PMSS: A programmable memory system and scheduler for complex memory patterns

HPC industry demands more computing units on FPGAs, to enhance the performance by using task/data parallelism. FPGAs can provide its ultimate performance on certain kernels by customizing the hardware for the applications. However, applications are getting more complex, with multiple kernels and complex data arrangements, generating overhead while scheduling/managing system resources. Due to this reason all classes of multi threaded machines–minicomputer to supercomputer–require to have efficient hardware scheduler and memory manager that improves the effective bandwidth and latency of the DRAM main memory. This architecture could be a very competitive choice for supercomputing systems that meets the demand of parallelism for HPC benchmarks. In this article, we proposed a Programmable Memory System and Scheduler (PMSS), which provides high speed complex data access pattern to the multi threaded architecture. This proposed PMSS system is implemented and tested on a Xilinx ML505 evaluation FPGA board. The performance of the system is compared with a microprocessor based system that has been integrated with the Xilkernel operating system. Results show that the modified PMSS based multi-accelerator system consumes 50% less hardware resources, 32% less on-chip power and achieves approximately a 19x speedup compared to the MicroBlaze based system.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Multi‐agent collaboration based on enhanced cognitive awareness: an architecture for agents' profiling on the semantic web

This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge‐based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent‐based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi‐agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge‐base. The conceptual model for the cognitive profile architecture is developed with Protégé Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically.     

Multiple human tracking in high-density crowds

In this paper, we introduce a fully automatic algorithm to detect and track multiple humans in high-density crowds in the presence of extreme occlusion. Typical approaches such as background modeling and body part-based pedestrian detection fail when most of the scene is in motion and most body parts of most of the pedestrians are occluded. To overcome this problem, we integrate human detection and tracking into a single framework and introduce a confirmation-by-classification method for tracking that associates detections with tracks, tracks humans through occlusions, and eliminates false positive tracks. We use a Viola and Jones AdaBoost detection cascade, a particle filter for tracking, and color histograms for appearance modeling. To further reduce false detections due to dense features and shadows, we introduce a method for estimation and utilization of a 3D head plane that reduces false positives while preserving high detection rates. The algorithm learns the head plane from observations of human heads incrementally, without any a priori extrinsic camera calibration information, and only begins to utilize the head plane once confidence in the parameter estimates is sufficiently high. In an experimental evaluation, we show that confirmation-by-classification and head plane estimation together enable the construction of an excellent pedestrian tracker for dense crowds.     

Ear recognition with feed-forward artificial neural networks

Biometrics has become one of the most important techniques in recognizing a person’s identity. A person’s face, iris and fingerprint are mostly used in biometrics today. It has been established that there are no two ears exactly alike, even in the cases of identical twins. In this paper, we define a 7-element ear feature set and design and train a feed-forward artificial neural network to recognize a human ear. We train and test the network with 51 ear pictures from 51 different persons. Simulation experiments with various networks with various number of layers and number of neurons per layer and with and without noise are conducted. Results indicate that a 95 % ear recognition accuracy is achieved with a simple 3-layer feed-forward neural network with only a total of 18 neurons even in the presence of some noise. This design outstands previous work in simplicity and implementation cost.     

Alternative approach to compute shear amplification in high‐rise reinforced concrete core wall buildings using uncoupled modal response history analysis procedure

The seismic response of the high‐rise reinforced concrete (RC) wall structures is really complicated as several vibration modes other than the fundamental mode normally contribute significantly to the response—commonly recognized as ‘higher mode effects’. Response spectrum analysis (RSA) procedure, which can account for higher mode effects, is usually employed to compute the seismic design demand for the high‐rise structures. Recent studies show that the inelastic seismic force demands obtained from the rigorous nonlinear response history analysis procedure are much larger than the seismic force design demands obtained from the code‐based RSA procedure for the high‐rise RC wall structures. Though, the nonlinear response history analysis procedure is widely accepted for its ability to provide the most accurate estimate of nonlinear seismic responses, the obtained responses are generally so complex that it is quite difficult for engineers to grasp the overall picture of the responses and gain some insight into them and use them to understand the cause of high seismic demands. Another important issue related to the nonlinear seismic response prediction of the high‐rise RC wall structures is the realistic and accurate numerical modeling of RC walls. In this study, a simplified but reasonably accurate procedure called the uncoupled modal response history analysis procedure is used to interpret the complex nonlinear behavior of high‐rise RC wall structures. Moreover, a finite element model based on modified compression field theory is employed for accurate numerical modeling of RC walls by incorporating the axial‐flexure‐shear interaction. This study, by making use of a better computer modeling approach and an in‐depth analysis by modal decomposition, aims to resolve some of the unanswered questions regarding realistic prediction of nonlinear seismic demands of high‐rise structures.     

Low temperature synthesis of <Emphasis Type="Italic">Manganese tungstate</Emphasis> nanoflowers with antibacterial potential: Future material for water purification

Pure water is the fundamental requisite for human life. The water has been recycled naturally but not in an adequate amount for consumption. Nanotechnology with extraordinary applications provides competent ways for the decontamination of contaminated water. In the present study MnWO₄ nanoflowers endorsed with inherent antibacterial activity were successfully synthesized by facile hydrothermal approach. XRD, SEM, EDX spectroscopy and UVDRS were used to characterize the as-synthesized nanoflowers. Gram negative Escherichia coli ATCC 52922 bacterium was used as model organism to test antibacterial activity of as-synthesized MnWO₄ nanoflowers. This study was conducted to optimize minimum concentration of MnWO₄ nanoflowers and maximum contact time required to achieve complete inactivation of bacteria present in contaminated water. Minimum inhibitory concentration (MIC) of MnWO₄ nanoflowers was found to be 10 μg/ml. The assessment and interpretation of bacterial viability was done using dual fluorescent staining. The synthesized 3D-nanoflowers were found as potent bactericides. Thus, MnWO₄ nanoflowers emerged to be very good future material for disinfection of biological pollutants present in the contaminated water reservoirs and as an anti-biofouling agent.