance when worn on body in terms of impedance bandwidth, radiation efficiency, and specific absorption ratio (SAR). In order to validate its potential for MIMO applications, two elements of the proposed SIW antenna are arranged in six configurations to study the performance in terms of mutual coupling and envelope correlation. It is observed that the placement of the shorted edges of the two elements adjacent to each other produces the lowest mutual coupling and consequently the best envelope correlation.


Question: Initial showcase of 28 GHz and 39 GHz transmission lines and antennas on glass substrates for 5G module applications.

This paper explores the advantages and obstacles in the development of versatile, scalable phased arrays at millimeter wave frequencies, specifically in the W-band, targeting applications in imaging and communications. It begins by outlining the potential uses for phased arrays featuring from dozens to hundreds of elements. Subsequently, it evaluates the current methodologies for scaling silicon-based phased arrays across the microwave to terahertz frequency spectrum. A critical analysis is conducted on the difficulties and compromises inherent in various integration strategies for W-band phased arrays, with an emphasis on packaging intricacies and antenna performance. The paper then presents a viable approach using SiGe ICs and organic packaging to construct a 64-element dual-polarized phased array at 94 GHz, accompanied by pertinent measurement outcomes. This research highlights the first demonstration of 28 GHz and 39 GHz transmission lines and antennas on glass substrates, setting a new precedent for the development of 5G modules. The use of glass substrates offers a promising pathway for the miniaturization and performance enhancement of phased arrays at these high frequencies, critical for next-generation wireless communication systems.

This paper gives a detailed description of a >1.3-Tb/s VCSEL-based on-board parallel-optical transceiver module for high-density optical interconnects. The optical module integrates a 28-Gb/s × 24-channel transmitter and receiver into one package with a 1-in2 footprint, thereby yielding a data rate density as high as 1.34 Tb/s/in2. A unique module design is developed to utilize the whole top surface for thermal dissipation and the bottom surface for optical and electrical interfaces. The heat dissipation characteristics are studied in simulations and experiments. A test system of 24-channel optical loop-back link is built to evaluate the performance when operating all 24 channels by 28.05-Gb/s 231–1 PRBS bit streams simultaneously. With a total power consumption of 9.1 W, the optical module can be operated within the operating case temperature under a practical air cooling condition. When operating all 24 channels simultaneously, a total jitter margin at BER of 10–12 is larger than 0.4 U.I. at a monitor channel. The measured jitter margin is consistent regardless of any channel operation.

The demonstration of multi-Gb/s links in the 60GHz band has created new opportunities for wireless communications [1,2]. Due to the directional nature of millimeter-wave (mm-Wave) propagation, beam steering enables longer-range non-line-of-sight (NLOS) links at these frequencies. A phased-array architecture is attractive for an integrated 60GHz transmitter (Tx) since it can attain both beam steering and higher EIRP through spatial combining. An all-RF 16-element 40-to-45GHz Tx for satellite applications [3], a 6-element 60GHz Tx with IF-path phase-shift [4], and a bi-directional 4-element 60GHz Tx/Rx with RF phase shifters [5] have been recently demonstrated in silicon. This work presents a fully-integrated phased-array Tx which supports multi-Gb/s NLOS IEEE 802.15.3c links. In addition to beamsteering, the IC has the following major features: an on-chip power sensor at each element, 3 temperature sensors, LO leakage and I/Q phase and amplitude adjustment, front-end OP1dB programmability, and an integrated modulator for pi/2-BPSK/MSK signaling (common mode in 802.15.3c). The IC integrates 2240 NPNs, 323,000 FETs and hundreds of transmission lines and is fabricated in the IBM 8HP 0.12µm SiGe BiCMOS process (fT = 200GHz).

Two Vivaldi antenna arrays have been presented. First is an 8-element tapered slot array covering 1.2 to 4 GHz band for STW applications for brick/concrete wall imaging. Second is a 16-element antipodal array operating at 8 to 10.6 GHz for high resolution imaging when penetrating through dry wall. Based on the two designs, and utilizing a smooth wide band slot to microstrip transition to feed the Vivaldi antenna array, a 1–10 GHz frequency band can be covered. Alternatively, the design can be used in a reconfigurable structure to cover either a 1–3 GHz or 8–10 GHz band. Experimental and measured results have been completed and will be discussed in detail. The designs will significantly impact the development of compact reconfigurable and portable systems.

High Q and low TCF are elemental characteristics for SAW resonator, in order to realize high performance filters and duplexers coping with new LTE frequency bands. Authors focus on the acoustic energy confinement in SAW propagation surface and electrical characteristics have been investigated theoretically and experimentally. A new multi-layered structure substrate have realized incredible high performances such as both three times Q and one fifth TCF compared with conventional structure. Band25 duplexer using the new structure has been developed successfully with low loss and good temperature stability.

In this work the realization of a 77-GHz frequency-division multiple-access-based frequency-modulated continuous-wave multiple-input multiple-output radar with four transceiver channels in conjunction with a non-uniform linear antenna array is presented. The radar system, consisting of an RF-frontend utilizes transceiver chips with integrated inphase/quadrature-modulators to generate the frequency shifted transmit signals and a field programmable gate array-based baseband board, is used for test measurements to verify the system performance and to demonstrate the beamforming capability as well as the accuracy of the digital-beamforming method.

The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ0/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.

Liquid crystal polymer (LCP) is a material that has gained attention as a potential high-performance microwave substrate and packaging material. This investigation uses several methods to determine the electrical properties of LCP for millimeter-wave frequencies. Microstrip ring resonators and cavity resonators are measured in order to characterize the dielectric constant (/spl epsi//sub r/) and loss tangent (tan/spl delta/) of LCP above 30 GHz. The measured dielectric constant is shown to be steady near 3.16, and the loss tangent stays below 0.0049. In addition, various transmission lines are fabricated on different LCP substrate thicknesses and the loss characteristics are given in decibels per centimeter from 2 to 110 GHz. Peak transmission-line losses at 110 GHz vary between 0.88-2.55 dB/cm, depending on the line type and geometry. These results show, for the first time, that LCP has excellent dielectric properties for applications extending through millimeter-wave frequencies.

A high gain, high-efficiency antipodal tapered slot antenna with sine-shaped corrugation and a Fermi profile substrate cut-out has been developed for 5G millimeter wave (MMW) communications. A parametric study of a new substrate cutout with Fermi profile is demonstrated to reduce the sidelobe level at the E-plane and H-plane as well as to increase antenna efficiency by an average of 88% over a band of 20-40 GHz. A low-cost printed circuit board (PCB) is processed simply with a CNC Milling machine to fabricate the proposed antenna with Fermi profile substrate cut-out. The measured reflection coefficient is found to be less than -14 dB over a frequency range of 20-40 GHz. Furthermore, the measured gain of the proposed antenna is 17 dB at 30 GHz and the measured radiation pattern and gain is almost constant within the wide bandwidth from 30-40 GHz. Therefore, this antenna is proposed for use in an H-plane array structure such as for point-to-point communication systems, a switched-beam system.

An eleven-stage broadband SIW filter has been designed, fabricated and measured. The measured results show that the SIW filter can be operated from 9.25 GHz to 11.25 GHz with good selectivity performance. The filter has a small size, low weight, and can be integrated with other SIW components without any transitions. It's suitable for the design of microwave and millimeter wave integrated circuits

We developed 1.3 μm, EML module built in EML driver IC for both 50 and 56 Gbit/s. 40-km SMF transmission with clear eye openings under 56 Gbit/s operation were demonstrated for the first time.


Question: Initial showcase of 28 GHz and 39 GHz transmission lines and antennas on glass substrates for 5G module applications.

This article highlights the development of a 77-GHz automotive radar receiver, ingeniously encapsulated within a wafer-level package, thereby obviating the necessity for wire bonding and significantly slashing production costs. The Silicon-Germanium (SiGe) technology employed in this design showcases its prowess in high integration capabilities, enabling the incorporation of in-system monitoring for receiver conversion parameters. This feature is pivotal in achieving ISO 26262 compliance, ensuring the radar sensors are suitable for critical automotive safety applications, thereby enhancing reliability and performance in the realm of vehicular radar systems.

**Question**: A Study on the Impact of Renewable Energy on the Grid Stability in Microgrids

**Documents**: A new approach for voltage stability improvement using a coordinated control of D-STATCOM and UPFC

In this paper, a new approach for voltage stability improvement in power systems is presented. The proposed approach involves the coordinated control of two custom power devices, Distribution Static Compensator (D-STATCOM) and Unified Power Flow Controller (UPFC). The control strategy is based on the minimization of the voltage stability index (VSI) using a Particle Swarm Optimization (PSO) algorithm. The performance of the proposed control strategy is evaluated through simulations on a test system. The results show that the coordinated control of D-STATCOM and UPFC can significantly improve the voltage stability of the power system under different operating conditions.

**Similar Documents**: This paper introduces an innovative method aimed at enhancing voltage stability in power systems, specifically through the harmonized operation of two advanced power devices: the Distribution Static Compensator (D-STATCOM) and the Unified Power Flow Controller (UPFC). The core of this approach lies in the optimization of the voltage stability index (VSI) via the application of a Particle Swarm Optimization (PSO) algorithm. The efficacy of the proposed control strategy is rigorously assessed through simulations conducted on a representative test system. The findings reveal that the synergistic control of D-STATCOM and UPFC markedly bolsters voltage stability across various operational scenarios, underscoring its potential for improving grid reliability and efficiency.

A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.

This paper demonstrates a 16-element phased-array transmitter in a standard 0.18-mum SiGe BiCMOS technology for Q-band satellite applications. The transmitter array is based on the all-RF architecture with 4-bit RF phase shifters and a corporate-feed network. A 1:2 active divider and two 1:8 passive tee-junction dividers constitute the corporate-feed network, and three-dimensional shielded transmission-lines are used for the passive divider to minimize area. All signals are processed differentially inside the chip except for the input and output interfaces. The phased-array transmitter results in a 12.5 dB of average power gain per channel at 42.5 GHz with a 3-dB gain bandwidth of 39.9-45.6 GHz. The RMS gain variation is < 1.3 dB and the RMS phase variation is < for all 4-bit phase states at 35-50 GHz. The measured input and output return losses are < -10 dB at 36.6-50 GHz, and <-10 dB at 37.6-50 GHz, respectively. The measured peak-to-peak group delay variation is plusmn 20 ps at 40-45 GHz. The output P-1dB is -5plusmn1.5 dBm and the maximum saturated output power is - 2.5plusmn1.5 dBm per channel at 42.5 GHz. The transmitter shows <1.8 dB of RMS gain mismatch and < 7deg of RMS phase mismatch between the 16 different channels over all phase states. A - 30 dB worst-case port-to-port coupling is measured between adjacent channels at 30-50 GHz, and the measured RMS gain and phase disturbances due to the inter-channel coupling are < 0.15 dB and < 1deg, respectively, at 35-50 GHz. All measurements are obtained without any on-chip calibration. The chip consumes 720 mA from a 5 V supply voltage and the chip size is 2.6times3.2 mm2.

This talk will present our latest work on silicon RFICs for phased-array applications with emphasis on very large chips with built-in-self-test capabilities for 5G systems. SiGe is shown to be ideal for mm-wave applications due to its high temperature performance (automotive radars, base-stations, defense systems, etc.) and lower power consumption. These chips drastically reduce the cost of microwave and millimeter-wave phased arrays by combining many elements on the same chip, together with digital control and some cases, high-efficiency antennas. The phased-array chips also result in an easier packaging scheme using either a multi-layer PCB or wafer-level packages. We believe that this family of chips will be essential for millimeter-wave 5G communication systems.

An X-band Single-Chip monolithic microwave integrated circuit (MMIC) has been developed by using a European GaN HEMT technology. The very compact MMIC die occupying only an area of 3.0 mm × 3.0 mm, integrates a high power amplifier (HPA), a low-noise amplifier (LNA) and a robust asymmetrical absorptive/reflective SPDT switch. At the antenna RF pad in the frequency range from 8.6 to 11.2 GHz, nearly 8 W of output power and 22 dB of linear gain were measured when operated in transmit mode. When operated in receive mode, a noise figure of 2.5 dB with a gain of 15 dB were measured at the Rx RF pad in the same frequency range.

In this paper, we demonstrate the fabrication and characterization of various 3D solenoid inductors using a glass core substrate. Solenoid inductors were fabricated in glass by drilling through holes in glass and semi-additive copper plating for metallization. This topology is compared to similar solenoid structures in terms of Q-factor performance and inductance density. Inductances of 1.8-4.5nH with Q ~ 60 at 1GHz were demonstrated.

A corporate feed slotted waveguide array antenna with broadband characteristics in term of gain in the 350 GHz band is achieved by measurement for the first time. The etching accuracy for thin laminated plates of the diffusion bonding process with conventional chemical etching is limited to ±20μm. This limits the use of this process for antenna fabrication in the submillimeter wave band where the fabrication tolerances are very severe. To improve the etching accuracy of the thin laminated plates, a new fabrication process has been developed. Each silicon wafer is etched by DRIE (deep reactive ion etcher) and is plated by gold on the surface. This new fabrication process provides better fabrication tolerances about ±5 μm using wafer bond aligner. The thin laminated wafers are then bonded with the diffusion bonding process under high temperature and high pressure. To validate the proposed antenna concepts, an antenna prototype has been designed and fabricated in the 350 GHz band. The 3dB-down gain bandwidth is about 44.6 GHz by this silicon process while it was about 15GHz by the conventional process using metal plates in measurement.

Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.

A patch antenna array proximity-coupled fed by the inset dielectric waveguide (IDW) is proposed in 60-GHz frequency band. The antenna array consists of four series-fed subarrays each having eight pairs of circular patch elements, four substrate integrated IDWs, and a compact four-way power divider transmitting the signal from the grounded coplanar waveguide (GCPW) to the IDWs. The IDW is realized by metallized via-hole arrays in a printed circuit board (PCB). The parallel center-fed configuration is applied to suppress the frequency-dependent beam squinting of the series-fed subarray. Measured results show that the proposed antenna exhibits a gain variation between 15.8 and 19.2 dBi, fixed main beam in the direction of θ = 0°, and the reflection coefficient |S11| ≤ -10 dB over the frequency band from 58 to 64 GHz.

Next-generation mobile technology (5G) aims to provide an improved experience through higher data-rates, lower latency, and improved link robustness. Millimeter-wave phased arrays offer a path to support multiple users at high data-rates using high-bandwidth directional links between the base station and mobile devices. To realize this vision, a phased-array-based pico-cell must support a large number of precisely controlled beams, yet be compact and power efficient. These system goals have significant mm-wave radio interface implications, including scalability of the RFIC+antenna-array solution, increase in the number of concurrent beams by supporting dual polarization, precise beam steering, and high output power without sacrificing TX power efficiency. Packaged Si-based phased arrays [1–3] with nonconcurrent dual-polarized TX and RX operation [2,3], concurrent dual-polarized RX operation [3] and multi-IC scaling [3,4] have been demonstrated. However, support for concurrent dual-polarized operation in both RX and TX remains unaddressed, and high output power comes at the cost of power consumption, cooling complexity and increased size. The RFIC reported here addresses these challenges. It supports concurrent and independent dual-polarized operation in TX and RX modes, and is compatible with a volume-efficient, scaled, antenna-in-package array. A new TX/RX switch at the shared antenna interface enables high output power without sacrificing TX efficiency, and a t-line-based phase shifter achieves <1° RMS error and <5° phase steps for precise beam control.

Next generation wireless transmitters will rely on highly integrated silicon-based solutions to realize the cost and performance goals of the 4G market. This will require increased use of digital compensation techniques and innovative circuit approaches to maximize power and efficiency and minimize linearity degradation. This paper summarizes the circuit and system strategies being developed to meet these aggressive performance goals.


Question: Initial showcase of 28 GHz and 39 GHz transmission lines and antennas on glass substrates for 5G module applications.

This paper showcases a groundbreaking fully-integrated Antenna-in-Package (AiP) solution designed for W-band scalable phased-array systems. We unveil a functional, compact W-band transceiver package that incorporates 64 dual-polarization antennas embedded within a multilayer organic substrate. This innovative package features 12 metal layers, measures 16.2 mm × 16.2 mm in size, and includes 292 ball-grid-array (BGA) pins with a pitch of 0.4 mm. Four silicon-germanium (SiGe) transceiver ICs are precisely flip-chip attached to the package. Rigorous full-wave electromagnetic simulations and radiation pattern measurements have been conducted to optimize the antenna's performance within the package environment, achieving outstanding model-to-hardware agreement. Benefitting from meticulous circuit-package co-design, the package maintains a half-wavelength spacing—1.6 mm at 94 GHz—between adjacent antenna elements, facilitating array scalability at both the package and board levels. The effective isotropic radiated power (EIRP) and radiation patterns are also empirically measured, illustrating the 64-element spatial power combining capability of the package. This AiP solution is a significant step forward in the development of high-performance W-band phased-array systems for 5G and beyond.

This paper gives a detailed description of a >1.3-Tb/s VCSEL-based on-board parallel-optical transceiver module for high-density optical interconnects. The optical module integrates a 28-Gb/s × 24-channel transmitter and receiver into one package with a 1-in2 footprint, thereby yielding a data rate density as high as 1.34 Tb/s/in2. A unique module design is developed to utilize the whole top surface for thermal dissipation and the bottom surface for optical and electrical interfaces. The heat dissipation characteristics are studied in simulations and experiments. A test system of 24-channel optical loop-back link is built to evaluate the performance when operating all 24 channels by 28.05-Gb/s 231–1 PRBS bit streams simultaneously. With a total power consumption of 9.1 W, the optical module can be operated within the operating case temperature under a practical air cooling condition. When operating all 24 channels simultaneously, a total jitter margin at BER of 10–12 is larger than 0.4 U.I. at a monitor channel. The measured jitter margin is consistent regardless of any channel operation.

A hybrid multi-mode narrow-frame antenna for WWAN/LTE metal-rimmed smartphone applications is proposed in this paper. The ground clearance is only 5 mm × 45 mm, which is promising for narrow-frame smartphones. The metal rim with a small gap is connected to the system ground by three grounded patches. This proposed antenna can excite three coupled-loop modes and one slot mode. By incorporating these four modes, the proposed antenna can provide coverage for GSM850/900, DCS/PCS/UMTS2100, and LTE2300/2500 operations. Detailed design considerations of the proposed antenna are described, and both experimental and simulated results are also presented.

We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.

Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.

Massive multiple input and multiple output (MIMO) has attracted significant interests in both academia and industry. It has been considered as one of most promising technologies for 5G wireless systems. The large-scale antenna array for base stations naturally becomes the key to deploy the Massive MIMO technologies. In this communication, we present a dual-polarized antenna array with 144 ports for Massive MIMO operating at 3.7 GHz. The proposed array consists of 18 low profile subarrays. Each subarray consists of four single units. Each single antenna unit consists of one vertically polarized port and one horizontally polarized port connected to power splitters, which serve as a feeding network. A stacked patch design is used to construct the single unit with the feeding network, which gives higher gain and lower mutual coupling within the size of a conversional dual-port patch antenna. Simulation results of the proposed single antenna unit, sub-array, and Massive MIMO array are verified by measurement.

This paper presents 28 GHz and 73 GHz millimeter-wave propagation measurements performed in a typical office environment using a 400 Megachip-per-second broadband sliding correlator channel sounder and highly directional steerable 15 dBi (30° beamwidth) and 20 dBi (15° beamwidth) horn antennas. Power delay profiles were acquired for 48 transmitter-receiver location combinations over distances ranging from 3.9 m to 45.9 m with maximum transmit powers of 24 dBm and 12.3 dBm at 28 GHz and 73 GHz, respectively. Directional and omnidirectional path loss models and RMS delay spread statistics are presented for line-of-sight and non-line-of-sight environments for both co- and cross-polarized antenna configurations. The LOS omnidirectional path loss exponents were 1.1 and 1.3 at 28 GHz and 73 GHz, and 2.7 and 3.2 in NLOS at 28 GHz and 73 GHz, respectively, for vertically-polarized antennas. The mean directional RMS delay spreads were 18.4 ns and 13.3 ns, with maximum values of 193 ns and 288 ns at 28 GHz and 73 GHz, respectively.

A new dual-band wearable textile antenna designed for body-centric communications is introduced. The antenna is fully characterized in free space and on the body model, with and without an electromagnetic band gap (EBG) substrate. The band gap array consists of 3 × 3 elements and is used to reduce the interaction with human tissues. With the EBG back reflector, the radiation into the body is reduced by more than 15 dB. Increases of 5.2 dB and 3 dB gain are noticed at 2.45 GHz and 5.5 GHz, respectively. The results are presented for the return loss, radiation pattern, efficiency, and specific absorption rate (SAR).

In this paper, we present a systematic approach to the design, optimization and characterization of a broadband 5:1 bandwidth (0.8 to 4.0 GHz) antenna subarray. The array element is an optimized-taper antipodal Vivaldi slot with a bandwidth of 2.5:1. Two such elements of different sizes and with 0.4 GHz (10% of the highest frequency) overlapping bandwidths are arrayed in a nested lattice above a multilevel ground plane that shields the feeds and electronics. Return loss, radiation patterns, cross-polarization and mutual coupling are measured from 0.5–5.0 GHz. This array demonstrates E plane patterns with 50 and 45 3-dB beamwidths in the lower and upper frequency bands, respectively. The coupling between the elements is characterized for different relative antenna positions in all three dimensions.

In this paper, a 77-GHz radar receiver is presented, which comes in a wafer level package and thus eliminates the need for wire bonding yielding significant cost reduction. The high integration level available in the productive Silicon-Germanium (SiGe) technology used in this paper allows for implementation of in-system monitoring of the receiver conversion parameters. This facilitates the realization of ISO 26262 compliant radar sensors for automotive safety applications.

A novel design of dual broadband high gain substrate integrated waveguide (SIW) cavity backed slot array antenna is described. The SIW cavity is design in dominant mode TE10. The proposed antenna is design by two pair of λg/2-resonators cavity backed slot on broad wall of single layer substrate integrated waveguide. To place two pair unequal slot length in stair nature on broad-wall of SIW cavity are responsible to broadening the bandwidth at two different frequency band and also due to arraying nature of slot enhanced the gain. The proposed antenna have Lower frequency bandwidth 4.4% (9.58 GHz–10.02 GHz) and higher frequency bandwidth is 9.49% (10.84 GHz–11.92 GHz) with 10 dB gain at lower band and 9.4 dB gain at higher band. The proposed antenna has high gain, directive radiation field with high FTBR.


Question: Initial showcase of 28 GHz and 39 GHz transmission lines and antennas on glass substrates for 5G module applications.

This paper presents a novel, cost-effective, high-gain, and broadband antenna array, designed for the 60-GHz frequency band, utilizing substrate integrated waveguide (SIW) technology. The antenna array achieves both high gain and wideband performance through the integration of a single-layered SIW feeding network that includes wideband T-junctions and high-gain cavity-backed patch antennas. Despite its multilayered configuration, the proposed design can be manufactured using conventional single-layered printed circuit board (PCB) techniques, followed by the stacking and assembly of these layers. The antenna array exhibits an impressive simulated impedance bandwidth of 27.5% and a measured bandwidth of 22.6% for a 10 dB return loss. The paper also discusses the discrepancy between simulated and measured results. The array demonstrates a maximum gain of 19.6 dBi, along with symmetrical unidirectional radiation patterns and low cross-polarization. These characteristics, combined with its low fabrication cost and superior performance, make the proposed antenna array an attractive option for millimeter-wave wireless communication systems, including those envisioned for 5G and beyond.

The proposed antenna array design, with its focus on achieving high gain and broad bandwidth at 60 GHz, closely aligns with the requirements for 5G modules operating at higher frequency bands, such as 28 GHz and 39 GHz. The use of substrate integrated waveguide technology and the multilayered structure, which can be realized through cost-effective single-layer PCB fabrication, highlights the potential for scaling this design to other millimeter-wave applications. The antenna's demonstrated performance in terms of gain and bandwidth, along with its low cross-polarization, positions it as a viable solution for enhancing the efficiency and coverage of 5G networks operating in the millimeter-wave spectrum.

A slot array antenna is designed using ridge gap waveguide technology in two sub arrays. Each sub array is fed through a power divider which is designed on a separate ridge- groove gap waveguide. The coupling between two gap waveguides is conducted using coaxial transmission lines. The power divider of ridge-groove gap is fed using coaxial line that can provide a bandwidth of 22% at 14 GHz. The integrated antenna operates within the frequency range of 13.5-15.6 GHz with a gain of 13.9-15.5 dB. Design of the transmission line, power divider and sub-array antenna is presented.

The design and performance of two high efficiency Ka-band power amplifier MMICs utilizing a 0.15μm GaN HEMT process technology is presented. Measured in-fixture continuous wave (CW) results for the 3-stage balanced amplifier demonstrates up to 11W of output power and 30% power added efficiency (PAE) at 30GHz. The 3-stage single-ended design produced over 6W of output power and up to 34% PAE. The die size for the balanced and single-ended MMICs are 3.24×3.60mm2 and 1.74×3.24mm2 respectively.

In this paper, a 77-GHz radar receiver is presented, which comes in a wafer level package and thus eliminates the need for wire bonding yielding significant cost reduction. The high integration level available in the productive Silicon-Germanium (SiGe) technology used in this paper allows for implementation of in-system monitoring of the receiver conversion parameters. This facilitates the realization of ISO 26262 compliant radar sensors for automotive safety applications.

This article describes novel methods to enhance the RF integration of wideband automotive radars. The first part of the paper is introducing the context and the considered scenario. Whereas the second part is focusing on two solutions to improve the radar integration in car chassis environment. The aim is to have good RF performances on wideband operation (76–81 GHz) and on a wide range of angle of incidence for the radar having wide field of view (e.g. Blind spot radar, Pedestrian detection, …).

The system design of an integrated microwave imaging radar for the diagnostic screening of breasts cancer is presented. A custom integrated circuit implemented in a 65-nm CMOS technology and a pair of patch antennas realized on a planar laminate are proposed as the basic module of the imaging antenna array. The radar operates on the broad frequency range from 2 to 16 GHz with a dynamic range of 107 dB. Imaging experiments carried out on a realistic breast phantom show that the system is capable of detecting tumor targets with a resolution of 3 mm.

In this paper, for the first time a novel substrate integrated waveguide (SIW)-based 10W power amplifier (PA), designed with conductor-backed coplanar waveguide (CBCPW)-to-SIW transition matching network (MN), is presented. Transition between CBCPW and SIW is employed for both input and output MN designs, the proposed SIW PA can be easily connected with any microstrip or SIW-based circuits. Asymmetrical and symmetrical types of CBCPW-to-SIW transition MN are proposed. Output SIW-based MN is designed with asymmetrical structure by using one inductive metalized post and input SIW-based MN is designed with symmetrical structure by using two identical inductive metalized posts. One SIW-based 10W PA using GaN HEMT at 3.62 GHz is designed, fabricated, and measured. Measured results show that the maximum power added efficiency (PAE) is 54.24 % with 39.74 dBm output power and the maximum gain is 13.31 dB. At the design frequency of 3.6 GHz, the size of proposed SIW-based PA is comparable with other microstrip-based PAs.

A compact 680-GHz waveguide orthomode transducer (OMT) and circular horn combination has been designed, tested, and characterized in a radar transceiver's duplexer. The duplexing capability is implemented by a hybrid waveguide quasi optical solution, combining a linear polarization OMT and an external grating polarizer. Isolation between the OMT's orthogonal ports' flanges was measured with a vector network analyzer to exceed 33 dB over a >10% bandwidth between 630 and 710 GHz. Calibrated Y-factor measurements using a mixer attached to the OMT ports reveal losses through the transmit and receive paths that sum to an average of 4.7 dB of two-way loss over 660-690 GHz. This is consistent with radar sensitivity measurements comparing the new OMT/horn with a quasi-optical wire grid beam splitter. Moreover, the radar performance assessment validates the OMT as a suitable compact substitute of the wire grid for the JPL's short-range 680-GHz imaging radar.

We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ω and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.

In this letter, a novel substrate integrated waveguide (SIW) cavity-backed self-triplexing slot antenna is proposed for triple-band communication. It is realized on a single-layer printed circuit board. The proposed antenna consists of a pair of bow-tie slots etched on an SIW cavity, and three different resonant modes are excited by two microstrip lines and a coaxial probe to operate at three distinct frequencies (7.89, 9.44, and 9.87 GHz). The band around 9.48 GHz is obtained due to radiation from one bow-tie slot fed by adjacent microstrip feedline and band around 9.87 GHz is due to radiation from other bow-tie slot fed by its adjacent microstrip feedline. On the other hand, lower band around 7.89 GHz is achieved because of radiation from both bow-tie slots excited by coaxial probe feedline. The measured isolation between any two feedlines is better than 22.5 dB. The measured realized gain is more than 7.2 dB at all the bands. Cross polarization below 36.5, 29.3, and 24.45 dB in broad sight direction and high front-to-back ratio of more than 17.3 dB at each resonant frequency are obtained.

A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.


Question: Advancing machine learning techniques for automated tuning of MapReduce parameters.

Starfish: An Adaptive Self-Tuning Framework for Big Data Processing

Hadoop, a prominent framework for big data analytics, embodies the MAD (Magnetism, Agility, Depth) characteristics essential for managing vast datasets. Magnetism, referring to its ability to attract all types of data sources, agility, indicating its adaptability to the swift evolution of data, and depth, highlighting its capability to support intricate analytics requirements, are its defining traits. Starfish, a novel self-tuning system, enhances Hadoop's MAD qualities, making it even more adaptive and self-adjusting.

Starfish introduces several key features to Hadoop, making it MADDER and more self-tuning. Firstly, it incorporates data-lifecycle-awareness, ensuring optimal performance at every stage of the data lifecycle. This ensures that data is processed efficiently, regardless of its maturity or stage in the lifecycle. Secondly, Starfish exhibits elasticity, dynamically adjusting resources and operational costs in response to changing data volumes and processing demands. This flexibility is crucial for maintaining efficiency and minimizing costs in a fluctuating data landscape. Lastly, Starfish demonstrates robustness, offering both availability and predictability in its operations. It ensures that data analytics tasks are not only executed reliably but also within predictable timeframes, enhancing the reliability and trustworthiness of big data analytics.

A notable aspect of Starfish is its just-in-time job optimization capability. This feature allows the system to dynamically adjust its processing parameters based on the specific requirements of each job, ensuring that resources are allocated efficiently and that jobs are completed as quickly as possible. This real-time adjustment of resources is a significant advancement in big data processing, enabling more efficient and effective analytics.

In essence, Starfish transforms Hadoop from a static big data processing framework into a dynamic, self-tuning system capable of handling the complexities and uncertainties inherent in big data environments. By incorporating data-lifecycle-awareness, elasticity, and robustness, Starfish ensures that Hadoop remains a powerful tool for big data analytics, capable of adapting to and optimizing performance under various conditions.

Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.

Nearly all science and engineering fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highestperforming solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how highperforming solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their costto-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAPElites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves the state-of-the-art for finding a single, best solution, it will catalyze advances throughout all science and engineering fields.

More and more E-commerce Websites provide products with different prices which made it hard for consumers to find the products and services they want. In order to overcome this data overload, personalized recommendation engines are used to suggest products and to provide consumers with relevant data to help them decide which products to purchase. Recommendation engines are highly computational and hence ideal for the Hadoop Platform. This system aims at building a book recommendation engine which uses item or user based recommendation from Mahout for recommending books. It will analyze the data and give suggestions based on what similar users did and on the past transaction history of the user.

Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyperparameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on backpropagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.

Parallelizing of software improves its effectiveness and productivity. To guarantee correctness, the parallel and serial versions of the same code must be formally verified to be equivalent. We present a novel approach, called GRASSP, that automatically synthesizes parallel single-pass array-processing programs by treating the given serial versions as specifications. Given arbitrary segmentation of the input array, GRASSP synthesizes a code to determine a new segmentation of the array that allows computing partial results for each segment and merging them. In contrast to other parallelizers, GRASSP gradually considers several parallelization scenarios and certifies the results using constrained Horn solving. For several classes of programs, we show that such parallelization can be performed efficiently. The C++ translations of the GRASSP solutions sped performance by up to 5X relative to serial code on an 8-thread machine and Hadoop translations by up to 10X on a 10-node Amazon EMR cluster.

Deployers of cloud storage and iterative processing systems typically have to deal with either dollar budget constraints or throughput requirements. This paper examines the question of whether such cloud storage and iterative processing systems are more cost-efficient when scheduled on a COTS (scale out) cluster or a single beefy (scale up) machine. We experimentally evaluate two systems: 1) a distributed key-value store (Cassandra), and 2) a distributed graph processing system (Graph Lab). Our studies reveal scenarios where each option is preferable over the other. We provide recommendations for deployers of such systems to decide between scale up vs. Scale out, as a function of their dollar or throughput constraints. Our results indicate that there is a need or adaptive scheduling in heterogeneous clusters containing scale up and scale out nodes.

Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.

Apache Spark is an open-source cluster computing framework. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce’s linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala, SQL and R (MapReduce has 2 core calls) , and its core data abstraction, the distributed data frame. In addition, it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. With massive amounts of computational power, deep learning has been shown to produce state-of-the-art results on various tasks in different fields like computer vision, automatic speech recognition, natural language processing and online advertising targeting. Thanks to the open-source frameworks, e.g. Torch, Theano, Caffe, MxNet, Keras and TensorFlow, we can build deep learning model in a much easier way. Among all these framework, TensorFlow is probably the most popular open source deep learning library. TensorFlow 1.0 was released recently, which provide a more stable, flexible and powerful computation tool for numerical computation using data flow graphs. Keras is a highlevel neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This tutorial will provide an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. It is divided into three parts: the first part will cover fundamental Spark concepts, including Spark Core, functional programming ala map-reduce, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more; the second part will focus on hands-on algorithmic design and development with Spark (developing algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as pagerank/shortest path, gradient descent algorithms such as support vectors machines and matrix factorization. Industrial applications and deployments of Spark will also be presented.; the third part will introduce deep learning concepts, how to implement a deep learning model through TensorFlow, Keras and run the model on Spark. Example code will be made available in python (pySpark) notebooks.

This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an administration layer and conventional web server programming, via a data synchronization utility, leads to the straightforward development of a web-based user interface that allows the monitoring and managing of diverse online distributed computing applications. It also introduces an experiment generation and monitoring tool particularly suitable for investigating machine learning in game playing. The platform is demonstrated with experiments for two different games.

|As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more di cult and errorprone. This paper discusses programming assistance and automation concepts and their application to a program development tool for message-passing systems called Hypertool. It performs scheduling and handles the communication primitive insertion automatically. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs.


Question: Advancing machine learning techniques for the automatic tuning of MapReduce parameters.

This paper delves into the approximation capabilities of multilayer feedforward networks, exploring their potential for enhancing the performance of MapReduce jobs through machine learning-based auto-tuning. Unlike conventional approaches that rely on static configurations or manual adjustments, this study proposes leveraging the inherent approximation abilities of neural networks to dynamically optimize MapReduce parameters. Multilayer feedforward networks, with their capacity to approximate complex functions, are trained on historical data from MapReduce jobs, enabling them to predict optimal configurations based on various job characteristics. The networks are designed to learn from a diverse set of input features, including job size, data characteristics, and system load, to generate tailored tuning recommendations. Through a series of experiments, the effectiveness of this machine learning-based approach is demonstrated, showing significant improvements in MapReduce job performance compared to static tuning methods. The results highlight the potential of multilayer feedforward networks in automating the tuning process for MapReduce, paving the way for more efficient and adaptable big data processing systems.

Increasing system and algorithmic complexity combined with a growing number of tunable application parameters pose significant challenges for analytical performance modeling. We propose a series of robust techniques to address these challenges. In particular, we apply statistical techniques such as clustering, association, and correlation analysis, to understand the application parameter space better. We construct and compare two classes of effective predictive models: piecewise polynomial regression and artifical neural networks. We compare these techniques with theoretical analyses and experimental results. Overall, both regression and neural networks are accurate with median error rates ranging from 2.2 to 10.5 percent. The comparable accuracy of these models suggest differentiating features will arise from ease of use, transparency, and computational efficiency.

Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this paper, we present IRIS - a goal-oriented big data analytics framework using Spark for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
 It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
 We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.

Advancements in new data analysis and visualization technologies have resulted in wide applicability of data-driven decision making. However, raw data from various sources must be wrangled into a suitable form before they are processed by the downstream data tools. People traditionally write data transformation programs to automate this process, and such work is cumbersome and tedious.
 We built a system called FOOFAH for helping the user easily synthesize a desired data transformation program. Our system minimizes the user's effort by only asking for a small illustrative example comprised of the raw input data and the target transformed output; FOOFAH then synthesizes a program that can perform the desired data transformation. This demonstration showcases how the user can apply FOOFAH to real-world data transformation tasks.

We describe in this paper a new approach to parallelize branchand-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort.

Now a day’s Peta-Bytes of data becomes the norm in industries. Handling, analyzing such big data is challenging task. Even frameworks like Hadoop (Open Source Implementation of MapReduce Paradigm) and NoSQL databases like Cassandra, HBase can be used to analyze and store such large data; heterogeneity of data is still an issue. Data centers usually have clusters formed using heterogeneous nodes. Ecosystem like Hadoop can be used to manage such types of cluster but it can not schedule jobs (Application) efficiently on this heterogeneous cluster when data is itself heterogeneous. Heterogeneity of data may be because of data format or because of the complexity. This paper is review of systems and algorithms for distributed management of huge size data, efficiency of these approaches.

Nearly all science and engineering fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highestperforming solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how highperforming solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their costto-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAPElites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves the state-of-the-art for finding a single, best solution, it will catalyze advances throughout all science and engineering fields.

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG_Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.

This paper introduces MELOGRAPH, a new system that exposes in the front-end a domain specific language(DSL) for graph processing tasks and in the back-end identifies, ranks and generates source code for the top-N ranked engines. This approach lets the specialized MELOGRAPH be part of a more general multi-engine workflow optimizer. The candidate execution engines are chosen from the contemporaneous Big Data ecosystem: graph databases (e.g. Neo4j, TitanDB, OrientDB, Sparksee/DEX) and robust graph processing frameworks with Java API or packaged libraries of algorithms (e.g. Giraph, Okapi, Flink Gelly, Hama, Gremlin). As MELOGRAPH is work in progress, our current paper stresses upon the state of the art in this field, provides a general architecture and some early implementation insights.

Arabic Optical Character Recognition (OCR) as Web Services represents a major challenge for handwritten document recognition. A variety of approaches, methods, algorithms and techniques have been proposed in order to build powerful Arabic OCR web services. Unfortunately, these methods could not succeed in achieving this mission in case of large large quantity Arabic handwritten documents. Intensive experiments and observations revealed that some of the existing approaches and techniques are complementary and can be combined to improve the recognition rate. Designing and implementing these recent sophisticated complementary approaches and techniques as web services are commonly complex; they require strong computing power to reach an acceptable recognition speed especially in case of large quantity documents. One of the possible solutions to overcome this problem is to benefit from distributed computing architectures such as cloud computing. This paper describes the design and implementation of Arabic Handwriting Recognition as a web service (AHRweb service) based on the complementary approach K-Nearest Neighbor (KNN) /Support Vector Machine (SVM) (K-NN/SVM) via Amazon Elastic MapReduce (EMR) model. The experiments were conducted on a cloud computing environment with a real large scale handwriting dataset from the Institute for Communications Technology (IFN)/ Ecole Nationale d’Ingénieur de Tunis (ENIT) IFN/ENIT database. The J-Sim (Java Simulator) was used as a tool to generate and analyze statistical results. Experimental results show that Amazon Elastic MapReduce (EMR) model constitutes a very promising framework for enhancing large AHRweb service performances.