NuMDG: A New Tool for Multiway Decision Graphs Construction

Multiway Decision Graphs (MDGs) are a canonical representation of a subset of many-sorted first-order logic. This subset generalizes the logic of equality with abstract types and uninterpreted function symbols. The distinction between abstract and concrete sorts mirrors the hardware distinction between data path and control. Here we consider ways to improve MDGs construction. Efficiency is achieved through the use of the Generalized-If-Then-Else (GITE) commonly operator in Binary Decision Diagram packages. Consequently, we review the main algorithms used for MDGs verification techniques. In particular, Relational Product and Pruning by Subsumption are algorithms defined uniformly through this single GITE operator which will lead to a more efficient implementation. Moreover, we provide their correctness proof. This work can be viewed as a way to accommodate the ROBBD algorithms to the realm of abstract sorts and uninterpreted functions. The new tool, called NuMDG, accepts an extended SMV language, supporting abstract data sorts. Finally, we present experimental results demonstrating the efficiency of the NuMDG tool and evaluating its performance using a set of benchmarks from the SMV package

Continuous formal Arabic speech recognition system based on hidden Markov model

Speech recognition is a mechanism to recognize words and phrases in any language and translate them to a machine-readable layout. Recently, Automatic Speech Recognition (ASR) systems are used widely in many applications like translating speech to text, home security systems, and military applications. Unfortunately, the speech recognition field for the Arabic language is not yet mature and it needs more development. The main objective of this paper is to implement and evaluate a continuous speech recognition system for the formal Arabic language using MelFrequency Cepstral Coefficients (MFCCs) and Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM). Adding the energy feature to the MFCCs twelve features for each frame has a good impact on the accuracy for the continuous speech. In addition, using GMM was effective as a post processing step before calculating the HMM parameters for training. A recognition rate is enhanced to be 94% as a result of tuning parameters and using different techniques. The Arabic speech recognition system is built using MATLAB based on HMM toolbox. A comparison between our work and the state of the art works is presented to show the acceptability and intelligence (accuracy) of our work

An Enhanced VoD Streaming Model for P2P Cloud Computing Systems

Availability of ample digital multimedia content and advances in network bandwidth has fueled the growth of Video on Demand (VoD). Client-server based centralized system have limitations in the number of users who can get connected at a time. The growth of Peer-to-Peer (P2P) based VoD streaming systems, together with cloud computing offers better availability of the video content, improved scalability and load balancing among peers. Providing the end user a smooth VoD playback has become an essential quality assurance component of VoD based system. However, higher quality of service and better user experience during video playback is still an open challenge. In this paper, we propose an Enhanced VoD Streaming Model (EVSM) for P2P cloud based system. The proposed model has four layers where each layer considers the various quality assurance factors of the P2P VoD and ensures better video streaming. Our evaluation results show that the proposed model decreases the video seek time and lowers the startup delay when compared to other methods

FPGA Modeling and Optimization of a SIMON Lightweight Block Cipher

Security of sensitive data exchanged between devices is essential. Low-resource devices (LRDs), designed for constrained environments, are increasingly becoming ubiquitous. Lightweight block ciphers provide confidentiality for LRDs by balancing the required security with minimal resource overhead. SIMON is a lightweight block cipher targeted for hardware implementations. The objective of this research is to implement, optimize, and model SIMON cipher design for LRDs, with an emphasis on energy and power, which are critical metrics for LRDs. Various implementations use field-programmable gate array (FPGA) technology. Two types of design implementations are examined: scalar and pipelined. Results show that scalar implementations require 39% less resources and 45% less power consumption. The pipelined implementations demonstrate 12 times the throughput and consume 31% less energy. Moreover, the most energy-efficient and optimum design is a two-round pipelined implementation, which consumes 31% of the best scalar’s implementation energy. The scalar design that consumes the least energy is a four-round implementation. The scalar design that uses the least area and power is the one-round implementation. Balancing energy and area, the two-round pipelined implementation is optimal for a continuous stream of data. One-round and two-round scalar implementations are recommended for intermittent data applications

POSIT vs. Floating Point in Implementing IIR Notch Filter by Enhancing Radix-4 Modified Booth Multiplier

The increased demand for better accuracy and precision and wider data size has strained current the floating point system and motivated the development of the POSIT system. The POSIT system supports flexible formats and tapered precision and provides equivalent accuracy with fewer bits. This paper examines the POSIT and floating point systems, comparing the performance of 32-bit POSIT and 32-bit floating point systems using IIR notch filter implementation. Given that the bulk of the calculations in the filter are multiplication operations, an Enhanced Radix-4 Modified Booth Multiplier (ERMBM) is implemented to increase the calculation speed and efficiency. ERMBM enhances area, speed, power, and energy compared to the POSIT regular multiplier by 26.80%, 51.97%, 0.54%, and 52.22%, respectively, without affecting the accuracy. Moreover, the Taylor series technique is adopted to implement the division operation along with cosine arithmetic unit for POSIT numbers. After comparing POSIT with floating point, the accuracy of POSIT is 92.31%, which is better than floating point’s accuracy of 23.08%. Moreover, POSIT reduces area by 21.77% while increasing the delay. However, when the ERMBM is utilized instead of the POSIT regular multiplier in implementing the filter, POSIT outperforms floating point in all the performance metrics including area, speed, power, and energy by 35.68%, 20.66%, 31.49%, and 45.64%, respectively

POSIT vs. Floating Point in Implementing IIR Notch Filter by Enhancing Radix-4 Modified Booth Multiplier

The increased demand for better accuracy and precision and wider data size has strained current the floating point system and motivated the development of the POSIT system. The POSIT system supports flexible formats and tapered precision and provides equivalent accuracy with fewer bits. This paper examines the POSIT and floating point systems, comparing the performance of 32-bit POSIT and 32-bit floating point systems using IIR notch filter implementation. Given that the bulk of the calculations in the filter are multiplication operations, an Enhanced Radix-4 Modified Booth Multiplier (ERMBM) is implemented to increase the calculation speed and efficiency. ERMBM enhances area, speed, power, and energy compared to the POSIT regular multiplier by 26.80%, 51.97%, 0.54%, and 52.22%, respectively, without affecting the accuracy. Moreover, the Taylor series technique is adopted to implement the division operation along with cosine arithmetic unit for POSIT numbers. After comparing POSIT with floating point, the accuracy of POSIT is 92.31%, which is better than floating point’s accuracy of 23.08%. Moreover, POSIT reduces area by 21.77% while increasing the delay. However, when the ERMBM is utilized instead of the POSIT regular multiplier in implementing the filter, POSIT outperforms floating point in all the performance metrics including area, speed, power, and energy by 35.68%, 20.66%, 31.49%, and 45.64%, respectively