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PurposeThe purpose of this paper is to propose the best method of maintenance for electrical equipment of a building and especially elevators in order to achieve various benefits.Design/methodology/approachLiterature review shows that there are various methods such as corrective maintenance, condition-based maintenance, time-based maintenance and reliability-centered maintenance (RCM) for the maintenance of electrical equipment of a building. Selecting the most appropriate maintenance method, especially in large buildings such as commercial ones, is a multiple-criteria decision-making problem. To solve this problem, the fuzzy analytic hierarchy process is used in the present study. It considers the uncertainties of the experts' judgments to provide an optimized maintenance method.FindingsTheoretically, the RCM method will take a lot of time and cost. But in prioritizing the methods, RCM gained more points with regard to safety criteria and value added. The main finding of this study suggests that RCM can be used in the case of elevators and would improve the safety and reliability of buildings.Originality/valueThe authors believe that this study will be valuable because one of the problems in building maintenance complexes is the lack of knowledge about the best method to prevent the risks of elevators. The proposed method has compared with other methods in various sub-criteria, and the results have been fully analyzed.

In this work, we develop a model for forecasting the stock market using News Headlines. The main step of the work is to pre-process daily news, as well as Opinion Finding method to generate features for the prediction problem. The target variable is a binary variable which takes value of 1, when the Dow Jones Industrial Average (DJIA) increases, and zero otherwise. A deep learning model with Long Short-Term Memory (LSTM) architecture to improve the prediction of the changes in DJIA. As a baseline, a time series data of DJIA is used without daily news. Then, we develop a model of time series prediction along with N-Gram of daily news. We discuss how N-gram model can help to improve the baseline. In the main pre-processing task, we develop a sentiment analysis (using polarity lexicons). This model assigns weights as sentiment polarity to all the news headlines. We also find the scores for degree of subjectivity and objectivity in news headlines, and we use them as additional features in our model. In experiments, an LSTM model is developed. A grid search is used to find the best architecture along with the effect of generated features from text pre-processing and Opinion Finding on time series forecasting.

Despite the growing popularity of field-programmable gate arrays (FPGA), this technology still suffers from drawbacks such as larger area, higher power consumption and longer runtime than application-specific integrated circuits (ASIC). Reconfiguration capability gives FPGAs an advantage over ASIC and can make it a more appropriate option for implementing some applications. One important issue regarding the reconfiguration process is the time it takes to carry out this procedure. This paper seeks to accelerate the reconfiguration process by using a bitstream compression method with a primary focus on FPGA architecture. Since multiplexers are among the main components of FPGAs, the proposed method is focused on encoding and compression of configuration bitstream for such resources. In addition, the bitstream format and the decompression operation are designed to obtain desirable results in terms of area overhead and time improvement. Results of experiments show that in comparison to the basic scheme, the proposed design improves the reconfiguration time by 33%.

Estimation techniques such as Unscented Kalman filter (UKF) are deployed for accurate joint location estimation in cooperative localization of cyber physical systems (CPS), e.g., to locate each robot in a cooperative mobile robotic network in GPS-denied environments. In order to avoid single point of failure in the centralized implementation of such estimation techniques, the decentralization of estimation algorithms has attracted considerable attention in the past two decades. However, the design of decentralized algorithms with reduced communication cost without loss in accuracy for compute-intensive estimation techniques such as UKF has been challenging. In the decentralized UKF, the tasks are partitioned and computed locally at robot nodes. Data communication overhead is overwhelming due to tight data dependency between the robots' computations. In this paper, we present a CPS framework for UKF decentralization in which computation and communication are tightly intertwined and computation replication is deployed in order to reduce the communication overhead among cooperative mobile robots. We demonstrate and evaluate the performance of our proposed work in a wireless network of 15 Raspberry Pi 3 B, with quad-core 1.2GHz 64bit CPU, emulating a network of mobile robots with onboard computation and communication capabilities. Our experimental results show that the End-to-End execution time of decentralized UKF prediction and update steps with replication are faster by up to 12.29 and 3.57 times, respectively, compared to the partially decentralized UKF algorithm of [1].

One of the major challenges in the process of three-dimensional integrated circuit fabrication is the manufacturing of through silicon vias (TSV). These TSVs compared with other connection elements require high manufacturing costs as well as large silicon area. In this paper, replication technique has been used to reduce the number of TSVs in 3D FPGAs. Replication is implemented for circuit input pads and logic blocks. Experimental results over 20 MCNC benchmarks show 33% and 20% reduction in the number of TSVs and delay on average, respectively, at the cost of 3% more logic blocks and 3% more input pads that they place in the unused resources of FPGA.

SRAM-based FPGAs suffer from soft errors caused by cosmic particles. This paper introduces a new switch box architecture to mitigate soft errors. In this switch box architecture, the number of SRAM bits required for programming the switch boxes is reduced by means of switch reduction with slight impact on routing capability of the switch box. This architecture does not require any modification of the existing placement and routing algorithms. The proposed architecture was evaluated on several MCNC benchmarks using VPR tool. The experimental results show that this architecture decreases the susceptibility of switch boxes to single event upsets by about 18% on average compared to the traditional ones. Also, our architecture decreases the probability of ridging and short faults in the switch boxes by about 32% on average.

In recent years, parameter variations present critical challenges for manufacturability and yield on integrated circuits. In this paper, a new method for improving the timing yield of field programmable gate array (FPGA) devices affected by random and systematic within-die variation is proposed. By selection of an appropriate configuration from a set of functionally equivalent configurations average critical path delay is reduced under conditions of large random and systematic variation considering spatial correlation. Compared to the previous approach which is limited to a fixed placement, our method improves timing yield by attempting several placements and routings without lengthy placement and routing phases to handle systematic variations and spatial correlation. The average critical path delay is reduced by 7% compared to the previous work over 20 MCNC benchmarks. General Terms: FPGA, process variation, placement, routing

Mammography is a low dose x-ray technique that's used to create an image of the breast. It is an efficient way for early detection of any cancerous changes and malignancy of lumps. Mammographic images are usually archived and in many cases are transferred on Internet. Therefore, compression of these images has attracted the attention of many researchers. In this paper an efficient method is proposed for lossless compression of mammographic images. Gradual compression of prediction errors in an iterative manner is the basic idea of the proposed method. The simulation results were compared with standard image compression routines such as JPEG-LS, JPEG2000, JBIG and PNG. The superiority of the proposed method was shown for compression of mammographic images.

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces large amount of image data, hence efficient compression routines that exploit microarray image structures are required. In this paper we introduce a lossless image compression method which segments the pixels of the image into three categories of background, foreground, and spot edges. The segmentation is performed by finding a threshold value which minimizes the weighted sum of the standard deviations of the foreground and background pixels. Each segment of the image is compressed using a separate predictor. The results of the implementation of the method show its superiority compared to the well-known microarray compression schemes as well as to the general lossless image compression standards.