In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However, traditional data clustering algorithms, when applied to the handover prediction, exhibit three main limitations: the risk of data privacy breach, the fixed shape of clusters, and the non-adaptive number of clusters. To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division. We show that the generative adversarial network-based clustering preserves privacy. The cluster calibration deals with dynamic environments by modifying clusters. Moreover, the divisive clustering explores the different number of clusters by repeatedly selecting and dividing a cluster into multiple clusters. A baseline algorithm and our algorithm are tested on a time series forecasting task. We show that our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.

The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics. We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4)required scenario complexity and amount of test cases, and 5)handover of responsibility between the driver and the vehicle.

A novel partial-product reduction circuit for use in integer multiplication is presented. The high-performance multiplier (HPM) reduction tree has the ease of layout of a simple carry-save reduction array, but is in fact a high-speed low-power Dadda-style tree having a worst-case delay which depends on the logarithm (O(log TV)) of the word length N

Fast parallel multipliers that contain logarithmic partial-product reduction trees pose a challenge to simulation-based high-accuracy timing verification, since the reduction tree has many reconvergent signal branches. However, such a multiplier architecture also offers a clue as how to attack the test-vector generation problem. The timing-critical paths are intimately associated with long carry propagation. We introduce a multiplier test-vector generation method that has the ability to exercise such long carry propagation paths. Through extensive circuit simulation and static timing analysis, we evaluate the quality of the test vectors that result from the new method. Especially for fast multipliers with a pronounced carry propagation, the timing-critical vectors manage to stimulate a path, which has a delay that comes close to the true worst case delay. We investigate the complexity and run-time for the test-vector generation, and derive timing-critical vectors up to a factor word length of 54 bits.

We present a twin-precision multiplier that in normal operation mode efficiently performs N-b multiplications. For applications where the demand on precision is relaxed, the multiplier can perform N/2-b multiplications while expending only a fraction of the energy of a conventional N-b multiplier. For applications with high demands on throughput, the multiplier is capable of performing two independent N/2-b multiplications in parallel. A comparison between two signed 16-b multipliers, where both perform single 8-b multiplications, shows that the twin-precision multiplier has 72% lower power dissipation and 15% higher speed than the conventional one, while only requiring 8% more transistors.

Full-custom design is considered superior to standard-cell design when a high-performance circuit is requested. The structured routing of critical wires is considered to be the most important contributor to this performance gap. However, this is only true for bitsliced designs, such as ripple-carry adders, but not for designs with inter-bitslice interconnections spanning several bitslices, such as tree adders and reduction-tree multipliers. It is found that standard-cell design techniques scale better with the data width than full-custom bitsliced layouts for designs dominated by inter-bitslice interconnections.

The reuse of library components is a cornerstone of today's software development. Therefore, developers need good library documentation to take advantage of complex application programmer interfaces (APIs). It is possible to use ontologies as the basis for modeling libraries and library documentation. The knowledge-engineering tool Protégé can manage such library ontologies as well as knowledge bases containing the documentation. JDView is an extension to Protégé that allows developers to navigate and read the documentation contained in the knowledge base. The JDView approach illustrates how knowledge-engineering tools can enhance the representation and viewing of library documentation.

A new interconnect-driven DFT implementation is proposed in this paper. The normal way to implement the DFT is to use the FFT algorithm since it is computationally favorable. However, the increased speed comes at the cost of increased communications which give a higher power consumption. If the DFT algorithm is directly implemented instead, each channel becomes independent of all other channels and consequently communications and hence power consumption are reduced. Other benefits of using the DFT directly are the possibility to calculate a spectrum of any length, not only a power of two, and to have an irregular frequency step between channels. A number of ad hoc processing-element (PE) and system-level solutions are also proposed to reduce the power consumption even further.

This paper describes how we applied the PROTÉGÉ-II architecture to build a knowledge-based system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTÉGÉ-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTÉGÉ-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Beginning with the textual description of the elevator-configuration task, we analysed the domain knowledge with respect to PROTÉGÉ-II’s main goal: to build domain-specific knowledge-acquisition tools. We used PROTÉGÉ-II’s suite of tools to construct a knowledge-based system, called ELVIS, that includes a reusable domain ontology, a knowledge-acquisition tool, and a propose-and-revise problem-solving method that is optimized to solve the elevator-configuration task. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that PROTÉGÉ-II generated from the ontologies. After we constructed mapping relations to connect the knowledge base with the method’s code, the final executable problem solver solved the test case provided with the Sisyphus-2 material. We have found that the development of ELVIS has afforded a valuable test case for evaluating PROTÉGÉ-II’s suite of system-building tools. Only projects based on reasonably large problems, such as the Sisyphus-2 task, will allow us to improve the design of PROTÉGÉ-II and its ability to produce reusable components.

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. Protégé-ii is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, Protégé-ii generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a “game board” on which the problem solver moves “playing pieces” according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem). We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.

This paper is intended to improve Huffman's results on universal encoding of asynchronous sequential networks. The method employed here consists of assigning to each row of the flow table a single vertex on the corresponding hypercube. The remaining vertices are used for the transitions between stable states. Such a vertex can be used for different transitions if they correspond to distinct input states.