Multi-dimensional arrays

A subset of the MIMOLA language, a Pascal-like language that includes recursive procedure calls and multi-dimensional arrays. 

Logic synthesis of otie dimensional and multi-dimensional arrays in VHDL or Verilog results in x times y xQgvsX vs for an array with x and y dimensions. Further, to write to an element of the array requires decoding logic and to read an element from the array... 

Fig. 2.1. Outline of the hybrid algorithm. The unstructured array of sensors is clustered using multi-dimensional scaling (MDS) with a mutual information (MI) based distance measure. Then Vector Quantization (VQ) is used to partition the sensor into correlated groups. Each such group provides input to one module of an associative memory layer. VQ is used again to provide each module unit with a specific receptive field, i.e. to become a feature detector. Finally, classification is done by means of BCPNN.

Prediction of tea quality is not an easy job due to the presence of innumerable compounds and their multi-dimensional contribution in determining the quality of tea. In practice, tea samples are tested by human sensory panel called Tea Tasters, who assign quality scores in the scale of 1-10 according to the quality of tea. This method is highly subjective, and the scores vary from taster to taster [1]. The prediction of black tea quality by experimental means, co-relation of sensor array response with Tea Tasters scores have been established by the author in [13] with good accuracy. 

Array of sensors used in EN systems generate a multi-dimensional re xmse pattern (n-dimensional space), where n is the number of sensors in the array. When n is more than 3, human perception of the struaural relationships is difficult unlike in 2-and 3-dimensions. A numb ci a iroacii are therefore necessary to map such a... 

For the experiment array, I prefer an orthogonal central-composite design (2), (3), which consists of three main parts, as shown in Table I. The first is a conventional 16-experiment fractional factorial design for five variables at two levels. The second comprises three identical experiments at the average, or center-point, conditions for the first 16 experiments. The final part comprises two out-lier experiments for each variable. These augment the basic two level design to provide an estimate of curvature for the response to each variable. The overall effect of the design is to saturate effectively the multi-dimensional variable space. It is more effective than the conventional "one-variable-at-a-time" approa.ch. 

As shown in Fig. 22, the resulting procedure, referred to as a multi-block experiment, produces a two-dimensional data set, such as an array of FIDs (its exact nature depends upon the signal acquisition method). The data of each x-block are then reduced to a single quantity, S(t) which should be proportional either to the total sample magnetization Ma(x) or to one of its components. Since the vertical scale of the relaxation curve is irrelevant, we can identify S(t) with Ma(x) at the exact time of detection (usually just after the first excitation pulse). 

---