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The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multioperand SD addition algorithm to reduce the partial product array. Finally, a digit-set conversion algorithm with a hybrid prefix network to decrease the number of the logic gates on the critical path is discussed. An analysis of the timing delay and an HDL model synthesized under 90 nm technology show that by considering the tradeoff of designs among three components, the overall delay of the proposed $(16\times 16)$-digit multiplier takes about 11 percent less timing delay with 2 percent less area compared to the current fastest design. [ABSTRACT FROM AUTHOR]

Jogging is a common and cheap form of exercise which improves health and can reduce the risk of illness. Many people wear mobile devices to listen to music during this activity. However, the playlist in mobile devices are predefined and independent of the jogger 'space. Whenever the user wants to change the music that is playing, they have to stop exercising and push buttons on the device. In this paper, we introduce a novel service that provides the jogger with a smart music player agent to reduce the effort required to control the mobile device. This system involves music filtration and pace prediction technologies. An artificial neural networks that can filter out undesired music is used as the kernel of the music filter. Pace prediction is based on hidden Markov models (HMM), and similar pattern searching techniques are used to select music that has a suitable tempo. A series of experiments were carried out to demonstrate the performance of this system. The results show that this unique service is attractive to joggers. [ABSTRACT FROM AUTHOR]

Proposes a Hopfield-model net based on fuzzy possibilistic reasoning for the classification of multispectral images. Modification of the Hopfield network embedded with fuzzy probabilistic C-means method; Construction of a classification system; Implementation of fuzzy logic systems in neural network architecture.

This paper presents the algorithm and architecture of the decimal floating-point (DFP) logarithmic converter, based on the digit-recurrence algorithm with selection by rounding. The proposed approach can compute faithful DFP logarithm results for any one of the three DFP formats specified in the IEEE 754-2008 standard. In order to optimize the latency for the proposed design, we mainly integrate the following novel features: 1) using the redundant carry-save representation of the data path; 2) reducing the number of iterations by determining the number of initial iteration; and 3) retiming and balancing the delay of the proposed architecture. The proposed architecture is synthesized with STM 90-nm standard cell library and the results show that the critical path delay and the number of clock cycles of the proposed Decimal64 logarithmic converter are 1.55 ns (34.4 FO4) and 19, respectively, and the total hardware complexity is 43,572 NAND2 gates. The delay estimation results of the proposed architecture show that its latency is close to that of the binary radix-16 logarithmic converter, and that it has a significant decrease on latency compared with a recently published high performance CORDIC implementation. [ABSTRACT FROM PUBLISHER]

While mesh networking is gaining momentum with widespread application, we are concerned with fast handoff in a secure mesh environment. To this end, this paper presents a means in the context of IEEE 802.11s of letting a mesh portal act as an IEEE 802.1X authenticator to reduce costly IEEE 802.1X authentication processes during handoff. Our approach is developed for alignment with IEEE 802.11s and 802.11i, keeping protocols at the station side operable with no changes. As another strength, our design applies to generic multihop wireless networks. Both analytical and simulation modeling are conducted to evaluate our scheme as well. Performance results show that our approach reduces handoff delay by up to 268% or achieves comparable performance resulting from the counterpart IEEE 802.11i scheme with high likelihood of 70%–85% successful preauthentication. Moreover, our performance analysis suggests an optimal number of access points managed by one mesh portal in a network. Qualitative and quantitative discussions indicate that our approach is applicable in pragmatic settings. [ABSTRACT FROM PUBLISHER]

This work proposes and implements a novel and robust adaptive speech enhancement system, which contains both time domain and frequency domain beamformers using H∞ filtering approach to provide a clean and undisturbed speech waveform and improve the speech recognition rate in vehicle environments. A microphone array data acquisition hardware is also designed and implemented for the proposed speech enhancement system. Mutually matched microphones are needed for traditional multidimensional noise reduction methods, but this requirement is not practical for consumer applications from the cost standpoint. To overcome this issue, the proposed system adapts the mismatch dynamics to maintain the theoretical performance allowing unmatched microphones to be used in an array. Furthermore, to achieve a high speech recognition performance, the speech recognizer is usually required to be retrained for different vehicle environments due to different noise characteristics and channel effects. The proposed system using the H∞ filtering approach, which makes no assumptions about noise and disturbance, is robust to the modeling error in a channel recovery process. Consequently, the real vehicular experimental results show that the proposed frequency domain beamformer provides a satisfactory speech recognition performance without the need to retrain the speech recognizer. [ABSTRACT FROM AUTHOR]

Human-computer interaction (HCI) using speech communication is becoming increasingly important, especially in driving where safety is the primary concern. Knowing the speaker's location (i.e., speaker localization) not only improves the enhancement results of a corrupted signal, but also provides assistance to speaker identification. Since conventional speech localization algorithms suffer from the uncertainties of environmental complexity and noise, as well as from the microphone mismatch problem, they are frequently not robust in practice. Without a high reliability, the acceptance of speech-based HCI would never be realized. This work presents a novel speaker's location detection method and demonstrates high accuracy within a vehicle cabinet using a single linear microphone array. The proposed approach utilize Gaussian mixture models (GMM) to model the distributions of the phase differences among the microphones caused by the complex characteristic of room acoustic and microphone mismatch. The model can be applied both in near-field and far-field situations in a noisy environment. The individual Gaussian component of a GMM represents some general location-dependent but content and speaker-independent phase difference distributions. Moreover, the scheme performs well not only in nonline-of-sight cases, but also when the speakers are aligned toward the microphone array but at difference distances from it. This strong performance can be achieved by exploiting the fact that the phase difference distributions at different locations are distinguishable in the environment of a car. The experimental results also show that the proposed method outperforms the conventional multiple signal classification method (MUSIC) technique at various SNRs. [ABSTRACT FROM AUTHOR]