ABSTRACT: In the embedded system applications the RISC-ARM architecture is proven to be very much useful by providing several advantages. The three stage pipeline structure i.e., ARM7 TDMI architecture is the highest sold chip till now in comparison with all other micro controllers. The ARM7 has become so popular due to its data path design consisting ALU, Barrel shifters, CPSR register. This architecture allowed optimised assembly language coding for several applications resulting low power designs. The paper scope includes the study of 3-stage pipeline instruction set and implementing the ALU. The barrel shifter will be implemented to optionally shift one of the input before going to the ALU. In this paper, 3-stagedata path structure consists of ALU, Barrel shifters, CPSR register unit blocks will be implemented in VHDL. In the ASIC Digital design the RTL Compiler and SOC Encounter are used for simulation and synthesis purpose and finally we obtain the GDSII file. This paper uses TSMC 45nm technology libraries for implementation. RTL Complier tool will be used for functional simulation (RTL SCHEMATICS) and verification of data path structure. SOC ENCOUNTER tool will be used to synthesize the blocks. This data path structure which can be used for different applications like Military, Automation and Consumer Electronics.
Collection of all relevant data and information is done during this stage. Data will be collected mainly through documentary analysis. All collected data and information are recorded systematically. Data collected are mainly from the Malayan Law Journal, Singapore Law Report, Building Law Report, Construction Law Report and other law journals. It is collected through the Lexis-Nexis online database. All the cases relating to the research topic will be sorted out from the database. Important cases will be collected and used for the analysis at the later stage.
Collection of all relevant data and information is done during this stage. Data will be collected mainly through documentary analysis. Data collected are mainly from Googles, the Malayan Law Journal, Singapore Law Report, UK Cases & Combined Courts, Malaysia & Brunei Cases and Law Reports from Wales and England. It is collected through the Lexis-Nexis online database. All the cases relating to the research topic will be sorted out from the database. Important cases will be collected and used for the analysis at the later stage.
used to select which source be forwarded. The Most Significant Bit (MSB) of the signal indicates the condition of the data, whereby 0 indicate normal flow without data forwarding and else otherwise. The third path is the hilo path, which is used to forward MEM stagedata to EX stage. It is meant to resolve the first scenario of the HILO register related data hazards discussed. The same convention of rs and rt path applies to hilo path. The 3-bit control signal is used and the MSB indicates the condition of the data. 001 and 010 of the ex_hilo_ctrl in Table 8 is used to transfer the HI or LO register’s data when issue mfhi or mflo instruction respectively. On the other hand, 000 indicates that instructions other than mfhi and mflo were issued. It is to avoid duplicated logic and reduce the multiplexer used to transfer the data from stage to stage. The last path is used to resolve load-store data hazard. This path forwards the data from the MEM stage. 1-bit control signal is used to indicate the condition of the data, whereby 1 represent the data is forwarding from other stage and else otherwise. Table 8 shows the information of the data paths used for data forwarding.
Online controlled experiments, also called A/B testing, have been established as the mantra for data-driven decision mak- ing in many web-facing companies. A/B Testing support decision making by directly comparing two variants at a time. It can be used for comparison between (1) two can- didate treatments and (2) a candidate treatment and an established control. In practice, one typically runs an ex- periment with multiple treatments together with a control to make decision for both purposes simultaneously. This is known to have two issues. First, having multiple treatments increases false positives due to multiple comparison. Second, the selection process causes an upward bias in estimated ef- fect size of the best observed treatment. To overcome these two issues, a two stage process is recommended, in which we select the best treatment from the first screening stage and then run the same experiment with only the selected best treatment and the control in the validation stage. Tra- ditional application of this two-stage design often focus only on results from the second stage. In this paper, we propose a general methodology for combining the first screening stagedata together with validation stagedata for more sensitive hypothesis testing and more accurate point estimation of the treatment effect. Our method is widely applicable to existing online controlled experimentation systems.
The qualitative research represented the second stage of the research design of this study. Involving in-depth interviews with shopping centre marketing managers and focus group discussions with shoppers, the qualitative research was conducted to explore the relevance of and the relationships between the six key factors identified from the literature review. These six factors were Cognition, Emotion, Actual Behaviour, Intended Behaviour, Social Crowding, and Shopping Orientation. The qualitative research, indeed, supported the importance of these six factors in explaining shoppers’ experiences with special event entertainment. However, it appeared to have reached theoretical saturation as it did not reveal any new or additional factor. Despite that, the qualitative research was insightful for two major reasons. First, it indicated that the nature of each of these six factors was complex and multidimensional, and thus further research would be needed to verify its dimensionality. Second and final, it suggested that the labels of two factors to be revised in order to reflect their proper meanings in the context of special event entertainment. These two factors were Cognition and Emotion and their labels were revised to Perceived Event Quality and Enjoyment respectively.
Information on Looked After Children is collected in the web-based SSDA903 return by local authorities in England. Information in the CLA database is collected at individual level and since 2005-06 includes the Unique Pupil Number (UPN) field. This data is collected annually between April and June for the previous financial year. Once the data has been collected and checked, an extract is produced which is sent to our matching contractors for linking to the NPD. The UPN is the main field used for matching purposes but other information about the child is also used such as date of birth, gender, ethnicity and responsible local authority. Local authorities are required to update the database every year, including making amendments to previous years’ records where there have been changes.
Part of Speech (POS) tagging and Named Ent- ity (NE) tagging have become important com- ponents of effective text analysis. In this paper, we propose a bootstrapped model that involves four levels of text processing for Ur- du. We show that increasing the training data for POS learning by applying bootstrapping techniques improves NE tagging results. Our model overcomes the limitation imposed by the availability of limited ground truth data required for training a learning model. Both our POS tagging and NE tagging models are based on the Conditional Random Field (CRF) learning approach. To further enhance the performance, grammar rules and lexicon lookups are applied on the final output to cor- rect any spurious tag assignments. We also propose a model for word boundary segmen- tation where a bigram HMM model is trained for character transitions among all positions in each word. The generated words are further processed using a probabilistic language mod- el. All models use a hybrid approach that combines statistical models with hand crafted grammar rules.
underlying oneness of time, earth, God and humanity which I experienced at each particular site. Thus, I deemed it helpful to bring into each compilation the same site observed at different times of the day and year in order to reflect the seasonal variations as well as my subjective sensorial responses to those fluctuations and changes. In this way the final composition could encompass the most diverse sensory responses at each site. Therefore, layering different sensate ‘snapshots’ of the one site could increase the possibility of visually expressing a holistic spiritual experience. The later steps in the research involved compiling the data from each site then representing the sensate experiences in a visual form. Each visual form intended to convey a sense of the
This study is a retrospective cohort analysis using data from the SEER database which was designed and main- tained by the National Cancer Institute (NCI). The SEER database collects clinical information on various cancer types for associated incidence, prevalence, and survival from 17 population-based cancer registries covering approximately 28% of the US population. 18 We used the SEER*STAT software (version 8.3.5) to extract data from the SEER database. The cohort for this analysis consisted of adult patients ( ≥ 18 years) diagnosed with PTC who underwent thyroid surgery between 2004 and 2013. The histological subtypes of PTC were limited using the site code C73.9 and the International Classi ﬁ cation of Diseases for Oncology-3: 8050, 8260, 8340 – 8344. The exclusion criteria were: (1) patients with second primary malignan- cies, (2) patients diagnosed at autopsy and those lost to follow-up, and (3) patients with incomplete clinical infor- mation (marital status, cause of death, survival month, tumor size, staging information, and follow-up months). All patients were randomly assigned to either the training set for nomograms or the validation set for the purposes of
Abstract: In the world of mining, calculation of reserves was the most decisive in exploration activities. The results of the calculation of coal reserves were needed to evaluate the economic value of a mining operation to be planned. Observation of fundamental data in the calculation of reserves was in accordance with the level and accuracy in retrieving the data. Therefore, so that mining activities can be done easily then the required supporting software that can simplify the calculation and modeling. The purpose of this research was to calculate the reserves of coal by using two methods include methods of block model and cross-section method. In this study, the acquisition of the coal obtained from the comparison between the methods of block model and cross-section method. Calculation of coal reserves by stripping ratiowas not more than 7: 1. The block method model the obtained results 13,646,218.25 MT of coal reserves and overburden volume of 91,472,579.44 BCM. While the section method the results obtained by 14,540,371.3 MT of coal reserves and overburden volume of 92,547,132 BCM. Comparison of the two methods produce different amount of coal reserves in the same mining pit design. because the accuracy of the calculation was not the same. Factors that lead to differences in methods of calculation of reserve block model of the method was calculated based on the volume of blocks with dimensions adapted to the conditions of the sediment in the area. While the cross-sectiol method was that the volume was calculated based on a model that can represent a cross-section model of sediment in the area
Text analytics refers to deriving high quality information from text, usually using methods that involve structuring input text. For example, an unstructured data source can be made available to an application for analysis purposes, and a set of arbitrary patterns based on keywords can be developed, resulting in an output. This process is called Text Analytics, and typically involves tasks such as text categorisation, text clustering and concept extraction. While these processes have been followed in many text analyses, the major difference between text analysis and text analytics is the opportunity to unearth insights and extract value out of information. Unearthing insights requires a comprehensive understanding of the context, stakeholders, their emotional aspects, the problem or issue on hand, and the ability to articulate these as part of the analysis leading to text analytics.
However, many classes except for few of them that are clearly unrelated tend to be overlapped especially when dealing with large numbers of data points and clusters. This is inherently due to the nature of the second-stage dimension reduction in which only the two axes are chosen so that the classes which contribute most to the second stage criteria can be well-discriminated. Such behavior can exaggerate the distances between particular clusters, and more elaboration towards new criteria that fits in visualization is required. In the MEDLINE and the REUTERS datasets, visualization results seem to have a tail-shape along specific directions. We often found this phenomenon to occur in many other data sets. It is still unclear as to what causes this and how it affects the visualization, e.g. char- acteristics of information loss in the second stage. Finally, in order to determine how much loss of information is introduced by each method, more rigorous analysis based on various quantitative mea- sures such as pairwise between-cluster distance and within-cluster radii should be conducted.
Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m- dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.
Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART) for feature selection on the second stage which can also produce a classification model. We used a dataset which comparing gene expression in breast tumor tissues and other tumor tissues. This dataset has 10,936 predictor variables and 1,545 observations. The results of this study were the proposed method able to produce a few numbers of selected genes but gave high accuracy. The model also acquired in line with the Oncogenomics Theory by the obtained of GATA3 to split the root node of the decision tree model. GATA3 has become an important marker for breast tumors.
Figure 11 shows the compression-time data recorded for specimens (250 mm diameter by 45 mm high) of moderately and strongly degraded sludge material that were consolidated from the slurry state (w 720 %) using the hydraulic consolidation cell. The top surfaces of the specimens were allowed to drain freely to atmosphere while the pore water pressure response was measured at the base of the specimens. The degree of consolidation is also shown in Fig. 11 for the strongly degraded specimen. Although the cell confining pressure of 300 kPa applied to the strongly degraded specimen was three times greater than that applied to the moderately degraded specimen, it is still evident from Fig. 11 that consolidation occurred more readily for the more strongly degraded material. An average degree of consolidation of about 50 % was achieved within 20 min for the strongly degraded specimen and the shape of the compression-time curve was more characteristic of the consolidation behaviour associated with mineral soils. Primary consolidation also accounted for a larger proportion of the overall specimen strain and the coefficient of secondary compression value of 0.08 was also significantly lower than the value of 0.41 measured for the moderately degraded specimen. Hence, more thorough biodegradation of the sludge at the wastewater plant would facilitate more efficient mechanical consolidation and lead to higher rates of consolidation occurring in the field.
With the questionnaire for the children in Key Stages 1 and 2, several issues needed addressing prior to distribution. Lowe (2007) discusses the language used in questionnaires for children, urging the researcher to be ‘clear and simple’, avoiding ‘complex questions with multiple ideas’ (p.40). The first questionnaire written was reviewed by the Headteacher, who felt it was not sufficiently child friendly and the second draft was piloted on one KS1 child and one KS2 child. The two children provided feedback; one particularly valuable piece being that the phrase ‘afternoon curriculum’ should be changed to ‘afternoon learning’ as the first phrase did not make sense to them. Two different questionnaires were then produced; one for KS1 and one for KS2 classes. The only difference in the questionnaires related to two questions which listed specific Key Stage themes. It was felt that the most appropriate data collection method would be to read out the questions to a whole class and then ask the children to record their answers on their
This stage starts when the nodes form the horizontal chains and choose chain head for each chain (each row will consider as a chain), the selection would be based on periodic way. At the same time all heads will be at the same column to decrease the energy consumed as much as possible. The nodes will receive the data from the closest neighbor and fuse the data with its own data then send the fused data to the next neighbor and so on so forth until all data reach the chain head. In our model we have 100 nodes divided into equal number of rows and columns (10 rows, 10 columns). This means that each node will be chain head 1 time only within 10 sensing rounds. The nodes will sense the area and send their data to the chain head using greedy algorithm. Normally after specific period of time, when some nodes consume big share of their energy, they will not be able to act as a chain head then it would be not possible to select a chain head sequentially, in that time we consider the maximum residual energy in each node to select the chain head and avoid data losing. We can see a summarized description for the operation of first stage in the flowchart in figure (1.a).