Chelmsford, MA, United States
Chelmsford, MA, United States

Apollo Computer Inc., founded 1980 in Chelmsford, Massachusetts by William Poduska and others, developed and produced Apollo/Domain workstations in the 1980s. Along with Symbolics and Sun Microsystems, Apollo was one of the first vendors of graphical workstations in the 1980s. Apollo produced much of its own hardware and software.Apollo was acquired by Hewlett-Packard in 1989 for US$476 million, and gradually closed down over the period 1990-1997. Wikipedia.

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Bhanumathi R.,Apollo Computer | Suresh G.R.,Easwari Engineering College
2nd International Conference on Electronics and Communication Systems, ICECS 2015 | Year: 2015

Breast cancer has been most frequent form of common cancer in women. It is also the leading cause of mortality in women each year. Breast cancer is much less common in younger women and is most often diagnosed when women are over 60. Breast cancer is the second-most common and leading cause of cancer death among women. It has turn into a major health issue in the world over the past 50 years, and its occurrence has increased in recent years. One of the leading methods for diagnosing breast cancer is screening mammography. The appearance of micro-calcification in mammograms is an early sign of breast cancer. To overcome the issue automated micro-calcification detection techniques play a vital role in cancer diagnosis and treatment. This paper aims to develop an automatic system to classify the digital mammogram images into Benign or Malignant images. We have proposed Support vector machine (SVM) based classifier for to detect the microcalcification at each location in the mammogram images. The proposed method has been implemented in three stages (a) preprocessing (b) feature extraction (c) SVM classification. The proposed method has been evaluated using Mammogram Image Analysis Society (MIAS) database. Experimental results show that, when compared to several other methods SVM shows 94.94% micro calcification detection in mammograms. © 2015 IEEE.

Solley D.J.,Apollo Computer
Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC | Year: 2015

D.C. multimeter, frequency sweeper and network analyzer. The circuit editor, which allows schematic entry, the above instruments and the analysis to be performed whether it be time domain, frequency domain or dc are all set up using a mouse and menu selections. The work bench offers a large model library, a parametric plotter and two statistical packages to boost engineering productivity. Some of its capabilities will be reviewed in the context of this paper. For the Spice connoisseur, the work bench will soon support Spice Plus, a C based version of Berkley 2G.6, which promises a 1.4X to 2.OX speed enhancement. To the first time user I would suggest he become familiar with Tnum, RELTOL, ABSTOL and VNTOL in the Analysis Options menu. Tnum defines the number of steps during the simulation. The default values for ABSTOL (absolute surrent error tolerance) and VNTOL (absolute voltage error tolerance) are 1.0E-12 and 1.0E-6 respectively and the author is not aware of too many power supply applications that require that kind of convergence accuracy. © 1986 IEEE.

Sara G.S.,Anna University | Devi S.P.,Apollo Computer | Sridharan D.,Anna University
ETRI Journal | Year: 2012

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy-efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near-optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near-optimal energy-efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy-efficient routing technique produces a longer network lifetime and achieves better energy efficiency. © 2012 ETRI.

Vinu Kiran S.,Apollo Computer | Prasanna Devi S.,Drmgr Educational And Research Institute University | Manivannan S.,Drmgr Educational And Research Institute University
Procedia Computer Science | Year: 2016

In this paper, we propose to transform the global matching mechanism in an electronic exchange between the producers and consumers in the SCM system for perishable commodities over large scale data sets. Matching of of consumers and producers satisfactions are mathematically modeled based on preferential evaluations based on the bidding request and the requirements data which is supplied as a matrix to Gale Shapely matching algorithm. The matching works over a very transparent approach in a e-trading environment over large scale data. Since, Bigdata is involved; the global SCM could be much clearer and easier for allocation of perishable commodities. These matching outcomes are compared with the matching and profit ranges obtained using simple English auction method which results Pareto-optimal matches. We are observing the proposed method produces stable matching, which is preference-strategy proof with incentive compatibility for both consumers and producers. Our design involves the preference revelation or elicitation problem and the preference-aggregation problem. The preference revelation problem involves eliciting truthful information from the agents about their types that are used for computation of Incentive compatible results. We are using Bayesian incentive compatible mechanism design in our match-making settings where the agents' preference types are multidimensional. This preserves profitability up to an additive loss that can be made arbitrarily small in polynomial time in the number of agents and the size of the agents' type spaces. © 2016 The Authors.

Lakshmi Narayanan S.,Manonmaniam Sundaranar University | Vinukiran S.,Apollo Computer
Procedia Computer Science | Year: 2016

In this paper, we propose to transform the traditional cornea transplantation methods into an electronic exchange between the cornea donors and recipients in the cornea transplantation elective surgery. Preferential evaluations of recipient and donors (individuals / eye-bank) satisfactions are mathematically modeled, then the preference matrix is used as input for Gale Shapely matching algorithm. The results of m∗n match happens to be a very transparent approach in a bilateral e-cornea transplantation environment. These matched results are compared with the results obtained using Generalized Assignment problem which produces NP-hard approximated matches. It is found that the proposed method produces stable matching, which is preference based and strategy proof and it also reduces the need for number of iterations for matching. © 2016 The Authors.

Ronald Reagan C.P.,Apollo Computer | Prasanna Devi S.,Apollo Computer
2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014 | Year: 2014

This paper presents an intelligent expert based system ANFISGA for the dosage planning for Type2 diabetes male patients. Even the Type2 diabetes accounts 90% of total diabetes patients the accurate dosage planning for the disease is complicated due to the variations of human body. In this paper two Artificial Intelligence techniques ANFIS and GA were combined. This human genetic algorithm accept the dosage predicted from ANFIS and do the optimization using genetic operators to produce the most accurate dosage prediction. © 2014 IEEE.

Devi S.P.,Apollo Computer | Manivannan S.,Dr. M.G.R. Educational and Research Institute | Rao K.S.,Anna University
International Journal of Advanced Manufacturing Technology | Year: 2012

Abstract The primary aim of the paper is to compare the different nongradient methods of multiobjective optimization for optimizing the geometry parameters of a cylindrical fin heat sink. The methods studied for comparison are Taguchi-based grey relational analysis, ε (epsilon) constraint method and genetic algorithm. The various responses that have been studied are electromagnetic emitted radiations, thermal resistance and mass of the heat sink. Since the responses are obtained using complex simulation softwares (HFSS-Ansoft for emitted radiations and CFD-Flotherm for thermal resistance), there is no way of calculating the derivates of the objective functions. Hence, the Taguchi design of experiments design is used to derive the linear regression equations for the responses studied, which are then taken as the objective functions to be optimized. A new hybrid method known as Taguchi-based epsilon constraint method has been proposed in this paper for obtaining nondominated Pareto solution set. The results obtained using the proposed method show that the Pareto optimal set is competitive in terms of diversity of the solutions obtained. It is not likely that there exists a solution, which simultaneously minimizes all the objectives using any of the multiobjective techniques implemented. The value path analysis has been done to compare the trade-off among the design alternatives for the chosen multiple objective parameter optimization problem. ©Springer-Verlag London Limited 2012.

News Article | July 26, 2013

Matrix Partners began in Boston in 1977 as Hellman Ferri Investment Associates, one of the first firms on the East coast (US) to have a startup technology focus. The firm was a founding investor in Apollo Computer, Stratus Computer and Continental Cable (now Comcast), and an early investor in Apple Computer.  In 1982, Paul Ferri re-established the firm as Matrix Partners and opened a Silicon Valley office. And in 2006 Matrix Partners India was established with Matrix India advisory offices in Mumbai. The fund invests across significant growth sectors in India, including internet, mobile, education, financial services, healthcare, consumer and emerging areas. Here is the dissection and view the full article on YS Research.

News Article | March 3, 2009

Sometimes (just sometimes) it pays to look behind the jargon in press releases. One glance at yesterday’s coming-out-of-stealth-mode announcement from Boxborough, MA-based Digital Reef, which starts off talking about “massively scalable unstructured data management platforms” and “capabilities [that] improve eDiscovery outcomes,” was enough to make even a nerd like me want to tune out. But it turns out that Digital Reef has built something fairly new and interesting, a “similarity search engine” for big corporate networks that can start with one document—say, a Word or Excel file—and find others that resemble it. That could be very useful if, for instance, you were a compliance officer at a big health plan and you wanted to see whether any of your employees had unsecured patient records sitting around on their laptop hard drives (which would be a big violation of federal healthcare privacy regulations). Just plop an example of a patient record into the Digital Reef system, and it will scour the network for other examples. Or say you were a lawyer at a big firm writing a brief in an employment case and you wanted to find out whether any of your colleagues working on similar cases in the past had already assembled the relevant citations. You could simply submit your entire draft to Digital Reef, and see what washed up. The problem with most big organizations, says Brian Giuffrida, Digital Reef’s vice president of marketing and business development, is that they don’t know what they don’t know. A traditional keyword-based search might be effective if you already have a good mental picture of the kinds of information stored on your company network, and if you know what search terms are most likely to ferret out what you need. But if you don’t even know whether the information you’re searching for exists, where do you start? “A lot of times, larger firms have dedicated knowledge experts to retrieve data…because searching is a cumbersome task that requires knowledge of the corpus and of the structure of the data,” says Giuffrida. “But once you start doing searches by similarity, you no longer need to be an expert in keyword searching or Boolean algebra. You can just say ‘Here’s the presentation we did at the conclusion of this engagement with Company ABC, I’m looking for similar stuff.’ It’s as simple as that.” Digital Reef’s system, which has been nearly three years in the making, is designed specifically to index and organize the miscellaneous types of documents—Word, PowerPoint, Excel, PDF, e-mail—that clutter the average knowledge worker’s local file system. (Such data is “unstructured,” in computer science lingo, at least when you compare it to the nice rows and columns of information in transactional databases.) Not only are these the kinds of files where most of a company’s collective experience is stored, but they’re piling up faster than any other kind of corporate data, according to Digital Reef’s founder, president, and CEO, Steve Akers. Enterprise content management systems like EMC’s Documentum offer one way to get a handle on all this information. But in a blog post yesterday, Akers argues that these systems force users to tag and classify documents in a way that’s contrived, error-prone, and poorly adapted to people’s actual work flows. The only way for a company to really find out what kinds of data it has laying around—and thereby what kinds of security, compliance, and litigation risks it might be opening itself up to—is to let that data “speak for itself,” in Akers’ words, through similarity-based search. Akers is a longtime Boston-area technology entrepreneur who’s done stints at Apollo Computer, Hewlett-Packard, Stratus Computer, and Shiva Corporation. Spring Tide Networks, the Boxborough-based Internet data switch maker Akers co-founded in 1998, was purchased by Lucent in 2000 for a cool $1.5 billion. According to Guiffrida, Akers took several years off after leaving Lucent deal to develop the mathematical modeling techniques behind similarity-based search. He formed Digital Reef in late 2006 with a $10 million investment from Waltham, MA-based Matrix Partners. A second $10 million round, led this time by Boston’s Pilot House Ventures, closed late last year. Until emerging from stealth mode this week, the company did business under the name Auraria Networks. It has 32 employees, including a handful in Atlanta, GA.

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