Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.     

Determination of hydraulic retention time using a Piecewise linear regression analysis in the anaerobic digestion process

Hydraulic retention time (HRT) determination in the anaerobic digestion process is an important factor for reactor design. This study aims to investigate the optimal HRT determination method for beef manure and sawdust mixture in the anaerobic digestion (AD). The Piecewise linear regression (PLR) analysis method was applied to compare the HRT with MGT_P95 determined by the Modified Gompertz model analysis, cumulative methane yield (Cum.CH₄), cumulative biomass removal (Cum.BMR) and biodegraded volatile solids (PLT_BVS). Lab-scale (6 cases of lab-scale in the anaerobic digestion) experiments were performed and analyzed by Modified Gompertz model and Piecewise linear regression analysis. The parameters selected for Piecewise linear regression analysis: Cumulative methane yield (PLTCUM.CH4), cumulative biomass removal (PLTCUM.BMR), biodegradable volatile solids (PLTBVS) and time to produce 95% of methane yield potential from Modified Gompertz model (MGTP95). The HRT determined from PLTCum.CH4 varied in between 26.4~34.9 days, and which were 0.4~3.9 days, 8.3~16.0 days, and 9.4~22.2 days shorter than PLTCum.BMR, MGTP95, and PLTBVS, respectively. The daily maximum methane yield rate in the HRT from PLTCum.CH4 showing the highest daily methane yields rate which was less than 2.1%. Therefore, reactor utilization rate and organic loading rate for PLTCum. BMR, MGTP95, and PLTBVS were 94.4~99.4%, 78.0~84.4%, and 70.9~83.9%, respectively, lower than PLTCum.CH4. Conclusively, the HRT from PLTCum.CH4 is the most appropriate method to maximize the organic loading rate relative to other determination methods.     

Comparison and analysis of ten static heuristics-based Internet data replication techniques

This paper compares and analyzes 10 heuristics to solve the fine-grained data replication problem over the Internet. In fine-grained replication, frequently accessed data objects (as opposed to the entire website contents) are replicated onto a set of selected sites so as to minimize the average access time perceived by the end users. The paper presents a unified cost model that captures the minimization of the total object transfer cost in the system, which in turn leads to effective utilization of storage space, replica consistency, fault-tolerance, and load-balancing. The set of heuristics include six A-Star based algorithms, two bin packing algorithms, one greedy and one genetic algorithm. The heuristics are extensively simulated and compared using an experimental test-bed that closely mimics the Internet infrastructure and user access patterns. GT-ITM and Inet topology generators are used to obtain 80 well-defined network topologies based on flat, link distance, power-law and hierarchical transit–stub models. The user access patterns are derived from real access logs collected at the websites of Soccer World Cup 1998 and NASA Kennedy Space Center. The heuristics are evaluated by analyzing the communication cost incurred due to object transfers under the variance of server capacity, object size, read access, write access, number of objects and sites. The main benefit of this study is to facilitate readers with the choice of algorithms that guarantee fast or optimal or both types of solutions. This allows the selection of a particular algorithm to be used in a given scenario.     

Investigating quality routing link metrics in Wireless Multi-hop Networks

In this paper, we propose a new Quality Link Metric (QLM), “Inverse Expected Transmission Count (InvETX),” in Optimized Link State Routing (OLSR) protocol. Then, we compare performance of three existing QLMs which are based on loss probability measurements: Expected Transmission Count (ETX), Minimum Delay (MD), and Minimum Loss (ML) in Static Wireless Multi-hop Networks (SWMhNs). A novel contribution of this paper is enhancement in conventional OLSR to achieve high efficiency in terms of optimized routing load and routing latency. For this purpose, first we present a mathematical framework, and then to validate this frame work, we select three performance parameters to simulate default and enhanced versions of OLSR. The three chosen performance parameters are throughput, Normalized Routing Load, and End-to-End Delay. From the simulation results, we conclude that adjusting the frequencies of topological information exchange results in high efficiency.     

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the of 0.956.     

Response time analysis of multiframe mixed-criticality systems with arbitrary deadlines

Real-Time Systems - The well-known model of Vestal aims to avoid excessive pessimism in the quantification of the processing requirements of mixed-criticality systems, while still guaranteeing the...     

Synthesis of nano-phase ZrC by carbothermal reduction using a ZrO2–carbon nano-composite

Carbothermal reduction of Zr-sucrose gels powders into nano-phase ZrC, or ZrC-Zr(C,O) core-shell powders, via a composite of 2–4 nm sized ZrO₂ and amorphous carbon, is described. Samples with 1.7–20 sucrose-carbon:Zr ratio gels heated to 1495 °C at 10 °Cmin^?1, with 3 and 30 min hold time were studied in detail using; TG, XRD, SEM, TEM, STEM-EDX, and XPS with Ar⁺-ion etching. After 1495 °C, 3 min, the samples with 12-20C:Zr ratios yielded weakly agglomerated 30 to 40 nm sized ZrC particles, surrounded by a dense 5 nm thick shell of Zr(C,O). With 5C:Zr significant amounts of ZrO₂ was present after heating at 1495 °C for 3 min, while after 30 min annealing, ZrC particles without residual amorphous carbon was obtained. Minor amounts of zirconia was found in most samples, which in similarity with the 5 nm Zr(C,O) shell, is believed to stem from post synthesis oxidation.     

Potential efficacy of processing technologies for mitigating crustacean allergenicity

ABSTRACT

Crustacean allergy has become a growing food safety concern at a global scale. In the past decades, various food processing approaches have been employed to develop food products with reduced allergenic potential. Thermal treatment can dramatically influence the allergenicity of crustaceans by either reducing or enhancing their allergenic potential. Maillard reaction, enzymatic and acid treatments have shown to be promising in mitigating crustacean allergenicity. Recently, novel processing technologies, namely high-pressure processing, high-intensity ultrasound, irradiation, pulsed ultraviolet light and hurdle technology have attracted special attention from the researchers and the food industry professionals owing to their benefits over the conventional methods. In this context, this review paper provides an updated overview of the current knowledge on how different food processing methods induce structural changes of crustacean allergens and, subsequently, influence their allergenic potential. Data on prevalence and clinical relevance of crustacean allergy are presented, as well as, the molecular characterization of crustacean allergens and the main analytical methods for their detection in processed foods.     

On multiprocessor task scheduling using efficient state space search approaches

Obtaining an optimal schedule for a set of precedence-constrained tasks is a well-known NP-complete problem in its general form. In view of the intractability of the problem, most of the previous work relies on heuristics that try to find reasonably high quality solutions in an acceptable amount of time. While optimal polynomial-time algorithms are known only for a few simple cases (and in other cases can only be obtained through an exhaustive search with prohibitively high time complexity), they may be critically important for applications in which performance is the prime objective. Optimal solutions can also serve as a reference to test the performance of various heuristics. Moreover, an optimal schedule for a program at hand needs to be determined only once (and off-line) but the program using that schedule is in general executed several times. In this paper, we propose optimal algorithms for static scheduling of task graphs with arbitrary parameters to multiple homogeneous processors. The first algorithm is based on the A^* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity. Additionally, we propose a number of effective state-pruning techniques to reduce the search space. For further lowering the complexity, we propose an efficient parallelization of the search algorithm. We parallelize the algorithm with reduced interprocessor communication as well as with static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but executes in a considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions of reasonably large problems while the approximate algorithm is effective if slightly degraded solutions are acceptable.     

MPEG-4 Video Transmission over Wireless Networks: A Link Level Performance Study

With the scalability and flexibility of the MPEG-4 and the emergence of the broadband wireless network, wireless multimedia services are foreseen to become deployed in the near future. Transporting MPEG-4 video over the broadband wireless network is expected to be an important component of many emerging multimedia applications. One of the critical issues for multimedia applications is to ensure that the quality-of-service (QoS) requirement to be maintained at an acceptable level. This is further challenged in that such a service guarantee must be achieved under unreliable and time-varying wireless channels. In this paper we study the link level performance of MPEG-4 video transmission over the uplink of an unreliable wireless channel. We introduce the discrete time batch Markovian arrival process (DBMAP) with two types of arrivals to model the MPEG-4 video source, which takes into account the inherent nature of the adaptiveness of the video traffic. We prove that in a hidden Markov modeled (HMM) wireless channel with probabilistic transmission, the service time for an arbitrary radio link control (RLC) burst follows phase type (PH-type) distribution. We show that the link level performance of a wireless video transmission system can be modeled by a DBMAP/PH/1 priority queue, and present computation algorithm and numerical results for the queueing model. Extensive simulations are carried out on the queueing behavior of the video transmission buffer, as well as on the packet level error behavior of the video data. The results demonstrate that video quality can be substantially improved by preserving the high priority video data during the transmission.