In the absence of noise, if C is channel capacity in bits/s, δf is channel bandwidth in Hz and N is number of coding levels. Then ?->(Show Answer!)

1. In the absence of noise, if C is channel capacity in bits/s, δf is channel bandwidth in Hz and N is number of coding levels. Then

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MCQ->In the absence of noise, if C is channel capacity in bits/s, δf is channel bandwidth in Hz and N is number of coding levels. Then....

MCQ-> Read the passage carefully and answer the questions given at the end of each passage:Turning the business involved more than segmenting and pulling out of retail. It also meant maximizing every strength we had in order to boost our profit margins. In re-examining the direct model, we realized that inventory management was not just core strength; it could be an incredible opportunity for us, and one that had not yet been discovered by any of our competitors. In Version 1.0 the direct model, we eliminated the reseller, thereby eliminating the mark-up and the cost of maintaining a store. In Version 1.1, we went one step further to reduce inventory inefficiencies. Traditionally, a long chain of partners was involved in getting a product to the customer. Let’s say you have a factory building a PC we’ll call model #4000. The system is then sent to the distributor, which sends it to the warehouse, which sends it to the dealer, who eventually pushes it on to the consumer by advertising, “I’ve got model #4000. Come and buy it.” If the consumer says, “But I want model #8000,” the dealer replies, “Sorry, I only have model #4000.” Meanwhile, the factory keeps building model #4000s and pushing the inventory into the channel. The result is a glut of model #4000s that nobody wants. Inevitably, someone ends up with too much inventory, and you see big price corrections. The retailer can’t sell it at the suggested retail price, so the manufacturer loses money on price protection (a practice common in our industry of compensating dealers for reductions in suggested selling price). Companies with long, multi-step distribution systems will often fill their distribution channels with products in an attempt to clear out older targets. This dangerous and inefficient practice is called “channel stuffing”. Worst of all, the customer ends up paying for it by purchasing systems that are already out of date Because we were building directly to fill our customers’ orders, we didn’t have finished goods inventory devaluing on a daily basis. Because we aligned our suppliers to deliver components as we used them, we were able to minimize raw material inventory. Reductions in component costs could be passed on to our customers quickly, which made them happier and improved our competitive advantage. It also allowed us to deliver the latest technology to our customers faster than our competitors. The direct model turns conventional manufacturing inside out. Conventional manufacturing, because your plant can’t keep going. But if you don’t know what you need to build because of dramatic changes in demand, you run the risk of ending up with terrific amounts of excess and obsolete inventory. That is not the goal. The concept behind the direct model has nothing to do with stockpiling and everything to do with information. The quality of your information is inversely proportional to the amount of assets required, in this case excess inventory. With less information about customer needs, you need massive amounts of inventory. So, if you have great information – that is, you know exactly what people want and how much - you need that much less inventory. Less inventory, of course, corresponds to less inventory depreciation. In the computer industry, component prices are always falling as suppliers introduce faster chips, bigger disk drives and modems with ever-greater bandwidth. Let’s say that Dell has six days of inventory. Compare that to an indirect competitor who has twenty-five days of inventory with another thirty in their distribution channel. That’s a difference of forty-nine days, and in forty-nine days, the cost of materials will decline about 6 percent. Then there’s the threat of getting stuck with obsolete inventory if you’re caught in a transition to a next- generation product, as we were with those memory chip in 1989. As the product approaches the end of its life, the manufacturer has to worry about whether it has too much in the channel and whether a competitor will dump products, destroying profit margins for everyone. This is a perpetual problem in the computer industry, but with the direct model, we have virtually eliminated it. We know when our customers are ready to move on technologically, and we can get out of the market before its most precarious time. We don’t have to subsidize our losses by charging higher prices for other products. And ultimately, our customer wins. Optimal inventory management really starts with the design process. You want to design the product so that the entire product supply chain, as well as the manufacturing process, is oriented not just for speed but for what we call velocity. Speed means being fast in the first place. Velocity means squeezing time out of every step in the process. Inventory velocity has become a passion for us. To achieve maximum velocity, you have to design your products in a way that covers the largest part of the market with the fewest number of parts. For example, you don’t need nine different disk drives when you can serve 98 percent of the market with only four. We also learned to take into account the variability of the lost cost and high cost components. Systems were reconfigured to allow for a greater variety of low-cost parts and a limited variety of expensive parts. The goal was to decrease the number of components to manage, which increased the velocity, which decreased the risk of inventory depreciation, which increased the overall health of our business system. We were also able to reduce inventory well below the levels anyone thought possible by constantly challenging and surprising ourselves with the result. We had our internal skeptics when we first started pushing for ever-lower levels of inventory. I remember the head of our procurement group telling me that this was like “flying low to the ground 300 knots.” He was worried that we wouldn’t see the trees.In 1993, we had $2.9 billion in sales and $220 million in inventory. Four years later, we posted $12.3 billion in sales and had inventory of $33 million. We’re now down to six days of inventory and we’re starting to measure it in hours instead of days. Once you reduce your inventory while maintaining your growth rate, a significant amount of risk comes from the transition from one generation of product to the next. Without traditional stockpiles of inventory, it is critical to precisely time the discontinuance of the older product line with the ramp-up in customer demand for the newer one. Since we were introducing new products all the time, it became imperative to avoid the huge drag effect from mistakes made during transitions. E&O; – short for “excess and obsolete” - became taboo at Dell. We would debate about whether our E&O; was 30 or 50 cent per PC. Since anything less than $20 per PC is not bad, when you’re down in the cents range, you’re approaching stellar performance.Find out the TRUE statement:
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MCQ->If h1[n] = 3δ[n] + δ[n - 1], h2[n] = 2δ[n] + δ[n - 2]h3[n] = δ[n] - 3δ[n - 1] + 7δ[n - 4] + 6δ[n - 6]h[n] = h1[n] h2[n] + h3[n] h1[n] has value....

MCQ-> Read the following passage carefully and answer the questions given. Certain words/phrases have been given in bold to help you locate them while answering some of the questions. From a technical and economic perspective, many assessments have highlighted the presence of cost-effective opportunities to reduce energy use in buildings. However several bodies note the significance of multiple barriers that prevent the take-up of energy efficiency measures in buildings. These include lack of awareness and concern, limited access to reliable information from trusted sources, fear about risk, disruption and other ‘transaction costs’ concerns about up-front costs and inadequate access to suitably priced finance, a lack of confidence in suppliers and technologies and the presence of split incentives between landlords and tenants. The widespread presence of these barriers led experts to predict thatwithout a concerted push from policy, two-thirds of the economically viable potential to improve energy efficiency will remain unexploited by 2035. These barriers are albatross around the neck that represent a classic market failure and a basis for governmental intervention. While these measurements focus on the technical, financial or economic barriers preventing the take-up of energy efficiency options in buildings, others emphasise the significance of the often deeply embedded social practices that shape energy use in buildings. These analyses focus not on the preferences and rationalities that might shape individual behaviours, but on the ‘entangled’ cultural practices, norms, values and routines that underpin domestic energy use. Focusing on the practice-related aspects of consumption generates very different conceptual framings and policy prescriptions than those that emerge from more traditional or mainstream perspectives. But the underlying case for government intervention to help to promote retrofit and the diffusion of more energy efficient particles is still apparent, even though the forms of intervention advocated are often very different to those that emerge from a more technical or economic perspective. Based on the recognition of the multiple barriers to change and the social, economic and environmental benefits that could be realised if they were overcome, government support for retrofit (renovating existing infrastructure to make it more energy efficient) has been widespread. Retrofit programmes have been supported and adopted in diverse forms in many setting and their ability to recruit householders and then to impact their energy use has been discussed quite extensively. Frequently, these discussions have criticised the extent to which retrofit schemes rely on incentives and the provision of new technologies to change behaviour whilst ignoring the many other factors that might limit either participation in the schemes or their impact on the behaviours and prac-tices that shape domestic energy use. These factors are obviously central to the success of retrofit schemes, but evaluations of different schemes have found that despite these they can still have significant impacts. Few experts that the best estimate of the gap between the technical potential and the actual in-situ performance of energy efficiency measures is 50%, with 35% coming from performance gaps and 15% coming from ‘comfort taking’ or direct rebound effects. They further suggest that the direct rebound effect of energy efficiency measures related to household heating is Ilkley to be less than 30% while rebound effects for various domestic energy efficiency measures vary from 5 to 15% and arise mostly from indirect effects (i.e., where savings from energy efficiency lead to increased demand for goods and services). Other analyses also note that the gap between technical potential and actual performance is likely to vary by measure, with the range extending from 0% for measures such as solar water heating to 50% for measures such as improved heating controls. And others note that levels of comfort taking are likely to vary according to the levels of consumption and fuel poverty in the sample of homes where insulation is installed, with the range extending from 30% when considering homes across all income groups to around 60% when considering only lower income homes. The scale of these gapsis significant because it materially affects the impacts of retrofit schemes and expectations and perceptions of these impacts go on to influence levels of political, financial and public support for these schemes. The literature on retrofit highlights the presence of multiple barriers to change and the need for government support, if these are to be overcome. Although much has been written on the extent to which different forms of support enable the wider take-up of domestic energy efficiency measures, behaviours and practices, various areas of contestation remain and there is still an absence of robust ex-post evidence on the extent to which these schemes actually do lead to the social, economic and environmental benefits that are widely claimed.Which of the following is most nearly the OPPOSITE in meaning to the word ‘CONCERTED’ as used in the passage ?
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MCQ-> Read the following passage carefully and answer the questions given below it. Certain words/phrases have been printed in bold tohelp you locate them while answering some of the questions. During the last few years, a lot of hype has been heaped on the BRICS (Brazil, Russia, India, China, and South Africa). With their large populations and rapid growth, these countries, so the argument goes, will soon become some of the largest economies in the world and, in the case of China, the largest of all by as early as 2020. But the BRICS, as well as many other emerging-market economieshave recently experienced a sharp economic slowdown. So, is the honeymoon over? Brazil’s GDP grew by only 1% last year, and may not grow by more than 2% this year, with its potential growth barely above 3%. Russia’s economy may grow by barely 2% this year, with potential growth also at around 3%, despite oil prices being around $100 a barrel. India had a couple of years of strong growth recently (11.2% in 2010 and 7.7% in 2011) but slowed to 4% in 2012. China’s economy grew by 10% a year for the last three decades, but slowed to 7.8% last year and risks a hard landing. And South Africa grew by only 2.5% last year and may not grow faster than 2% this year. Many other previously fast-growing emerging-market economies – for example, Turkey, Argentina, Poland, Hungary, and many in Central and Eastern Europe are experiencing a similar slowdown. So, what is ailing the BRICS and other emerging markets? First, most emerging-market economies were overheating in 2010-2011, with growth above potential and inflation rising and exceeding targets. Many of them thus tightened monetary policy in 2011, with consequences for growth in 2012 that have carried over into this year. Second, the idea that emerging-market economies could fully decouple from economic weakness in advanced economies was farfetched : recession in the eurozone, near-recession in the United Kingdom and Japan in 2011-2012, and slow economic growth in the United States were always likely to affect emerging market performance negatively – via trade, financial links, and investor confidence. For example, the ongoing euro zone downturn has hurt Turkey and emergingmarket economies in Central and Eastern Europe, owing to trade links. Third, most BRICS and a few other emerging markets have moved toward a variant of state capitalism. This implies a slowdown in reforms that increase the private sector’s productivity and economic share, together with a greater economic role for state-owned enterprises (and for state-owned banks in the allocation of credit and savings), as well as resource nationalism, trade protectionism, import substitution industrialization policies, and imposition of capital controls. This approach may have worked at earlier stages of development and when the global financial crisis caused private spending to fall; but it is now distorting economic activity and depressing potential growth. Indeed, China’s slowdown reflects an economic model that is, as former Premier Wen Jiabao put it, “unstable, unbalanced, uncoordinated, and unsustainable,” and that now is adversely affecting growth in emerging Asia and in commodity-exporting emerging markets from Asia to Latin America and Africa. The risk that China will experience a hard landing in the next two years may further hurt many emerging economies. Fourth, the commodity super-cycle that helped Brazil, Russia, South Africa, and many other commodity-exporting emerging markets may be over. Indeed, a boom would be difficult to sustain, given China’s slowdown, higher investment in energysaving technologies, less emphasis on capital-and resource-oriented growth models around the world, and the delayed increase in supply that high prices induced. The fifth, and most recent, factor is the US Federal Reserve’s signals that it might end its policy of quantitative easing earlier than expected, and its hints of an even tual exit from zero interest rates. both of which have caused turbulence in emerging economies’ financial markets. Even before the Fed’s signals, emergingmarket equities and commodities had underperformed this year, owing to China’s slowdown. Since then, emerging-market currencies and fixed-income securities (government and corporate bonds) have taken a hit. The era of cheap or zerointerest money that led to a wall of liquidity chasing high yields and assets equities, bonds, currencies, and commodities – in emerging markets is drawing to a close. Finally, while many emerging-market economies tend to run current-account surpluses, a growing number of them – including Turkey, South Africa, Brazil, and India – are running deficits. And these deficits are now being financed in riskier ways: more debt than equity; more short-term debt than longterm debt; more foreign-currency debt than local-currency debt; and more financing from fickle cross-border interbank flows. These countries share other weaknesses as well: excessive fiscal deficits, abovetarget inflation, and stability risk (reflected not only in the recent political turmoil in Brazil and Turkey, but also in South Africa’s labour strife and India’s political and electoral uncertainties). The need to finance the external deficit and to avoid excessive depreciation (and even higher inflation) calls for raising policy rates or keeping them on hold at high levels. But monetary tightening would weaken already-slow growth. Thus, emerging economies with large twin deficits and other macroeconomic fragilities may experience further downward pressure on their financial markets and growth rates. These factors explain why growth in most BRICS and many other emerging markets has slowed sharply. Some factors are cyclical, but others – state capitalism, the risk of a hard landing in China, the end of the commodity supercycle -are more structural. Thus, many emerging markets’ growth rates in the next decade may be lower than in the last – as may the outsize returns that investors realised from these economies’ financial assets (currencies, equities. bonds, and commodities). Of course, some of the better-managed emerging-market economies will continue to experitnce rapid growth and asset outperformance. But many of the BRICS, along with some other emerging economies, may hit a thick wall, with growth and financial markets taking a serious beating.Which of the following statement(s) is/are true as per the given information in the passage ? A. Brazil’s GDP grew by only 1% last year, and is expected to grow by approximately 2% this year. B. China’s economy grew by 10% a year for the last three decades but slowed to 7.8% last year. C. BRICS is a group of nations — Barzil, Russia, India China and South Africa.....