یہ کتاب 1982ء میں شائع ہوئی جو اس وقت کی ملیالم کہانیوں کا اردو ترجمہ ہے۔ اس کتاب میں دس ملیالم کہانیاں ترجمہ کی گئی ہیں۔
Fish Aggregating Devices (FAD's) have long been used by fishermen in South Buton Regency. This study aims to determine the utilization of FAD distribution as a fishing aid by purse sein vessels in the waters of Kadatua District, South Buton Regency. This study aims to determine the utilization of FAD distribution as a fishing tool by purse sein vessels in Kadatua District, South Buton Regency. Data collection methods include FAD point data taken from GPS (Global Position System) fishermen and dominant catch data from purse sein fishermen. The results of the study obtained data on the distribution of FADs in Kadatua District spread between a distance of 0-12 miles, during the study the coordinates of FADs were obtained as many as 79 coordinate points owned by fishermen in Kadatua District. FADs used by purse sein fishermen to carry out fishing operations have 24 FAD points, which are spread over a distance of 0 – 12 miles. In April the distribution of FADs used as fishing aids by purse sein vessels was at a distance of ± 0 – 12 miles, while in May and June FADs used as fishing aids were at a distance of ± 2 – 12 miles. The dominant fish catch data for purse sein fishermen is 690 kg in April, 525 kg in May and 735 kg in June. The dominant catch during the study was dominated by scad fish (Decapterus spp). The level of effectiveness of the dominant catch of purse sein fishermen is highest at a distance of 4-12 miles with a total catch of 1,070 kg of the total catch of 1,950 kg.
The thesis is divided in three parts. In the rst part, it explores and discusses the diversity of concepts and motivations for obtaining good resolution and highly concentrated time–frequency distributions (TFDs) for the research community. The description of the methods used for TFDs'' objective assessment is provided later in this part. In the second part, a novel multi–processes ANN based framework to obtain highly concentrated TFDs is proposed. The propose method utilizes a localised Bayesian regularised neural network model (BRNNM) to obtain the energy concentration along the instantaneous frequencies (IFs) of individual components in the multicomponent signals without assuming any prior knowledge. The spectrogram and pre–processed Wigner–Ville distribution (WD) of the signals with known IF laws are used as the train- ing set for the BRNNM. These distributions, taken as two–dimensional (2–D) image matrices, are vectorized and clustered according to the elbow criterion. Each cluster contains the pairs of the input and target vectors from the spectrograms and highly concentrated pre–processed WD respectively. For each cluster, the pairs of vectors are used to train the multiple ANNs under the Bayesian framework of David Mackay. The best trained network for each cluster is selected based on network error criterion. In the test phase, the test TFDs of unknown signals, after vectorization and clustering, are processed through these specialized ANNs. After post–processing, the resultingTFDs are found to exhibit improved resolution and concentration along the individual components then the initial blurred estimates. The third part presents the discussion on the experimental results obtained by the proposed technique. Moreover the framework is extended to include the various objec- tive methods of assessment to evaluate the performance of de–blurred TFDs obtained through the proposed technique. The selected methods not only allow quantifying the quality of TFDs instead of relying solely on visual inspection of their plots, but also help in drawing comparison of the proposed technique with the other existing tech- niques found in literature for the purpose. In particular the computation regularities show the effectiveness of the objective criteria in quantifying the TFDs'' concentration and resolution information.