varunr14/newsummary huggingface dataset:
We propose a novel technique for automatic classification of modulation formats bit rates of digitally modulated signals as well as non data aided NDA estimation of signal to noise ratio SNR in wireless networks. The proposed technique exploits modulation format bit rate and SNR sensitive features of asynchronous delay tap plots ADTPs for the joint estimation of these parameters. Simulation results validate successful classification of three commonly used modulation formats at two different bit rates with an overall accuracy of 99.12 . Similarly in service estimation of SNR in the range of 0 30 dB is demonstrated with mean estimation error of 0.88 dB. The proposed technique requires low speed asynchronous sampling of signal envelope and hence it can enable simple and cost effective joint modulation format bit rate classification and NDA SNR estimation in future wireless networks. 


Summary: A technique for joint modulation format and bit rate classification is proposed. The proposed technique can simultaneously enable non data aided SNR estimation. The technique uses asynchronous delay tap plots with artificial neural networks. The signal classification accuracy is 99.12 and mean SNR estimation error is 0.88 dB. Due to its simplicity it is attractive for future cognitive wireless networks.
Network on Chips NoCs have become the mainstream for Chip Multi Processors CMPs design. Multicast a one to many communication pattern is widely used in barrier clock synchronization multithreading programs and cache coherence protocols for CMPs. Even though several multicast routing algorithms have been proposed for CMPs few can adaptively deal with heavy traffic loads. With the increase of multicast traffic load deterministic routing schemes suffer from long latency and low throughput whereas adaptive routing algorithms can improve the routing performance by providing multiple redundant paths. In this paper we proposed a novel multicast routing algorithm based on partition to reduce the latency of multicast packets by finding multiple routing paths and adaptively choosing available output ports based on the size of buffer space in downstream routers. We evaluate our scheme through simulations and results show that under various configurations both latency and energy consumption have been significantly reduced in comparison with recent multicast routing schemes. 


Summary: An efficient algorithm for routing multicast traffic using recursive partition is proposed. A novel and easy method for minimizing the link usage of a multicast tree is introduced. This algorithm uses minimal adaptive routing to balance the multicast traffic loads.
Inspired by the observation that a healthcare system usually involves various intelligent technologies from different disciplines especially metaheuristics and data mining this paper provides a brief survey of metaheuristics for healthcare system and a roadmap for researchers working on metaheuristics and healthcare to develop a more efficient and effective healthcare system . This paper begins with a discussion of changes for healthcare followed by a brief review of the features of up to date technologies for healthcare . . Then a learnable big data analytics framework for healthcare system is presented which provides a high performance solution to the forthcoming challenges of big data . Finally changes potentials open issues and future trends of metaheuristics for healthcare are addressed . 


Summary: A brief survey of metaheuristics for healthcare system is given. A learnable big data analytics framework for healthcare system is presented. Open issues and future trends of metaheuristics for healthcare system are addressed.
In recent years exchanging data has become far easier with the rapidly growing popularity and increased mobility of mobile devices. However data exchange between mobile devices is performed as a peer to peer communication. Each mobile device that serves as a candidate has a whole range of hardware statuses which may influence data exchange performance. Thus the selection of a mobile device with sufficient computing resources to facilitate round robin data exchange is an interesting process worth exploring. This study proposes an optimal mobile device selection approach for round robin data exchange via the monitoring of the hardware status of each candidate device s system profile. A case study demonstrates the proposed approach step by step. The experimental results show that the proposed approach can be used to improve round robin data exchange performance. The contribution of this study is to provide an approach which selects an optimal candidate mobile device for round robin data exchange in a local wireless communication network. 


Summary: We provide a mobile device selection approach for round robin data exchange. When a data exchange round is finished an adaptive function is carried out to adjust the criteria evaluation in order to obtain a more adaptive ranking order of candidate mobile devices for the next round. As a built in system mechanism it requires no user intervention.
This paper proposes a cross layer mobility support scheme for the IPv6 over low power wireless personal area network wireless sensor network . This scheme combines the handover in the network layer with the handover in the link layer so that the L3 handover and the L2 one can be performed simultaneously . During the L3 handover process a sensor node neither needs a care of address nor participates in the handover process . During the L2 handover process a node uses the channel information to directly achieve the L2 handover without scanning all channels . Finally this paper analyzes and evaluates the performance of this protocol and the data results show that this protocol improves the mobility handover performance . 


Summary: The L3 handover and the L2 handover can be performed simultaneously. A sensor node can directly achieve the L2 handover without scanning all channels. A sensor node neither needs a care of address nor participates in the L3 handover.
This paper presents a method to predict the milling cutting force and cutting coefficient for aluminum 6060 T6 which is a general commercial alloy with 170 190 MPa of tensile strength and is the most commonly used for anodizing and providing extra protection if needed. We introduce two cutting force prediction methods Altintas and recursive least square RLS and compare their results with experimental values. The influence of the feed per tooth and the tool diameter on the cutting force and cutting coefficient was investigated. After accurately determining the cutting coefficients the cutting parameters including the friction angle and shear stress were estimated using the oblique cutting theory. The forces simulated by the RLS method are in good agreement with the experimentally determined forces. An increase in the feed per tooth is shown to increase the cutting force and reduce the cutting coefficient for shearing forces in the tangential direction. The shear stress in the model is close to the actual shear strength of the material. 


Summary: Predict the milling cutting force and cutting coefficient for aluminum 6060 T6 successfully. An increase in the feed per tooth increases the cutting force and reduces the cutting coefficient for shearing forces in the tangential direction. The shear angle increased with the feed per tooth and shear stress was close to the actual shear strength of the material. Variation of the feed per tooth and tool diameter did not change the magnitude of the friction angle.
We develop a fractional extension of a mass conserving Allen Cahn phase field model that describes the mixture of two incompressible fluids . The fractional order controls the sharpness of the interface which is typically diffusive in integer order phase field models . The model is derived based on an energy variational formulation . An additional constraint is employed to make the Allen Cahn formulation mass conserving and comparable to the Cahn Hilliard formulation but at reduced cost . The spatial discretization is based on a Petrov Galerkin spectral method whereas the temporal discretization is based on a stabilized ADI scheme both for the phase field equation and for the Navier Stokes equation . We demonstrate the spectral accuracy of the method with fabricated smooth solutions and also the ability to control the interface thickness between two fluids with different viscosity and density in simulations of two phase flow in a pipe and of a rising bubble . We also demonstrate that using a formulation with variable fractional order we can deal simultaneously with both erroneous boundary effects and sharpening of the interface at no extra resolution . 


Summary: A new fractional mass conserving Allen Cahn model. A second order time spectral space method for the coupled system of fractional equations. A variable fractional order model to control multi rate diffusion. First numerical solution of the fractional Navier Stokes equations.
Code Division Multiple Access which allows multiple users to access and transmit data over wireless channel simultaneously is implemented using different code assignment schemes including Pair wise Code Assignment. In this paper the existing pair wise code assignment procedure is enhanced and a new such scheme is presented with at most codes where is the maximum degree of a network. In addition a co channel interference called secondary interference that exists in the existing schemes has been addressed and an interference free pair wise code assignment procedure is proposed with at most 2 c codes where c is a constant. On simulation over different synthesized actual and random networks it is found that our scheme improves successful transmission rate and blocking probability by 17 and 12.5 respectively. Although the code requirement of the proposed interference free scheme is increased by 40 it would improve the network performance. 


Summary: Two pair wise code assignment schemes are proposed for CDMA based MAC protocol. A co channel secondary interference is detected in the existing code assignment schemes. The proposed schemes deals with the minimum code requirement spatial reuse of codes and avoidance of secondary interference. Improves the network performance.
Enhancing the throughput of cognitive radio network in the presence of malicious cognitive radio user is addressed in this paper . We have considered the impact of MCRU on the sensing performance and achievable throughput of secondary network during two phases namely sensing phase and secondary user transmission phase respectively . This impact is mitigated by employing cognitive radio users equipped with multiple receiving antennas . We have performed diversity combining and have formulated the optimization problems with solutions during SP and SUTP . Moreover the method of estimating the channel between MCRU and CR receiver is integrated into the solution . Simulation results show that this method achieves 89 detection and 3.5 bits s Hz of achievable throughput while maximal ratio combining technique offers only 22 and 0.4 bits s Hz respectively when MCRU is active with ten times stronger power than the primary user . 


Summary: The detection performance of the cognitive radio CR network is improved when malicious cognitive radio user coexists in the network. Channel between malicious CR user and CR receiver is estimated. Throughput of the network is enhanced amidst the presence of malicious CR user. We have shown that detection performance and throughput are not compromised by the presence of malicious user.
Extracting river information from remote sensing images is of great importance in the investigation and monitoring of water resources and navigation of ships. In order to extract the river target from remote sensing images more accurately a method based on image decomposition and distance regularized CV Chan Vese model is proposed. Firstly the remote sensing image is decomposed based on tensor diffusion. The original image is decomposed into a cartoon image and a texture image and the river is contained in the cartoon part. Secondly the cartoon image is segmented based on the distance regularized CV model. Experimental results show that the method proposed is more accurate in extracting the river target comparing with 6 other methods including image segmentation based on CV model region scalable fitting energy level set model bias field correction level set model and some methods based on image decomposition and active contour model. 


Summary: A new river target extraction method is proposed based on image decomposition and distance regularized CV Chan Vese model. The original image is decomposed based on tensor diffusion and the river target is contained in the cartoon part. The river target is extracted from the cartoon image using distance regularized CV model.
The joint multipath routing and Network Coding method has been shown to improve the reliability and energy efficiency of multi hop relay wireless sensor networks . However NC is a kind of all or nothing code the destination node can not decode any information unless it successfully receives as many NC coded packets as the raw ones . Observed that WSNs often measure physical signals which show a high degree of correlation in this paper we propose a more deliberated scheme combined with Compressed Sensing . Depending on the proposed measurement and recovery method we prove that the original data can be recovered progressively while preserving the advantage of NC based scheme . Detailed mathematical analysis of the performance of our proposed scheme along with other existing schemes is given . The experimental results also show the efficiency and robustness of the proposed scheme . 


Summary: A scheme of reliable data transmissions for the WSNs is presented. The encoding decoding method is proposed and its effectiveness is proved. Performances of the proposed and other existing schemes are also analyzed.
We present a watermarking algorithm to synchronously transmit Gong Che notation musical scores and their musical instrument digital interface MIDI information. Our algorithm transforms the MIDI information into a binary sequence which is used to form watermarking data. This watermark is embedded into the Gong Che notation musical score image using an inward clockwise rotation. The algorithm then uses optical music recognition to judge whether the musical information in the image is loss. Experiments have validated the effectiveness of our watermarking and expansion algorithms. The expansion process increases transmission accuracy and the added processing time is minimal. Furthermore our methodology can be applied to other musical notations such as conventional musical notation or numbered musical notation. 


Summary: Achieve synchronous transmission of GCN musical scores and their MIDI information. Examine difference numbers of connected components in original and watermarked scores. Run expansion algorithm on original score to improve note extraction accuracy rate. Minimal added time complexity needed to expand the score and avoid information loss. Our algorithms are suitable for Conventional Musical Notation.
In this paper a novel architecture of Vedic multiplier with Urdhava tiryakbhyam methodology for 16 bit multiplier and multiplicand is proposed with the use of compressor adders. Equations for each bit of 32 bit resultant are calculated distinctly and compressor adders are used to implement these equations. They are chosen as they decrease vertical critical delay in comparison to the conventional architectures of compressors implemented using half and full adders only and so make the multiplier fast. The designs are coded in VHDL Very High speed Integrated Circuits Hardware Description Language and synthesized with Xilinx ISE 13.1 using Spartan 3e series of FPGA Field Programmable Gate Array . The combinational delay calculated for proposed 16 16 bit multiplier is 32 ns. Further speed comparisons of compressor adders with traditional ones and proposed multiplier with popular methods for multiplication are shown. Results clearly indicate the better speed performance of our proposed Vedic multiplier. 


Summary: A 16 bit Vedic multiplier with Urdhava Tiryakbhyam sutra is proposed. Higher compressor adders with the help of lower Compressor adders are proposed. Proposed compressor adders are used in architecture of Vedic multiplier. Proposed multiplier shows good speed results over Traditional multipliers.
Previous distributed file systems aim at storing very large data sets . Their architectures are often designed to support large scale data intensive applications which can not cope with massive daily users who want to store their data on the Internet . In this paper CSTORE is proposed to support mass data storage for a large number of users . The user independent metadata management can ensure data security through assigning an independent namespace to every user . Operating logs are applied to synchronize simultaneous sessions of the same user and resolve conflicts . We also implement a block level deduplication strategy based on our three level mapping hash method for the large quantity of repeated data . The migration and rank extension on the hash rules are defined to achieve load balancing and capacity expansion . Performance measurements under a variety of workloads show that CSTORE offers the better scalability and performance than other public cloud storage systems . 


Summary: A desktop oriented distributed public cloud storage system is proposed. Three level mapping hash method is used to distribute and locate data. Using migration and rank extension to implement load balancing and fault recovery. Sequence numbers are used to guarantee consistency. Implement data deduplication and use Bloom filter to recycle rubbish.
Botnet is one of the most serious threats to cyber security as it provides a distributed platform for several illegal activities . Regardless of the availability of numerous methods proposed to detect botnets still it is a challenging issue as botmasters are continuously improving bots to make them stealthier and evade detection . Most of the existing detection techniques can not detect modern botnets in an early stage or they are specific to command and control protocol and structures . In this paper we propose a novel approach to detect botnets irrespective of their structures based on network traffic flow behavior analysis and machine learning techniques . The experimental evaluation of the proposed method with real world benchmark datasets shows the efficiency of the method . Also the system is able to identify the new botnets with high detection accuracy and low false positive rate . 


Summary: A new traffic flow behavior analysis method has been proposed to detect botnets irrespective of their control structures. Benchmark datasets collected from various sources such as ISOT Botnet dataset from University of Victoria Conficker dataset from CAIDA dataset from CVUT University dataset from Dalhousie University and dataset from Centro University. It can successfully detect the various types of botnets with a high detection rate and a low false positive rate.
BPEL or Business Process Execution Language is so far the most important standard language for effective composition of Web services . However like most available process orchestration engines BPEL does not provide automated support for reacting according to many changes that are likely to arise in any Web services composition like downtime services modifications in the business logic or even new policies to govern the composition . Also low level specification of these new changes which would be integrated at runtime in the BPEL process will be far from being used conveniently . Moreover the complexity of interaction in composite Web services and the diversity of rules and policies can lead to critical behavioral conflicts . We propose in this paper AOMD a novel aspect oriented and model driven approach that defines new grammar to address both adaptability and behavioral conflicts problems and offers extension for WS BPEL meta model for high level specification of aspects . Further we formally verify our proposition and we present real life case study examples and experimental results that demonstrate the feasibility and effectiveness of our work . 


Summary: We provide aspect oriented constructs for context adaptable Web services composition. We afford aspect oriented constructs that can assure conflict free composition. We present new model that allows high level specification of AOP BPEL aspects. We formally verify consistency accuracy conflicts and deadlocks in the composition. We provide a plugin integrated in Eclipse BPEL environment.
Networks on Chip have emerged as a promising solution for the communication crisis in today s high performance Multi Processor System on Chip architectures . Routing methods have a prominent role in taking advantage of the potential benefits offered by NoCs . As a result designing high performance and efficient routing algorithms is highly desirable . In this paper the Hamiltonian based Odd Even turn model is proposed for both unicast and multicast routing in wormhole switched 2D mesh networks . HOE is able to maximize the degree of adaptiveness by minimizing the number of prohibited turns such that the algorithm remains deadlock free without adding virtual channels . By increasing the number of alternative minimal paths the hotspots are less likely to be created and the traffic is efficiently distributed throughout the network . The simulation results in terms of latency and power consumption indicate the better performance of the proposed method in comparison with the existing routing methods . 


Summary: A highly adaptive routing method is proposed for wormhole switched 2D mesh networks. The degree of adaptiveness is maximized by minimizing the number of prohibited turns. The deadlock freedom is guaranteed without adding virtual channels. The proposed method is minimal and can be used for unicast multicast routing. The number of hotspots is diminished and the traffic distribution is efficient.
The scalability of Network on Chip designs has become a rising concern as we enter the manycore era . Multicast support represents a particular yet relevant case within this context mainly due to the poor performance of NoCs in the presence of this type of traffic . Multicast techniques are typically evaluated using synthetic traffic or within a full system which is either simplistic or costly given the lack of realistic traffic models that distinguish between unicast and multicast flows . To bridge this gap this paper presents a trace based multicast traffic characterization which explores the scaling trends of aspects such as the multicast intensity or the spatiotemporal injection distribution for different coherence schemes . This analysis is the basis upon which the concept of multicast source prediction is proposed and upon which a multicast traffic model is built . Both aspects pave the way for the development and accurate evaluation of advanced NoCs in the context of manycore computing . 


Summary: Multicast traffic is characterized and modeled with an emphasis on scalability. Intensity concentration and burstiness increase with the system size. Growing correlation suggests the use of prediction to optimize NoC designs. Simple multicast source predictors achieve modest but promising accuracies.