The Transactional Conflict Problem

The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts is to immediately abort one of the transactions, some practical systems consider the alternative of delaying conflict resolution for a short interval, which may allow one of the transactions to commit. The challenge in the transactional conflict problem is to choose the optimal length of this delay interval so as to minimize the overall running time penalty for the conflicting transactions. In this paper, we propose a family of optimal online algorithms for the transactional conflict problem. Specifically, we consider variants of this problem which arise in different implementations of transactional systems, namely "requestor wins" and "requestor aborts" implementations: in the former, the recipient of a coherence request is aborted, whereas in the latter, it is the requestor which has to abort. Both strategies are implemented by real systems. We show that the requestor aborts case can be reduced to a classic instance of the ski rental problem, while the requestor wins case leads to a new version of this classical problem, for which we derive optimal deterministic and randomized algorithms. Moreover, we prove that, under a simplified adversarial model, our algorithms are constant-competitive with the offline optimum in terms of throughput. We validate our algorithmic results empirically through a hardware simulation of hardware transactional memory (HTM), showing that our algorithms can lead to non-trivial performance improvements for classic concurrent data structures

Systematic Risk Factors and Stock Return Volatility

This study analyzes the transmission of systematic risk exhaling from macroeconomic fundamentals to volatility of stock market by using auto regressive generalized auto regressive conditional heteroskedastic (AR-GARCH) and vector auto regressive (VAR) models. Systematic risk factors used in this study are industrial production, real interest rate, inflation, money supply and exchange rate from 2000-2014. Results indicate that there exists relationship among the volatility of macroeconomic factors and that of stock returns in Pakistan. The relationship among the volatility of macroeconomic variables and that of stock returns is bidirectional; both affect each other in different dynamics

UAV-assisted Cluster-head Selection Mechanism for Wireless Sensor Network Applications

The use of unmanned aerial vehicles (UAVs) is gaining popularity in many applications, i.e. data collection, surveillance, wireless sensor networks (WSNs) etc. In the WSN domain, the UAVs are used to create a more flexible data-gathering platform. This integration maximizes the lifetime of a WSN by optimizing the energy budget. In this paper, we have utilized these benefits of UAVs and have proposed an optimum cluster head (CH) selection strategy to maximize the lifetime of WSNs. The proposed method uses the average residual energy, the channel condition and the Euclidean distance of each sensor node (SN) with a UAV to nominate a group of CHs. Based on the initial analytical analysis, the proposed scheme maximizes the lifetime of a WSN by a fair amount in comparison to the state-of-the-art methods

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Generalized Regression Neural Network and Fitness Dependent Optimization: Application to energy harvesting of centralized TEG systems

The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. To overcome the TEG system behavior and shortcomings, A novel control technology for the TEG system is proposed, which utilizes the improved generalized regression neural network and fitness dependent optimization (GRNNFDO) to track the GMPP under dynamic operating conditions. Conventional TEG system control techniques are not likely to trace true GMPP. Our novel GRNNFDO can trace the true GMPP for NUTD and under varying temperature conditions In this article, some major contributions in the area of the TEG systems are investigated by solving the issues such as NUTD global maxima tracking, low efficiency of TEG module due to mismatch, and oscillations around optimum point. The results of GRNNFDO are compared with the Cuckoo-search algorithm (CSA), and grasshopper optimization (GHO) algorithm and particle swarm optimization (PSO) algorithm. Results of GRNNFDO are verified with experiments and authenticated with MATLAB/SIMULINK. The proposed GRNNFDO control technique generates up to 7% more energy than PSO and 60% fast-tracking than meta-heuristic algorithms

Advancing the State-of-the-Art in Hardware Trojans Detection

Over the past decade, Hardware Trojans (HTs) research community has made significant progress towards developing effective countermeasures for various types of HTs, yet these countermeasures are shown to be circumvented by sophisticated HTs designed subsequently. Therefore, instead of guaranteeing a certain (low) false negative rate for a small \textit{constant} set of publicly known HTs, a rigorous security framework of HTs should provide an effective algorithm to detect any HT from an \textit{exponentially large} class (exponential in number of wires in IP core) of HTs with negligible false negative rate. In this work, we present HaTCh, the first rigorous algorithm of HT detection within the paradigm of pre-silicon logic testing based tools. HaTCh detects any HT from $H_D$, a huge class of deterministic HTs which is orders of magnitude larger than the small subclass (e.g. TrustHub) considered in the current literature. We prove that HaTCh offers negligible false negative rate and controllable false positive rate for the class $H_D$. Given certain global characteristics regarding the stealthiness of the HT within $H_D$, the computational complexity of HaTCh for practical HTs scales polynomially with the number of wires in the IP core. We implement and test HaTCh on TrustHub and other sophisticated HTs

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next‐Generation Wireless Sensor Networks

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