The effectiveness of error-control coding in a frequency-hop radio system can be increased greatly by the use of side information that is developed in the radio receiver. The transmission of test symbols provides a simple method for the derivation of side information in a slow-frequency-hop receiver. Requirements on the reliability of the side information are presented, and their implications in determining the necessary number of test symbols are described. Other methods for developing side information are reviewed briefly, and applications of side information to routing protocols for frequency-hop packet radio networks are discussed.

Concatenated coding techniques are applied to slow frequency-hop packet radio communications for channels with partial-band interference. Binary orthogonal signaling (e.g., binary FSK) is employed with noncoherent demodulation. The outer codes are Reed-Solomon codes and the inner codes are convolutional codes. Two concatenated coding schemes are compared. The first employs an interleaver between the outer Reed-Solomon code and the inner convolutional code. The second scheme employs an additional interleaver following the convolutional code. Comparisons are made between the performance of these concatenated coding schemes and the performance of Reed-Solomon codes alone.

A narrowband interference in direct sequence spread spectrum communication systems also affects the characteristics of a delay lock loop. In this paper, the delay errors of a baseband delay lock loop (DLL) in the presence of the interference which consists of a narrowband Gaussian noise and several tones are examined, and when a filter is used to reject the interference, the characteristics of the DLL are analyzed using the Fourier method. Furthermore, from the calculation results of the delay error in case where a prediction error filter with two-sided taps is used as the rejection filter, it is shown that the filter is necessary to keep the DLL in the lock-on state.

In this paper we consider multiuser detection using a neural network in a synchronous code-division multiple-access channel. In a code-division multiple-access channel, a matched filter is widely used as a receiver. However, when the relative powers of the interfering signals are large, i.e. the near-far problem, the performances of the matched filter receiver degrade. Although the optimum receiver for multiuser detection is superior to the matched filter receiver in such situations, the optimum receiver is too complex to be implemented. A simple technique to implement the optimum multiuser detection is required. Recurrent neural networks which consist of a number of simple processing units can rapidly provide a collectively-computed solution. Moreover, the network can seek out a minimum in the energy function. On the other hand, the optimum multiuser detection in a synchronous channel is carried out by the maximization of a likelihood function. In this paper, it is shown that the energy function of the neural network is identical to the likelihood function of the optimum multiuser detection and the neural network can be used to implement the optimum multiuser detection. Performance comparisons among the optimum receiver, the matched filter one and the neural network one are carried out by computer simulations. It is shown that the neural network receiver has a capability to achieve near-optimum performance in several situations and local minimum problems are few serious.

A new type of synchronous code division multiple access (S/CDMA) scheme for optical subscriber systems is reported. Passive channel multiplexing is promising for optical subscriber systems because it realizes high system performance at low cost. Unfortunately, passive channel multiplexing suffers from phase differences among the upstream channels, and these differences prevent the usage of traditional synchronous CDMA techniques that reduce cross channel interference. This paper proposes the new technique of block-interleaving & redundancy code sequences to overcome this problem. This combination realizes S/CDMA even in the presence of phase differences and eliminates cross channel interference completely. Therefore, in an optical subscriber system using the new type S/CDMA, the bit error rate performance is independent of phase difference levels and the number of multiplexed channels.

We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.

Properties of a strongly-coupled nonlinear directional coupler (NLDC) with a lossy MQW coupling layer is analyzed using the Galerkin finite element method accompanied by a predictor-corrector algorithm. It is shown that the propagation attenuation along the NLDC is considerably smaller than that in the bulk MQW and tends to reduce with the input power. By the presence of losses, the powers guided in two waveguides do not become a maximum and a minimum at the same propagation length, unlike the lossless coupler. The losses make the nonlinear effect weak due to the decrease in guided power, and hence the coupling length decreases and the switching power increases. The extinction ratio of the switching becomes the largest value not in the cases of nonloss and high losses but in the case of moderately high losses, although the switching power is somewhat larger than that of the lossless case.

Assuming application to the mobile multiple-access communication, chip-asynchronous mobile-to-base performances of FH/FTH (Frequency-Time-Hopped)-MFTSK (Multi-level Frequency-Time Shift Keying) systems are investigated. Analytical expressions are obtained for the probabilities of false detection and missed detection of signal elements, assuming independent and asynchronous arrival of each of the signal elements with Rayleigh fading and optional AWG noise. Using the result or by simulation and employing dual-k coding, parameter optimization was carried out to obtain the maximum spectrum efficiency. The results of the noisy case analysis and simulation show high noise-robustness of the FTH systems. For a given value of information transmission rate the optimized FTH-MFTSK gives an effectively constant spectrum efficiency for a wide range of the number Kf of frequency chips. As a result, FTH-MFTSK well outperforms FTH-MFSK at any, especially small value of Kf. Relative to the overall optimum FH-MFSK, FTH-MFSK systems show typically around 20% of degradation in spectrum efficiency even with one-eighth of Kf. Compared with FH-MFSK, accordingly, FTH-MFTSK systems allow the designer to reduce, without any degradation in multiple-access performances, the number of frequency chips to the minimum value tolerated by the frequency selective fading characteristics and the time chip duration requirement imposed by the signal-to-noise ratio margin and the transmitter peak power rating.

In this paper we present a new tracking scheme using two tracking modes which are based on the concept of Delay Lock Loop (DLL). Under the multipath fading channels, a conventional DLL has problems of jitter performance degradation, lock-off and delay offset. It is necessary to solve these problems, because mobile communications have increased drastically. We propose the combination of a coarse tracking mode and a fine tracking mode. The former mode is employed for reducing the possibility of losing lock, the latter mode is used for suppressing the jitter of delay error and the delay offset in the presence of multipath fading. The both modes utilize the power of delay paths shown in the auto-correlation function of the received signal at the DLL. Computer simulation results show that our proposed scheme is extremely useful comparing with a conventional scheme over the multipath fading channels.

This paper describes an adaptive sensing system with tracking and zooming a moving object in the stable environment. Both the close contour matching technique and the effective determination of zoom ratio by fuzzy control are proposed for achieving the sensing system. First, the estimation of object feature parameters, 2-dimensional velocity and size, is based on close contour matching. The correspondence problem is solved with cross-correlation in projections extracted from object contours in the specialized difference images. In the stable environment, these contours matching, capable of eliminating occluded contours or random noises as well as background, works well without heavy-cost optical flow calculation. Next, in order to zoom the tracked object in accordance with the state of its shape or movement practically, fuzzy control is approached first. Three sets of input membership function--the confidence of object shape, the variance of object velocity, and the object size--are evaluated with the simplified implementation. The optimal focal length is achieved of not only desired size but safe tracking in combination with fuzzy rule matrix constituted of membership functions. Experimental results show that the proposed system is robust and valid for numerous kind of moving object in real scene with system period 1.85 sec.

An attempt to apply neural networks to the acoustic diagnosis for the reciprocating compressor is described. The proposed neural network, Hybrid Neural Network (HNN), is composed of two multi-layered neural networks, an Acoustic Feature Extraction Network (AFEN) and a Fault Discrimination Network (FDN). The AFEN has multi-layers and the number of units in the middle hidden layer is smaller than the others. The input patterns of the AFEN are the logarithmic power spectra. In the AFEN, the error back propagation method is applied as the learning algorithm and the target patterns for the output layer are the same as the input patterns. After the learning, the hidden layer acquires the compressed input information. The architecture of the AFEN appropriate for the acoustic diagnosis is examined. This includes the determination of the form of the activation function in the output layer, the number of hidden layers and the numbers of units in the hidden layers. The FDN is composed of three layers and the learning algorithm is the same as the AFEN. The appropriate number of units in the hidden layer of the FDN is examined. The input patterns of the FDN are fed from the output of the hidden layer in the learned AFEN. The task of the HNN is to discriminate the types of faults in the compressor's two elements, the valve plate and the valve spring. The performance of the FDN are compared between the different inputs; the output of the hidden layer in the AFEN, the conventional cepstral coefficients and the filterbank's outputs. Furthermore, the FDN itself is compared to the conventional pattern recognition technique based on the feature vector distance, the Euclid distance measure, where the input is taken from the AFEN. The obtained results show that the discrimination accuracy with the HNN is better than that with the other combination of the discrimination method and its input. The output criteria of network for practical use is also discussed. The discrimination accuracy with this criteria is 85.4% and there is no case which mistakes the fault condition for the normal condition. These results suggest that the proposed decision network is effective for the acoustic diagnosis.

A network-topology-independent static task allocation strategy has been designed and implemented for massively parallel computers. For mapping a task graph to a processor graph, this strategy evaluates several functions that represent some intuitively feasible properties or the graphs. They include the connectivity with the allocated nodes, distance from the median of a graph, connectivity with candidate nodes, and the number of candidate nodes within a distance. Several greedy strategies are defined to guide the mapping process, utilizing the indicated function values. An allocation system has been designed and implemented based on the allocation strategy. In experiments we have defined about 1000 nodes in task graphs with regular and irregular topologies, and the same order of processors with mesh, tree, and hypercube topologies. The results are summarized as follows. 1) The system can yield 4.0 times better total communication costs than an arbitrary allocation. 2) It is difficult to select a single strategy capable of providing the best solutions for a wide range of task-processor combinations. 3) Comparison with hypercube-topology-dependent research indicates that our topology-independent allocator produces better results than the dependent ones. 4) The order of computaion time of the allocator is experimentally proved to be O (n2) where n represents the number of tasks.

This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.

We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.

This letter presents a new algorithm for echo cancellers, which prevents the reduction of echo return loss due to a double-talk. The essence of the algorithm is to introduce signal delays to avoid the reduction. A convergence condition in the algorithm was examined by using the IIR filter expression of the NLMS algorithm, and it was concluded that the IIR filter should be a low pass filter with unity gain. The condition is accomplished by selecting a small step gain.

This letter proposes a new routing strategy and a design of ATM switches. By partitioning internal links into subgroups based on the bandwidth of a connection request, an ATM switching network which is nonblocking in the wide sense at the connection level can be constructed without the need of internal-link speedup.

We developed programmable high-performance and high-speed neurocomputer for a large neural network using ASIC neurocomputing chips made by CMOS VLSI technology. The neurocomputer consists of one master node and multiple slave nodes which are connected by two data paths, a broadcast bus and a ring bus. The nodes are made by ASIC chips and each chip has plural nodes in it. The node has four types of computation hardware that can be cascaded in series forming a pipeline. Processing speed is proportional to the number of nodes. The neurocomputer is built on one printed circuit board having 65 VLSI chips that offers 1.5 billion connections/sec. The neurocomputer uses SIMD for easy programming and simple hardware. It can execute complicated computations, memory access and memory address control, and data paths control in a single instruction and in a single time step using the pipeline. The neurocomputer processes forward and backward calculations of multilayer perceptron type neural networks, LVQ, feedback type neural networks such as Hopfield model, and any other types by programming. To compute neural computation effectively and simply in a SIMD type neurocomputer, new processing methods are proposed for parallel computation such as delayed instruction execution, and reconfiguration.

The test pattern generation for sequential circuits is more difficult than that for combinational circuits due to the presence of memory elements. Therefore we proposed a method for synthesizing sequential circuits with testability in the level of state transition table. The state transition table is augmented by adding extra two inputs so that it possesses a distinguishing sequence, a synchronizing sequence, and transfer sequences of short length. In this case the checking sequence which do a complete verification of the circuit can be test pattern. The checking sequence have been impractical due to the longer checking sequence required. However, in this paper, we have discussed the condition to reduce the length of checking sequence, then by using suitable state assignment codes sequential circuits with much shorter checking sequences can be realized. A heuristic algorithm of the state assignment which reduce the length of checking sequence is proposed and the algorithm and reduced checking sequence are presented with simple example. The state assignment is very simple with the state matrix which represents the state transition. Furthermore some experimental results of automated synthesis for the MCNC Logic Synthesis Workshop finite state machine benchmark set have shown that the state assignment procedure is efficient for reducing checking sequences.

Digital neural networks are suitable for WSI implementation because their noise immunity is high, they have a fault tolerant structure, and the use of bus architecture can reduce the number of interconnections between neurons. To investigate the feasibility of WSIs, we integrated either 576 conventional neurons or 288 self-learning neurons on a 5-inch wafer, by using 0.8-µm CMOS technology and three metal layers. We also developed a new electron-beam direct-writing technology which enables easier fabrication of VLSI chips and wafer-level interconnections. We fabricated 288 self-learning neuron WSIs having as many as 230 good neurons.

Embossed disks with discrete magnetic tracks and servo marks are proposed and evaluated. The tracks and the servo marks are made by etching the glass substrate. The guard band depth was decided to be 0.2 µm. Using the disks, the head positioning accuracy of 0.09µm (rms) and the recording density of 192 tracks per millimeter were demonstrated.