We omit Gompertz’ method of fitting, which the reader will find in Makeham, below. Also omitted are Gompertz’ mortality tables, and his discussion of life expectancy and annuities under his Law. The hyperbolic logarithms in Art. 5 are the natural logs; we are not able to follow completely his integration, which is by Newton's method of fluxions.

On the Nature of the Function Expressive of the Law of Human Mortality

Since the 1990s, the per capita GDP in Tanzania has been increasing and Tanzania’s growth trend has been impressive. The annual GDP growth has averaged 6.4 percent between 2000 and 2004 and exceeded seven percent in 2002 and 2003 (Figure 2, Real GDP Growth). Tanzania’s growth rate of 6.3 percent in 2004 was well above the rate achieved in South Africa (3.7 percent). This strong growth performance reflects the fruits of responsible monetary and fiscal policy, concerted reforms, rapid export growth, and significant debt relief.

The united republic of tanzania

Energy management using non-intrusive load monitoring techniques - State-of-the-art and future research directions

In this paper, a Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a process aiming to identify the individual contribution of appliances in the aggregate electricity load. The proposed model, Bayes-BiLSTM, is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase. In addition, a non-causal model is introduced in order to tackle with inherent structure, characterizing the operation of multi-state appliances. Furthermore, a Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, thus increasing performance. Experimental results indicate the proposed method’s superiority, compared to the current state-of-the-art.

Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation is introduced. The proposed model, CoBiLSTM, harnesses the representational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for: non-causality and adaptivity to contextual factors (e.g., seasonality). A Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Furthermore, the proposed model is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method’s superiority compared to the current state of the art.

Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models

Research Article published by International Journal of Computer Applications Volume 169 – No.11, July 2017

/pdf/design-and-analysis-of-smart-sensing-system-for-animal-2xc2huaq7s.pdf

Design and Analysis of Smart Sensing System for Animal Emotions Recognition

Research Article published by International Journal of Computer Applications Volume 101– No.8, September 2014

/pdf/a-grey-level-fitting-mechanism-based-on-gompertz-function-oq8qpli6yb.pdf

A Grey Level Fitting Mechanism based on Gompertz Function for Two Phase Flow Imaging using Electrical Capacitance Tomography Measurement Systems

The growing Tanzanian population currently estimated to be 48 Million people and their use of vehicles as means of transport has kept increasing making enforcing traffic rules and regulations among road users a major challenge. This calls for a need to have an automated system that monitors the motorists with a pre-defined sense of intelligence. A Vehicle Detection and Recognition Algorithm which can provide automated access to relevant information to a number plate from information systems containing and managing databases on vehicle and their movements is required. This paper presents work on developed algorithm that localizes plate area, extract and segment character, and finally recognizes and interprets registration number from vehicle image. MATLAB R2012b Simulation software with Image Processing toolbox is employed. HSV color space image, morphological and statistical analysis operations were integrated and employed to a vehicle image to compute plate number area. In segmentation the properties like aspect ratio, extent, and area ratio were important measurement parameters. Finally, the template matching database and statistical character extracted from car image was correlated to recognize alphanumeric character to deduce car registration number. Keywords — Character extraction, Detection algorithm, Recognition algorithm, Morphological matching, Template matching

/pdf/vehicle-plate-number-detection-and-recognition-using-y5iwxnvx44.pdf

Vehicle Plate Number Detection and Recognition Using Improved Algorithm

In this paper, we review image reconstruction methods and their suitability in electrical capacitance tomography measurement system. These methods can be grouped into direct and iterative methods. Direct methods include Linear back projection, Singular value decomposition, and Tikhonov regularization. Iterative methods are further divided into algebraic and optimization methods. Algebraic reconstruction methods include iterative linear back projection, iterative Tikhonov, Landweber iteration, simultaneously algebraic reconstruction, and model$ - $based reconstruction. Optimization methods include fuzzy mathematical modeling, genetic algorithms, artificial neural networks, generalized vector sampled pattern matching, total variation regularization, regularized total least squares, extended Tikhonov regularization, simulated annealing, compressed sensing principle, population entropy, adaptive differential evolution, least$ - $squares support vector machine, and self-adaptive particle swarm optimization. Some of these methods have been examined through experiments and their comparative analysis have been given. Results show that iterative methods generate high quality images compared with non-iterative ones when evaluated over full component fraction range. However, iterative methods are computationally expensive, and hence used for research and off-line investigations rather than for on-line process monitoring.

/pdf/a-review-of-image-reconstruction-methods-in-electrical-1zl29wechs.pdf

A review of image reconstruction methods in electrical capacitance tomography

study, we present a measurement-based model for path loss prediction in three GSM service areas at 900 MHz .Modified Hata model for rural, suburban, and urban environments were derived in this study on the basis of experimental path loss measurements with the use of least square method. The models developed predicted with reasonable accuracy the path loss of radio networks investigated with a root mean square error of < 6 dB .The developed model can be useful in network planning and optimization for the environments taken as case study for the investigation, as well as any macro- and microcell environment, which is similar to the environment considered in this research.

/pdf/optimization-of-hata-model-based-on-measurements-data-using-ilsn0o83ti.pdf

Michael Kisangiri

Papers

Design and Analysis of Smart Sensing System for Animal Emotions Recognition

A Grey Level Fitting Mechanism based on Gompertz Function for Two Phase Flow Imaging using Electrical Capacitance Tomography Measurement Systems

Vehicle Plate Number Detection and Recognition Using Improved Algorithm

A review of image reconstruction methods in electrical capacitance tomography

Optimization of Hata Model based on Measurements Data using Least Square Method: A Case Study in Dar-es-Salaam – Tanzania