Software-defined radio (SDR) is a self-contained, embedded software system with hardware components and real-time constraints. SDR is the basis for many of today's wireless communications systems. Because SDR combines basic digital signal processing, circuitry, and software elements, it is the perfect project for multi-disciplinary, undergraduate teams. This paper explains both the SDR system created by computer science and engineering undergraduates at Grove City College and how SDR projects can help undergraduates acquire the skills to succeed on multidisciplinary teams.

Abstract Query‐by‐humming systems offer content‐based searching for melodies and require no special musical training or knowledge. Many such systems have been built, but there has not been much useful evaluation and comparison in the literature due to the lack of shared databases and queries. The MUSART project testbed allows various search algorithms to be compared using a shared framework that automatically runs experiments and summarizes results. Using this testbed, the authors compared algorithms based on string alignment, melodic contour matching, a hidden Markov model, n‐grams, and CubyHum. Retrieval performance is very sensitive to distance functions and the representation of pitch and rhythm, which raises questions about some previously published conclusions. Some algorithms are particularly sensitive to the quality of queries. Our queries, which are taken from human subjects in a realistic setting, are quite difficult, especially for n‐gram models. Finally, simulations on query‐by‐humming performance as a function of database size indicate that retrieval performance falls only slowly as the database size increases.

We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of `query-by-humming' (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of {m error} or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications.

As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller?s profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naive application of multiagent reasoning techniques would require the seller?s agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a 'seller?s market', where many buy offers are available.

Abstract We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. The sung theme (query) is converted into a sequence of pitch‐intervals and rhythms. This sequence is compared to musical themes (targets) stored in a database. The top pieces are returned to the user in order of similarity to the sung theme. We describe, in detail, two different approaches to measuring similarity between database themes and the sung query. In the first, queries are compared to database themes using standard string‐alignment algorithms. Here, similarity between target and query is determined by edit cost. In the second approach, pieces in the database are represented as hidden Markov models (HMMs). In this approach, the query is treated as an observation sequence and a target is judged similar to the query if its HMM has a high likelihood of generating the query. In this article we report our approach to the construction of a target database of themes, encoding, and transcription of user queries, and the results of preliminary experimentation with a set of sung queries. Our experiments show that while no approach is clearly superior to the other system, string matching has a slight advantage. Moreover, neither approach surpasses human performance.

When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.

There is a well-recognized need for accurate timing verification tools that account for the functional behavior of component interfaces, and thereby do not traverse false combinational and sequential paths. Such tools, however, are susceptible to an exponential increase in task complexity as the circuit size and functional complexity of components increase. The viability of accurate timing verifiers hinges on their ability to efficiently analyze the smallest subset of circuit behaviors, while verifying the timing characteristics of the overall space of behaviors. This paper presents theoretical results that address this issue for the timing verification of interacting FSMs.

A timing verifier that scales to verify complex sequential circuits, modeled in terms of interacting FSMs, while rejecting false sequential and combinational paths has, so far, not been developed. We present an algorithm for this purpose. The inherently modular nature of interactions among FSMs, allow a highly efficient symbolic simulation verification methodology. Experimental results illustrate this methodology's ability to scale, while providing accurate timing verification results.

Article The minimization and decomposition of interface state machines Share on Authors: Ajay J. Daga ECES Department, The University of Michigan, Ann Arbor, MI ECES Department, The University of Michigan, Ann Arbor, MIView Profile , William P. Birmingham ECES Department, The University of Michigan, Ann Arbor, MI ECES Department, The University of Michigan, Ann Arbor, MIView Profile Authors Info & Claims DAC '94: Proceedings of the 31st annual Design Automation ConferenceJune 1994 Pages 120–125https://doi.org/10.1145/196244.196301Online:06 June 1994Publication History 3citation116DownloadsMetricsTotal Citations3Total Downloads116Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Article Free Access Share on Towards optimal system-level design Authors: Manjote S. Haworth View Profile , William P. Birmingham View Profile Authors Info & Claims DAC '93: Proceedings of the 30th international Design Automation ConferenceJuly 1993 Pages 434–438https://doi.org/10.1145/157485.164965Published:01 July 1993Publication History 3citation134DownloadsMetricsTotal Citations3Total Downloads134Last 12 Months3Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A hierarchical select-and-interconnect methodology for system-level design is described. It extends the flexibility of previous approaches by allowing dynamic subproblem ordering, which is essential for this domain. The design model of M1, a knowledge-based system that implements this approach for small computer systems, is presented. M1's design space covers five microprocessor families, and it has generated hundreds of designs, three of which were built. A set of experiments showing that M1 can be used successfully to design in this complex domain and provides a substantial increase in designer productivity is described.>

The authors point out that part selection is the problem of choosing a set of parts that implements given functions and meets cost bounds. Multiple-function parts make globally optimal part selection an exponentially complex task. Task difficulty is compounded by optimizing among multiple cost attributes. An algorithm that generates optimal designs, and handles multiple cost attributes is presented.>

Asynchronous finite state machines (AFSMS) have been limited because multiple-input changes have been disallowed. In this paper, we present an architecture and synthesis system to overcome this limitation. The AFSM marks potentially hazardous state transitions, and prevents output during them. A synthesis tool to create the AFS M incorporates novel algorithms to detect the hazardous states.

MICON is a knowledge-based system that designs small computer systems. The synthesis of a digital system may be viewed as successive design and redesign of individual subsystems. Redesign is necessary when the design of one subsystem imposes conflicting requirements on the design of another, and when a design cannot meet specifications. This paper discusses the technique used to make MICON capable of redesign. The paper discusses a dependency network used to facilitate failure correction and highlights the novel features of this scheme. An example is given.

The MICON system is an integrated collection of programs which automatically synthesizes small computer systems from high level specifications. The system addresses multiple levels of design, from logical through physical, providing a rapid prototyping capability. Two programs form MICON's nucleus: a knowledge-based synthesis tool called M1; and, an automated knowledge acquisition tool named CGEN which is used to teach M1 how to design. Other tools in the MICON system are an integrated database and associated data management tools. The system is fully functional, having been used to generate designs that have been fabricated. This paper describes the architecture and operation of the MICON system.

Knowledge-based systems are becoming pervasive in the computer-aided design area. For these systems to achieve satisfactory levels of performance large amounts of knowledge are necessary; however, the acquisition of knowledge is a difficult task. Automated knowledge-acquisition tools (AKAT) provide capabilities for quickly building and maintaining knowledge-bases. This paper describes CGEN, a tool which allows hardware designers, unfamiliar with artificial intelligence, to deposit their expertise into a knowledge-base. A set of experiments which tested CGEN's capabilities are presented. The experiments demonstrate hardware designers can produce high quality knowledge-bases using CGEN.

Most of the CAE/CAD tasks are characterized as having large search spaces, because of this they have been resistive to traditional algorithmic approaches. In recent years the knowledge-based expert systems approach of Artificial Intelligence has promised a new solution. This tutorial provides: a background as to what are knowledge-based expert systems; the type of tasks where knowledge-based expert systems can be applied; and the process of building knowledge-based expert systems.