This paper approaches an image classification which is based on cricketing activities with the help of Convolutional Neural Network. Fundamentally the aim is to detect the diverse incidents which come to pass throughout a cricket match. It includes different cricket shots played by the cricketers, several symbols which are betokened by the umpires. This scheme has compelled us to develop a distinct absolute dataset Cricket Image Classification (CIC), consisting of various cricketing incidents. Due to the scarcity of ready images, this paper utilizes transfer learning. This paper approaches a custom ResNet50 model together with the utilization of One Cycle Policy to make a robust and time efficient model. For comparison and to provide a better understanding, four other customized models namely ResNet34, ResNet101, VGG16 with Batch Normalization and AlexNet are introduced and studied in this paper. The model is so efficacious that it can even detect incidents in the quaint ambiance of street cricket, although the model is trained with the images of fields and stadiums in the dataset. The final model achieves 99.47% accurate test results.

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. FairPlay correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from Google Play app data (87 K apps, 2.9 M reviews, and 2.4M reviewers, collected over half a year), in order to identify suspicious apps. FairPlay achieves over 95 percent accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75 percent of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer's detection technology. FairPlay also helped the discovery of more than 1,000 reviews, reported for 193 apps, that reveal a new type of “coercive” review campaign: users are harassed into writing positive reviews, and install and review other apps.

The impact of citizen journalism raises important video integrity and credibility issues. In this article, we introduce Vamos, the first user transparent video “liveness” verification solution based on video motion, that accommodates the full range of camera movements, and supports videos of arbitrary length. Vamos uses the agreement between video motion and camera movement to corroborate the video authenticity. Vamos can be integrated into any mobile video capture application without requiring special user training. We develop novel attacks that target liveness verification solutions. The attacks leverage both fully automated algorithms and trained human experts. We introduce the concept of video motion categories to annotate the camera and user motion characteristics of arbitrary videos. We show that the performance of Vamos depends on the video motion category. Even though Vamos uses motion as a basis for verification, we observe a surprising and seemingly counter-intuitive resilience against attacks performed on relatively “stationary” video chunks, which turn out to contain hard-to-imitate involuntary movements. We show that overall the accuracy of Vamos on the task of verifying whole length videos exceeds 93 percent against the new attacks.

Eliminating or reducing skill gaps in the job market is critical to putting people back to work, reducing the unemployment rate, and increasing the labor market participation rate. A key element in closing the skills gap is accurately identifying the mismatch between the skills expected by employers and those possessed by job seekers. In this study, our goal was to profile job titles by effectively quantifying the relevance of skills. We started by using a naive, frequency-based skill ranking approach, which resulted in the most generic skills ranked on the top. We then adapted a number of alternative metrics and compared their performances on a number of job titles. The outcome of this study can support CareerCoach, an analytical solution CareerBuilder has piloted to provide insights and data dashboards to job seekers.

Fraudulent behaviors in Google's Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay's PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer's detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.

The increasing popular interest in personal telemetry, also called the Quantified Self or “lifelogging”, has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.

The ubiquitous and connected nature of camera-equipped mobile devices has greatly increased the value and importance of visual information they capture. Today, broadcasting videos from camera phones uploaded by unknown users is admissible on news networks, and banking customers expect to be able to deposit checks using mobile devices. In this paper, we introduce Movee, a system that addresses the fundamental question of whether the visual stream uploaded by a user has been captured live on a mobile device, and has not been tampered with by an adversary. Movee leverages the mobile device motion sensors and the intrinsic user movements during the shooting of the video. Movee exploits the observation that the movement of the scene recorded on the video stream should be related to the movement of the device simultaneously captured by the accelerometer. Contrary to existing algorithms, Movee has the unique strength of not depending on the audio track. We introduce novel attacks that focus on Movee's defenses, to fabricate acceleration data that mimics the motion observed in targeted videos. We use smartphones and wearable smart glasses to collect both genuine and attack data from 13 users. Our experiments show that Movee is able to efficiently detect human and automatically generated plagiarized videos: Movee's accuracy ranges between 68-93 percent on a smartphone, and between 76-91 percent on a Google Glass device.

Citizen journalism videos increasingly complement or even replace the professional news coverage through direct reporting by event witnesses. This raises questions of the integrity and credibility of such videos. We introduce Vamos, the first user transparent video "liveness" verification solution based on video motion, that can be integrated into any mobile video capture application without requiring special user training. Vamos' algorithm not only accommodates the full range of camera movements, but also supports videos of arbitrary length. We develop strong attacks both by utilizing fully automated attackers and by employing trained human experts for creating fraudulent videos to thwart mobile video verification systems.

In large networks, the connected triples are useful for solving various tasks including link prediction, community detection, and spam filtering. Existing works in this direction concern mostly with the exact or approximate counting of connected triples that are closed (aka, triangles). Evidently, the task of triple sampling has not been explored in depth, although sampling is a more fundamental task than counting, and the former is useful for solving various other tasks, including counting. In recent years, some works on triple sampling have been proposed that are based on direct sampling, solely for the purpose of triangle count approximation. They sample only from a uniform distribution, and are not effective for sampling triples from an arbitrary user-defined distribution. In this work we present two indirect triple sampling methods that are based on Markov Chain Monte Carlo (MCMC) sampling strategy. Both of the above methods are highly efficient compared to a direct sampling-based method, specifically for the task of sampling from a non-uniform probability distribution. Another significant advantage of the proposed methods is that they can sample triples from networks that have restricted access, on which a direct sampling based method is simply not applicable.

The increasing interest in personal telemetry has induced a popularity surge for wearable personal fitness trackers. Such trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fit bit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce Fit Bite and GarMax, tools to launch efficient attacks against Fit bit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fit bit and Garmin.

Unquestionably, one of the most important developments in this century has been the avalanche advancement of Integrated Circuits technology incorporating solid state systems into areas never before imagined. Students face special challenges to understand these complex systems using conventional instructional methods. We here present a new pedagogical approach using the Variational Thermodynamic principle to analyze and educate engineering students the quasi-static behavior of such circuit and systems at a level of detail not otherwise available either experimentally or by standard methods. Application of this method is expected to enhance students' grasp of the topic up to the design level.

The popularity and influence of reviews, make sites like Yelp ideal targets for malicious behaviors. We present Marco, a novel system that exploits the unique combination of social, spatial and temporal signals gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Marco increases the cost and complexity of attacks, by imposing a tradeoff on fraudsters, between their ability to impact venue ratings and their ability to remain undetected. We contribute a new dataset to the community, which consists of both ground truth and gold standard data. We show that Marco significantly outperforms state-of-the-art approaches, by achieving 94% accuracy in classifying reviews as fraudulent or genuine, and 95.8% accuracy in classifying venues as deceptive or legitimate. Marco successfully flagged 244 deceptive venues from our large dataset with 7,435 venues, 270,121 reviews and 195,417 users. Among the San Francisco car repair and moving companies that we analyzed, almost 10% exhibit fraudulent behaviors.

Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). PROFILR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows that PROFILR is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.

Majority of the existing works on network analysis study properties that are related to the global topology of a network. Examples of such properties include diameter, power-law exponent, and spectra of graph Laplacian. Such works enhance our understanding of real-life networks, or enable us to generate synthetic graphs with real-life graph properties. However, many of the existing problems on networks require the study of local topological structures of a network, which did not get the deserved attention in the existing works. In this work, we use graphlet frequency distribution (GFD) as an analysis tool for understanding the variance of local topological structure in a network; we also show that it can help in comparing, and characterizing real-life networks. The main bottleneck to obtain GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have up-to five vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5 percent.

Population density and natural and man-made disasters make public safety a concern of growing importance. In this paper we aim to enable the vision of smart and safe cities by exploiting mobile and social networking technologies to securely and privately extract, model and embed real-time public safety information into quotidian user experiences. We first propose novel approaches to define location- and user-based safety metrics. We evaluate the ability of existing forecasting techniques to predict future safety values. We introduce iSafe, a privacy-preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. We present implementation details of iSafe as both an Android application and a browser plugin that visualizes safety levels of visited locations and browsed geosocial venues. We evaluate iSafe using crime and census data from the Miami-Dade (FL) county as well as data we collected from Yelp, a popular geosocial network.

The visual information captured with camera-equipped mobile devices has greatly appreciated in value and importance as a result of their ubiquitous and connected nature. Today, banking customers expect to be able to deposit checks using mobile devices, and broadcasting videos from camera phones uploaded by unknown users is admissible on news networks. We present Movee, a system that addresses the fundamental question of whether the visual stream coming into a mobile app from the camera of the device can be trusted to be un-tampered with, live data, before it can be used for a variety of purposes.

Counting triangles in a large network is an important research task because of its usages in analyzing large networks. However, this task becomes expensive when runs on large networks with millions of nodes and millions of edges. For efficient triangle counting on such networks, researchers in recent years have adopted approximate counting or have proposed parallel or distributed solutions. In this work, we propose an approximate triangle counting algorithm, that runs on multi-core computers through a multi-threaded implementation. We show that for a given speedup factor, our method has a better approximation accuracy; further, the multi-threaded implementation that we propose is much superior to the Hadoop based distributed methods that earlier algorithms propose.

In this paper, we present a new approach which uses statistical methods to analyze the Electromigration (EM) reliability of a chip. This new approach utilizes statistical nature of EM failure distribution to assess overall EM risk and applies a within-die temperature map on a chip level design of multiple metal and via layers. The new proposed method provides a more optimistic EM violation estimation result when compared with results using traditional method.

Storing user friend lists, preferences and messages, online social networks have become a significant source of sensi-tive personal information. A recent addition to this space, geosocial networks (GSNs) such as Yelp [1] or Foursquare [2],

Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.

Providing input to targeted advertising, profiling social network users is an important source of revenue for geosocial networks. Since profiles contain personal information, their construction introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce ProfilR, a suite of mechanisms that construct LCPs in a private and correct manner. Our Android implementation shows that ProfilR is efficient: the end-to-end overhead is small even under strong correctness assurances.

Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose GUISE, which uses a Markov Chain Monte Carlo (MCMC) sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that GUISE obtains the GFD within few minutes, whereas the exhaustive counting based approach takes several days.

Smart cities combine technology and human resources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing smart, safe cities, by combining the use of personal mobile devices and social networks to make users aware of the safety of their surroundings. We propose novel metrics to define location and user based safety values. We evaluate the ability of forecasting techniques including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) to predict future safety values. We devise iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile device users and integrate our approach into an Android application for visualizing safety levels. We further investigate relationships between location dependent social network activity and crime levels. We evaluate our contributions using data we collected from Yelp as well as crime and census data.

The first Electric Circuits course is a core component of all engineering undergraduate curricula that include not only Electrical but other engineering disciplines such as Computer, Mechanical and Civil Engineering programs worldwide. The Electric Circuits Concept Inventory (ECCI) is a set of multiple choice questions that measures students' understanding of DC Circuit analysis. The topics include (i) fundamental conceptual circuit topics (ii) Circuit laws and (iii) Circuit analysis techniques. In this paper, we develop a rationale for developing a repertoire of classic and representative set of problems that test the major concepts taught in a typical Electric Circuits course. We also present a set of multiple-choice questions suggested for inclusion in the ECCI focusing on the fundamental topics part of DC circuits analysis only. We examine the various concepts tested in each question and relate its importance to an overall course in Electric Circuits. We welcome more additions to this growing repertoire of questions that we plan to build and use in finalizing the ECCI test, especially in AC Circuit analysis topics.

The first Electric Circuits course is a core component of all engineering undergraduate curricula that include not only Electrical but other engineering disciplines such as Computer, Mechanical and Civil Engineering programs worldwide. The Electric Circuits Concept Inventory (ECCI) is a set of multiple choice questions that measures students' understanding of DC Circuit analysis. The topics include (i) fundamental conceptual circuit topics (ii) Circuit laws and (iii) Circuit analysis techniques. In a companion paper [1], we presented a rationale for ECCI. In this paper, we present a set of multiple-choice questions suggested for inclusion in the ECCI focusing on (ii) DC Circuit laws and (iii) DC Circuit analysis techniques. We examine the various concepts tested in each question and relate its importance to a quality overall course in Electric Circuits. We welcome more additions to this growing repertoire of questions that we plan to build and use in finalizing the ECCI test especially in AC Circuit analysis topics.

In this paper, we propose a novel channel selection algorithm for a cognitive radio system that coexists with legacy wireless LAN systems operating in the ISM bands. The cognitive system is capable of scanning its environment to measure ISM channel occupancy in terms of WLAN channel utilization. The system takes the opportunity of spectrum availability to facilitate its communication to the cognitive radio users. Different users experience different levels of WLAN activities in their surroundings which also vary across channels. Based on measured utilization, the proposed algorithm selects a channel considering cognitive radio users' QoS requirements. The algorithm is flexible to achieve different degrees of trade-off between the system throughput and users' fairness. Simulation results show that the proposed scheme can achieve up to 30% higher throughput while ensuring a higher level of fairness compared to a scheme where channel selection is random.

We study a dynamic inter-cell interference coordination scheme formulated using a novel binary integer linear programming optimization technique. The scheme aims to maximize utility, where different utility measures have been defined to model varying levels of user fairness. The formulated problem is decomposed into a number of smaller sub-problems and solved iteratively to ease the computational complexity. We observe the cell-edge and sector throughput as well as user fairness and compare with a reference scheme, where interference coordination is not used. It is observed that the proposed scheme always outperforms the reference scheme in terms of cell-edge and sector throughput as well as in fairness.

Interference management has been a key concept for designing future high data-rate wireless systems that are required to employ dense reuse of spectrum. Static or semi-static interference coordination based schemes provide enhanced cell-edge performance but with severe penalty to the overall cell throughput. Furthermore, static resource planning makes these schemes unsuitable for applications in which frequency planning is difficult, such as femtocell networks. In this paper, we present a novel dynamic interference avoidance scheme that makes use of inter-cell coordination in order to prevent excessive inter-cell interference, especially for cell or sector edge users that are most affected by inter-cell interference, with minimal or no impact on the network throughput. The proposed scheme is comprised of a two-level algorithm - one at the base station level and the other at a central controller to which a group of neighboring base stations are connected. Simulation results show that the proposed scheme outperforms the reference schemes, in which either coordination is not employed (reuse of 1) or employed in a static manner (reuse of 3 and fractional frequency reuse), in terms of cell edge throughput with a minimal impact on the network throughput and with some increase in complexity.

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. Although distributed schemes are more attractive, it is essential to seek outstanding performance benchmarks to which various decentralized schemes can be compared. Therefore, this paper provides a comprehensive centralized RRM algorithm for downlink OFDMA cellular fixed relay networks in a way to ensure user fairness with minimal impact on network throughput. In contrast, it has been observed that pure opportunistic schemes and fairness-aware schemes relying solely on achievable and allocated capacities may not attain the desired fairness, e.g., proportional fair scheduling. The proposed scheme is queue-aware and performs three functions jointly; dynamic routing, fair scheduling, and load balancing among cell nodes. We show that the proposed centralized scheme is different from the traditional centralized schemes in terms of the substantial savings in complexity and feedback overhead.

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. This paper therefore provides a comprehensive RRM algorithm for OFDMA-based multi-cellular fixed relay networks in a way to ensure fairness among users with minimal impact on the network throughput (in contrast, pure opportunistic RRM techniques always favor users with good channel conditions). Unlike the majority of works in the literature, our proposed scheme is queue-aware and jointly performs routing, fair scheduling, and load balancing among cell nodes. The routing strategy has inherent learning ability and it dynamically converges to better routes.

The investigation of co-channel interference mitigation techniques (such as, interference cancellation through receiver processing, interference randomization by frequency hopping, and interference avoidance through resource usage restrictions imposed by frequency and power planning) has become a key focus area in achieving dense spectrum reuse in next generation cellular systems such as 3GPP LTE, LTE- advanced, and WiMAX. In this paper, we propose an interference avoidance scheme for LTE downlink that uses dynamic inter-cell coordination facilitated through X2 interface among neighbouring evolved UTRAN nodeBs (eNBs, i.e., LTE base stations). Proposed scheme is evaluated by extensive simulations and compared with a number of reference schemes available in the literature. It has been observed that the proposed scheme attains superior performance in terms of cell-edge and sector throughput compared to those in the reference schemes.

Adaptive token bank fair queuing (ATBFQ) algorithm has been proposed as a cross-layer scheduling technique for 4G wireless systems recently. This algorithm takes higher layer quality of service (QoS) attributes such as priorities, interflow fairness, and delay constraints into account. By selecting the user terminals (UTs) in a certain prioritized manner derived from QoS attributes, the performance of the UTs, suffering from high interference and/or shadowing in particular, can be improved. The ATBFQ algorithm has been tested in a multicell environment in the presence of intercell interference by comparing with reference Score Based (SB) and Round Robin (RR) algorithms. In this paper, we further analyze ATBFQ primarily with regard to fairness along with other performance metrics accessed in a more elaborate system considering varying interference and loading conditions. Furthermore, an adaptive method for the allocation of resources is proposed for ATBFQ parameter selection, and is shown to have better performance in various loading conditions. It is observed from simulation results that ATBFQ with adaptive parameter selection outperforms the reference schemes in terms of queuing delay and UT throughput for different network loading cases.

OFDMA subchannel allocation has been well-studied. However, most of the available literature considers a single cell system without cochannel interference. We present a novel intercell interference avoidance scheme, in which the downlink OFDMA subchannels are allocated through intercell coordination in a multicell environment. The proposed scheme not only aims to achieve maximized network throughput but also focuses on providing improved throughput for cell or sector edge users that are most affected by intercell interference. Enhanced cell edge performance may result in fewer base stations needed to cover a region; this, in turn, may yield substantial savings in deployment cost. The scheme is comprised of two different algorithms; one at the base station level, and the other at a central controller to which a group of neighboring base stations are connected. The performance of the proposed scheme is compared with that of a reference scheme in which coordination is not employed. It is observed from simulation results that the proposed scheme outperforms the reference scheme in terms of network and cell edge throughput.

In this paper, we present a novel integer programming (IP) optimization formulation for downlink OFDM subchannel allocations in a multicell system and propose viable sub-optimal solutions. The schemes use dynamic intercell coordination in order to avoid or minimize dominant co-channel interference and achieve maximum network throughput by exploiting multicell multiuser diversity and interference avoidance gains. The performance of the proposed schemes is compared with that of reference round-robin schedulers without intercell coordination. It is observed from snapshot simulation results that the proposed schemes outperform the reference schemes in terms of throughput and quality of service (QoS) considerations.

We propose an interference management scheme for providing quality of service (QoS) in the absence of automatic repeat request (ARQ) in cellular networks augmented with fixed relays. The high packet error rate in a system with ARQ not only incurs additional packet delay, which is undesirable for real-time services, but also reduces link throughput because of the increased over-the-air signaling overhead and retransmissions. The proposed scheme strives for improvements in packet error rate and net throughput while maintaining acceptable delay through the use of inter-cell coordination. The inter-cell coordination uses backbone network for information exchange and thereby transfers over-the-air signaling overhead to the high-speed backbone network. In our scheme, a group of base stations, each of which is either a recipient of or a contributor to dominant interference, form an interferer group and exchange channel state information with each other. Based on this instantaneous channel quality information, the proposed scheme makes intelligent scheduling and routing decisions taking the interference into account. The performance of the scheme is compared with that of an uncoordinated scheme through extensive simulations. It has been observed that the proposed scheme achieves significant performance benefits in terms of packet error rate and net throughput.

In this paper, we present a flow to minimize the variability of a circuit performance measure due to the statistical variations in the manufacturing process. The flow starts with 0.18mum worst case corner device model parameters and transforms them to uncorrelated random variables for the statistical design of experiments which generate response surface models (RSM) of the circuit variability in terms of the circuit designable parameters. The minimization of variance due to process fluctuations is performed on the RSM functions by changing the circuit designable parameters, such as channel widths of the devices without causing excessive mean response shifts. Finally, the mean response is re-adjusted using the designable parameters that are not used in the variability minimization

In this paper, we present a novel transmission scheduling scheme for fixed broadband wireless access (FBWA) networks. In order to mitigate high co-channel interference resulting from dense reuse of frequency, scheduling schemes often consider interference management issues as an essential part. To that end, a series of literature has been published recently, where a group of base stations form an interference group, and scheduling scheme deployed in the group allows only one base station to transmit at a time. As a result of time orthogonality in transmissions, the co-channel interference and hence the packet error rate can be reduced. However, prohibiting concurrent transmissions in these orthogonal schemes introduces higher end-to-end packet delay which might not be desirable for real-time traffic such as voice and video. Our proposed scheme aims to improve delay performance as well as to ensure better resource utilization in such schemes in several ways, most notably by introducing opportunistic non-orthogonality in transmissions, incorporating channel state based scheduling decisions, and employing adaptive modulation and coding.