nils-herrmann/clean_scirepeval huggingface dataset:
A new partitioning framework for uniform clock distribution during high-level synthesis

This weighted cluster partitioning technique is aided by an innovative resource allocation technique which provides a uniform resource utilization. This results in a uniform power requirement distribution across the synthesized chip which is practically beneficial as well. The resource cluster information obtained from this new partitioning synthesis framework could be used to aid the hierarchical placement and routing tools as well as the clock-router to design and produce a close to optimal uniform clock routing. This new framework applies the idea of local (intra-cluster) communication, while minimizing global communication busses. This resulted in a low-power design as well. The partitioner cost function considers three factors: the number of clock lines per unit area, the number of cuts between clusters, and the size of partitions. Experimental results show that this partitioning framework provides uniform clock distribution among clusters as well as fairly uniform sized partitions.
Three-dimensional defect sensitivity modeling for open circuits in ULSI structures

In the last three decades, device feature size has been reduced from ten's of microns to sub-0.5 /spl mu/m, shaping what is commonly called the ultra large scale integration (ULSI) era. At the same time, there has also been an appreciable decrease in the defect density over these years. While some of the defects have been completely eliminated, many still remain in modem fab lines. In view of the continuing trend of feature size reduction, the three-dimensional (3-D) nature of these defects is likely to have an impact on the functionality of integrated circuit (IC) structures. An open circuit is conventionally modeled as a break in a conductor due to an insulating defect. For 3-D defects and conductors, this physical interpretation of open circuits may not be the most appropriate one. In this paper, we present a parametrized modeling of open circuits for 3-D defects in ULSI processing. In the proposed approach, the maximum allowable increase in a conductor's resistance due to a partially or completely embedded insulating defect is suggested as the criterion for an open circuit. Analytical expressions for ULSI defect sensitivity are then obtained.
TALS: Trigonometry-based Ad-hoc Localization System for wireless sensor networks

Node localizations techniques are a critical part in many wireless sensor network (WSN) applications. There are several challenges for building an efficient localization system that is suitable for wireless sensor networks, mainly, reducing computational complexity and communication overhead. For example, solving an over constraint set of linear equations via least square approximations and utilizing square root operations are computationally intensive. Moreover, flooding techniques used for transmitting the locations of the anchors wastes bandwidth and energy. In this context, the Trigonometric based Ad-hoc Localization System (TALS) is an anchor-based range-based localization system that utilizes trigonometric identities and properties to compute the position of the node. TALS is designed to address the above challenges without deteriorating the quality of the estimates by taking advantage of redundancy and data fusion techniques. TALS is simulated and compared against popular localization techniques where it presented superiority against those techniques.
High Performance On-demand Video Transcoding Using Cloud Services

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics (e.g., spatial resolution) of clients' devices. Transcoding is a computationally expensive operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. However, recent studies show that accessing video streams have a long tail distribution. That is, there are few popular videos that are frequently accessed while the majority of them are accessed infrequently. The idea we propose in this research is to transcode the infrequently accessed videos in a on-demand (i.e., lazy) manner. Due to the cost of maintaining infrastructure, streaming service providers (e.g., Netflix) are commonly using cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address the challenge, in this research, we present an architecture for on-demand transcoding of video streams. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) requirements of video streams. In particular, the architecture includes a QoS-aware scheduling component to efficiently map video streams to cloud resources, and a cost-efficient dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS requirements.
Post-Silicon Clock-nvert (PSCI) for reducing process-variation induced skew in buffered clock networks

Proposed in this paper is a post-silicon technique and circuits that reduce random process-variation induced skew with simple leaf buffers modification of a buffered clock network. If a timing violation due to clock skew occurs during testing, the present technique offers a second chance via Post-Silicon Clock-Invert (PSCI), which decreases the probability of having errors due to clock skew thus increasing timing yield or design performance. In the cases where the clock frequency of each chip is adjusted post-silicon to deal with inter-die process variations, this technique allows significant number of chips to operate at higher frequencies. The present technique does not require any modifications to the clock network and repower buffers, thus it has negligible area, power, and design overheads, and its post-silicon activity is simple and fast. Evaluated via Monte-Carlo simulation in the context of a 16 leaves buffered H-Tree topology in a 10-mm×10-mm synchronous region, PSCI shows an improvement of 19.56%, 28.39%, and 30.3% in the global clock skew mean, standard deviation, and worst case, respectively.
A novel high write speed, low power, read-SNM-free 6T SRAM cell

In the nano-scaled technologies, increasing sub-threshold leakage, dynamic power and degrading SNM pose major hurdle for future generation circuits, especially in SRAM arrays. In this paper, a novel high write speed, low power, read-SNM-free 6T SRAM cell is presented. Simulation using 128 times 16 SRAM array in 90 nm CMOS technology shows that the cell can achieve 64% faster write operation than the conventional cell. Experimental results show that the write and read energy of the proposed cell are 76.8% and 53% lesser than the conventional cell respectively. Write precharge energy for the proposed cell is almost 81% less than that of conventional cell. During read operation, the proposed cell does not induce any noise at data nodes (dasiaQpsila & dasiaQbarpsila) which makes it a read-SNM-free design.
A fast discrete transform architecture for Frequency Domain Motion Estimation

Frequency Domain Motion Estimation (FDME) is a recent technique that promises to efficiently reduce the computational complexity of ME process. Related Transformed-Discrete Cosine Transform (RT-DCT) is one of the main modules that build the FDME encoder. The RT-DCT module is responsible for generating four transforms that are required for the ME process in the frequency domain. The main problem of generating such transforms is the low speed of the FDME encoder that prevents its use in real time applications. In this paper an efficient fast RT-DCT architecture is proposed to accelerate the encoding process in the frequency domain. The proposed architecture achieves approximately 58%, 39%, and 50% reductions in gate count, power consumption, and area compared to the conventional state of the art pipelined RT-DCT generators. Implementation and Simulation results project that the proposed RT-DCT architecture, when integrated in a whole FDME system, can perform ME for 60 fps of 4CIF video at 118 MHz.
Design, Implementation and Characterization of Practical Distributed Cognitive Radio Networks

in distributed cognitive radio networks (CRNs) has been well studied in the literature from a theoretical perspective. However, such theoretically-optimized distributed OSA approaches are challenged by several practical implementation issues. In this paper, we design a custom cross-layer framework that enables the: (i) clean-slate implementation of a wide variety of OSA mechanisms; (ii) experimental evaluation of the individual practical OSA components of the Rate-Adaptive Probabilistic (RAP) framework; and (iii) detailed comparison of the performance of such a practical OSA approach against theoretical OSA approaches developed for fully-capable CRNs. Our evaluation reveals the multi-fold goodput improvement and remarkable fairness characteristics of the practical RAP OSA approach compared to the OSA approaches that overlook the OSA and CR practical limitations. However, the superior performance of practical OSA comes at the expense of more outages to the primary licensed networks but within the permissible bounds. Another key finding is that the wide family of existing theoretically-optimized OSA protocols can benefit from the gains available to the individual components of the practical RAP approach, namely, the random spectrum sensing and the probabilistic non-greedy access.
Compressed domain texture classification from a modified EZW symbol stream

Researchers have demonstrated the effectiveness of wavelet subband energy values as a feature for representing and classifying texture images. However, the extraction of these texture features from compressed data can be cumbersome using traditional decompress-process approaches. A method has been developed for calculating wavelet energy features directly from a compressed embedded zerotree wavelet (EZW) symbol stream. The resulting technique is efficient and requires less memory than traditional approaches. In order to simplify the detection of subbands within the compressed data stream, end-of-subband markers have been inserted during the dominant pass of the EZW coding process. After compressing the test image, the reconstruction values described in Shapiro (1993) are used to calculate the energy of each subband. Following this technique, the memory requirements are reduced since the image is no longer reconstructed prior to the energy calculations. Additionally, the potentially large reconstruction matrix is no longer traversed which also reduces the time complexity.
A fast isointensity maps based approach to optimal motion field computation for MPEG coding

Given a video sequence characterized by correlations in space and time, the objective of motion estimation is to find and accurately represent such correlations. Block matching algorithms are mainly used as the core of the motion estimation unit. Search algorithms include FSBMA, TSSMA, logarithmic etc.. At least 3 isointensity centroids in two frames are required to compute 2-D affine motion. Access to up to 8 isointensity centroids allow for two independent affine motions. Virtual displacement method is used to form linear constraints
Spectrum-Awareness-Based Performance and Scalability of Cognitive Radio Networks

Spectrum awareness (SA) stretches the performance bounds of spectrum-sensing-based dynamic spectrum access by intelligently exploiting the big spectrum data (BSD) generated by a network. Hence, in order to analyze the performance and scalability of large scale cognitive radio networks (CRNs), spectrum awareness capacity would take preeminence over spectrum sensing capacity. Although, conventional methods use techniques such as the receiver operating characteristic (ROC) curve and the root mean square error (RMSE) technique to quantify the performance of CRN SA, they do not consider the impact of BSD velocity, variety and volume. Therefore, this research work proposes a novel knowledge-centric method for quantifying, analyzing and comparing the performance and scalability of CRNs based on spectrum awareness. The proposed method considers key performance indices including reliability, computational complexity and latency of the network parameters that are generated by spectrum data acquisition, conversion and dissemination. The steps and applicability of the proposed method in user and network-level performance measurement are also analyzed.
Hopfield associative memory on mesh

The associative Hopfield memory is a very useful artificial neural network (ANN) that can be utilized in numerous applications. Examples include pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. This paper provides an algorithm for implementing the Hopfield ANN on mesh parallel architectures. A Hopfield ANN model involves two major operations; broadcasting a value to a set of processors and summation of values in a set of processors. The main advantage of this algorithm is a high performance and cost effectiveness. An iteration of an N-bit (neuron) Hopfield associative memory only requires O(logN) time, whereas other known algorithms in literature of similar topology require O(N) time. Moreover, the proposed algorithm is cost effective because only higher dimension architectures were reported to achieve a complexity of O(logN) such as hypercubes.
A theta (log n) algorithm for modulo multiplication

A theta (log n) algorithm for large moduli multiplication for residue-number-system (RNS)-based architectures is proposed. The modulo multiplier is much faster than previously proposed multipliers, and more area efficient. The implementation of the multiplier is modular and is based on simple cells, which leads to efficient VLSI realization. A VLSI implementation using 3- mu m CMOS process shows that a pipelined n-bit modulo multiplication scheme can operate with a throughput of 30M operations/s.<<ETX>>
New automatic error control system with ATM cell header reduction for wireless ATM networks

In this paper we introduce a new error control system for wireless asynchronous transfer mode (WATM) networks. The new system is based on using a new scheme to reduce the ATM cell header size before applying the multiple error correcting Bose-Chaudhuri-Hocqenghem (BCH) code instead of the usual header error control (HEC) for the wireless portion of the transmission. This new scheme implies dividing the ATM cell header into segments, then comparing all segments with the first one to generate reduced length codes to represent the compared segments. The result of applying the new technique for the wireless portion of the network is a reduced header length which has an advantage in reducing delay while maintaining the required cell loss probability limit. The newly suggested system is also an automatic system with respect to the level of BCH encoding. When the bit error rate (BER) is high, the system increases its error correction capability and vice versa. Realistic multi-path fading error patterns were analytically approximated and applied to evaluate the system performance. The results show that the new system achieves an encoding delay which is considerably less than the encoding delay of a fixed BCH system with 3 error correction capability and an original HEC system, while maintaining the required limit of cell loss.
Fully Decentralized Weighted Kalman Filter for Wireless Sensor Networks with FuzzyART Neural Networks

A framework for developing a decentralized weighted Kalman filter with FuzzyART neural networks is presented. Active sensors communication is restricted to a node-to-node basis in the vicinity of each cluster where readings are only shared between neighbors. FuzzyART neural network takes these readings and classifies them into categories based on estimated measurement accuracy where appropriate weights are assigned to each measurement according to a decision tree. Few modifications have been made to the traditional Information Kalman Filter to adapt with the algorithm developed herein. The FuzzyART model is layered below the Kalman filter in a way it detects faulty measurements and spatial alteration of the target and accommodates these changes to better estimate the target. Sensors with faulty measurements are inhibited form participating in the estimation process and its readings are neither processed nor further communicated thus achieving higher energy efficiency. Furthermore, introducing a FuzzyART model lies within the complexity constraints of a sensor node and have acceptable overhead. Results and simulations show the superiority of the proposed algorithm when compared with the traditional Information Filter.
A low power turbo decoder architecture

A new method is proposed to decrease the power consumption of turbo decoders. Turbo decoders iteratively decode the received sequence by exchanging extrinsic information. In the proposed method, the exchange of extrinsic information that exceeds a certain threshold is terminated and, instead, a predefined value is exchanged. This method enables reduction of memory accesses to both the interleaver memory and state metric memory, hence reducting power consumption. Simulation and synthesis results shows up to 25% reduction of power consumption at E/sub b//N/sub O/ of 1.5 dB, compared to the conventional state parallel architecture.
Wireless sensor networks: a new communication paradigm

Computers, communication, and sensing technologies are converging to change the way we live, interact, and conduct business. Wireless sensor networks reflect such convergence. These networks are based on collaborative efforts of a large number of sensor nodes. They should be low-cost, low-power, and multifunction. These nodes have the capabilities of sensing, data processing, and communicating. Sensor networks have wide range of applications, from monitoring sensors in industrial facilities to control and management of energy applications to military and security fields. Because of the special features of these networks, new network technologies are needed for cost effective, low power, and reliable communication. These network protocols and architectures should take into consideration the special features of sensor networks such as: the large number of nodes, their failure rate, limited power, etc. In this talk the impact of wireless sensor networks is addressed, several of the design and communication issues is discussed, and a case study of a current project of using such networks in drilling and management off-shore oil is given.
An efficient low-bit rate motion compensation technique based on quadtree

A quadtree structured motion compensation technique effectively utilizes the motion content of a frame as oppose to a fixed size block motion compensation technique. In this paper, we propose a novel quadtree-structured region-wise motion compensation technique that divides a frame into equilateral triangle blocks using the quadtree structure. Arbitrary partition shapes are achieved by allowing 4-to-1, 3-to-1 and 2-to-1 merge/combine of sibling blocks having the same motion vector. We propose an optimal code scheme and a temporal predictive coding for the quadtree. Simulation results show that our techniques reduce the bit rate by 40% compared to other methods.
Performance analysis for a new automatic error control system for overcoming atmospheric multipath fading with random bit errors for wireless ATM networks

This paper suggests and analyzes the performance of a new wireless ATM adaptive error control system, which can be used in wireless ATM networks to improve the quality of service in keeping the cell loss under a certain predefined limit and reducing the average delay of cell encoding. The new system is based on multi-level forward error correction (FEC) and specifically the Bose-Chaudhuri-Hocqenghem (BCH) code, which has multiple error correcting capability and a wide class of block codes, to overcome random errors. Realistic error patterns caused by multipath fading are analytically approximated and applied to the suggested system. Performance analysis for the new system showed that it improves the cell loss rate to a value of less than 10/sup -9/ in case of non-fading and it keeps the average cell loss close to the limit achieved by using a fixed BCH error correction system, while maintaining an average encoding delay considerably less than the delay of a system using a fixed BCH correction capability.
A Novel Authentication and Key Agreement Protocol for Internet of Things Based Resource-Constrained Body Area Sensors

Secrecy and integrity of communication, as well as the authenticity of messages must be ensured by the security primitives and is critical element in Internet of Things (IoT). Devices in IoT applications such as smart healthcare applications deal with sensitive data of our health and daily lives and thus can execute malicious actuating commands with serious safety consequences. Therefore, it is important to ensure secure communication channel establishment among the medical sensors or devices and the remote servers. In this work, we propose a novel authentication and key agreement protocol that is lightweight for resource-constrained sensors and suitable to protect sensitive health related data in the context of IoT. The protocol is based on cooperation by offloading the heavy cryptographic functions of resource-constrained sensors to the trustful neighboring sensors. This trust verification is ensured by finding the data correlations from the data of embedded accelerometers in the smartphone acting as gateway and the sensors installed on the body. Security analysis and performance evaluation results show that the proposed protocol is secure and is energy efficient.
Dark silicon-power-thermal aware runtime mapping and configuration in heterogeneous many-core NoC

To address power-thermal-dark silicon issues in many-core chip, run time task-resource and voltage co-allocation with reconfigurable network-on-chip (NoC) framework for energy and hotspots minimization is proposed in this work. At runtime, the global manager with the help of proposed MinEnergy mapping algorithm reconfigures the NoC links bandwidth and nodes voltage-level and power-gated the resources depending on the traffic demand and resource statistics collected from the distributed cluster-managers. MinEnergy mapping algorithm minimizes overall chip power and thermal hotspots in heterogeneous large-scale NoC. We have formulated the mapping and configuration problem into a linear optimization model and implemented a traditional minimum-path contiguous mapping for comparisons. Simulations show that MinEnergy dynamic mapping solution is 80–90% close to the optimal solution, and significantly better than the minimum-path mapping solution.
EVA-MAC: An event-based adaptive medium access control for wireless sensor networks

Event-based systems are typically characterized by having long periods of sleep and short abrupt periods of high data traffic when an event is detected. High throughput event-based sensor network applications are severely limited by the medium access control layer. EVA-MAC is a medium access control that is tailored specifically for event based systems. In event-based systems, multiple sensors that are in communication range of each other collaborate to execute a specified task. As the number of sensors per hop increases, high contention is generated as multiple sensors detecting the event contend to deliver data to the base station. This contention will lower the throughput, increase latency and deteriorate the application's performance. EVA-MAC is simulated with NS2 and compared with an implementation of S-MAC and 802.11. EVA-MAC presented its superiority in throughput, Latency and energy consumption.