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With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals.

For sustainability to be recorded in the Nigeria power sector (NPS), there must be a well-integrated system that is not easily prone to failure and is readily available when called into action. The NPS has overtime suffered from degraded infrastructure, policy paralysis to mention but few. However, if the needful is done with respect to identifying weak links in the network and a corresponding fast action in clearing failures along the line(s) then, some remarkable achievements could be recorded. This paper, therefore, carried out power flow analysis using the Newton Raphson Algorithm on the Electrical Transient Analyser Program (ETAP) version 12.6 on the NPS network using Maryland transmission station (MTS), Lagos, Nigeria as a case study. The choice of the location was as a result of the sensitivity of Lagos State in the economic activities of Nigeria. Results from the load flow indicated several voltage violations at load1 bus, load3 bus and load5 bus with magnitudes of 94.51, 94.91 and 94.79 % respectively. Consequently, transformers designated as T2A and T3A were said to have the highest and lowest branch losses of 150.0kW and 18.2kW respectively.  Compensation of the losses along the line was carried out using optimal capacitor placement (OCP) subjected to constraints on the ETAP environment. The results from the OCP showed that it optimally sized and placed four capacitor banks on four of the candidate buses, which include load1 bus, load2 bus, load3 bus and load5 bus. An improvement of 2.26%, 1.12%, 1.93%, 1.12% and 2.006% were recorded for load1 bus, load2 bus, load3 bus, load4 bus and load5 bus respectively.