In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.

Terrorism is on the rise in the Indian Sub-Continent and application of data mining techniques to analyse terrorist attacks is the need of the hour. The Security Forces (SF) in this region still rely on traditional and manual analysis methods. Data mining in this field is in its budding stage and if utilised efficiently will greatly facilitate the SF in preventing any terrorist attacks. SF are constantly searching for latest data mining techniques to augment terror analytics and improve protection of the local civilians and self thereby reducing collateral damage. Predicting terror attacks can push the potential of SF to the beat of terrorist activities. It is significant to recognise the spatial and temporal patterns for a better learning of terror incidents and to conceive their correlation. Clustering and Association rule mining (ARM) thus become strong contenders for efficient terror strikes forecasting. The above techniques can be used for a systematic profiling of outfits thus leading to the discovery of a unique pattern of operations i.e. Modus Operandi (MO) of a particular terror outfit. After gaining knowledge from data mining it is essential to convert it into actionable intelligence in order to be used by foot soldiers. Therefore, the paper provides concrete intelligence about various terror outfits operating in the most active Jammu and Kashmir (J&K) region of the Indian sub-continent.

The past few years has seen a tremendous amount of work being published in the area of continuous-time delta-sigma ADC designs with various compensation techniques to counter its susceptibility to non-idealities like clock jitter and excess loop delay, to name a few. The focus of this paper is the design of a tunable continuous time bandpass delta-sigma modulator that utilizes an excess loop delay compensation technique proposed to optimize the SNR of the modulator, besides preserving its stability by incorporating a full clock cycle delay. An improved, low noise, compact gyrator-C structure is proposed to obtain a high-Q bandpass filter subsequently used in the design of a second-order bandpass delta-sigma modulator clocked at 400 MHz for direct conversion of narrow band signals around 100 MHz. The proposed structure eliminates the need of a capacitor bank/array for the coarse tuning of the modulator since this structure enables coarse tuning in the range of 80 to 120 MHz and fine tuning of 5 MHz above or below the centre frequency. This modulator has been implemented in AMI 0.5μ CMOS process and achieves an SNR of 46 dB over a bandwidth of 1 MHz, calculated from post-layout simulations. The power consumption of this design is 49 mW at a supply of 3V.